A grinding and polishing control method and control system based on sliding mode control

Abstract

A grinding and polishing control method and control system based on sliding mode control, relating to the field of robot industrial control, includes: collecting the force signal of the force sensor at the end of the robot arm and decomposing it into a normal force component perpendicular to the workpiece surface; determining the deviation value between the normal force component and the preset target pressure, and constructing a sliding surface expression for sliding mode control; determining the force control component according to the force control gain coefficient; determining the position deviation and speed deviation according to the position signal of each joint encoder and the preset grinding trajectory; determining the position control component according to the position deviation, speed deviation, position gain coefficient and speed gain coefficient; constructing a selection matrix, and performing orthogonal decomposition and superposition on the force control component and the position control component to obtain a comprehensive control instruction; converting the result into a drive signal for each joint motor, and transmitting the drive signal to the robot arm for grinding and polishing. Implementation of this application can optimize the force control of robot grinding and polishing and reduce response delay.

Description

Technical Field

[0001] The present application relates to the field of robot industrial control, and in particular to a grinding and polishing control method and control system based on sliding mode control. Background Art

[0002] With the continuous improvement of industrial automation, the application of robots in grinding and polishing is becoming increasingly widespread. Robotic arm grinding and polishing technology can be used for surface treatment of hardware parts, engine covers, leather products, and other products, playing a significant role in improving product quality and production efficiency. During the grinding and polishing process, the end of the robotic arm must maintain stable contact pressure with the workpiece surface while precisely moving along the workpiece surface trajectory, which places high demands on the robotic arm's force and position control.

[0003] Impedance control is the mainstream control method for robotic arm grinding and polishing. This method establishes an impedance model between force and position, leveraging the robotic arm's three-loop control architecture to achieve a closed position loop. Specifically, impedance parameters such as the damping coefficient and stiffness coefficient are adjusted to ensure the appropriate pressure is applied to the end of the robotic arm when it contacts the workpiece surface. Simultaneously, feedback from the robotic arm's position loop adjusts the motion trajectory, achieving smooth motion control during the grinding and polishing process.

[0004] However, as an indirect force control method, impedance control has a significant hysteresis characteristic in its force control response. When the end of the robotic arm moves along the surface of the curved workpiece, the force response delay causes a large deviation between the actual pressure and the set pressure. This pressure fluctuation can affect the processing quality and surface uniformity of grinding and polishing. Summary of the Invention

[0005] The present application provides a grinding and polishing control method and control system based on sliding mode control, which are used to optimize the force control of robot grinding and polishing and reduce response delay.

[0006] In the first aspect, the present application provides a grinding and polishing control method based on sliding mode control, which is applied to a control system, and the method includes: collecting the force signal of the force sensor at the end of the robotic arm, and decomposing the force signal into a normal force component perpendicular to the workpiece surface; determining the deviation value between the normal force component and the preset target pressure, and constructing a sliding surface expression for sliding mode control based on the deviation value; determining the force control component according to the sliding surface expression and the force control gain coefficient; determining the position deviation and speed deviation according to the collected position signals of each joint encoder and the preset grinding trajectory; determining the position control component according to the position deviation, speed deviation, position gain coefficient and speed gain coefficient; constructing a selection matrix based on the normal direction and tangential direction of the workpiece surface, and orthogonally decomposing and superimposing the force control component and the position control component based on the selection matrix to obtain a comprehensive control instruction; converting the comprehensive control instruction into a drive signal for each joint motor, and transmitting the drive signal to the robotic arm to perform grinding and polishing based on sliding mode control.

[0007] In the above embodiment, the control system implements force / position hybrid control based on sliding mode control, collects force signals and decomposes the normal force components, performs force control in combination with the sliding surface expression, and realizes precise trajectory tracking through position and velocity feedback; when grinding the surface of a flat workpiece, compared with impedance control, although the initial downward pressure over-thrust force is slightly larger, the pressure stability is better, and the fluctuation range is only within ±1N; when grinding the surface of a curved workpiece, the pressure tracking accuracy is significantly better than that of impedance control, and the fluctuation range is controlled within ±2N, which effectively overcomes the problem of response lag of impedance control.

[0008] In combination with some embodiments of the first aspect, in some embodiments, the steps of determining the deviation value between the normal force component and the preset target pressure, and constructing the sliding surface expression of the sliding mode control based on the deviation value, specifically include: calculating the deviation value between the normal force component and the preset target pressure and the deviation integral term within a preset time window; and performing a weighted combination of the deviation value, the deviation integral term and the sliding mode coefficient to obtain the sliding surface expression.

[0009] In the above embodiment, the control system calculates the force deviation and the integral term, and performs weighted combination to construct the sliding surface expression, thereby achieving rapid response adjustment of the grinding pressure, making full use of the characteristics of the sliding mode control, and improving the control accuracy while ensuring the stability of the system. The pressure fluctuation during curved surface grinding is significantly smaller than that of the impedance control method, effectively improving the grinding quality.

[0010] In combination with some embodiments of the first aspect, in some embodiments, before the step of weighted combination of the deviation value, the deviation integral term and the sliding mode coefficient to obtain the sliding surface expression, the method also includes: constructing a grinding dynamic model including the surface curvature of the workpiece and the posture of the grinding tool; determining the Lyapunov function based on the grinding dynamic model; solving the derivative expression of the Lyapunov function, determining the adaptive law parameter range, and determining the sliding mode coefficient based on the adaptive law parameter range.

[0011] In the above embodiment, the control system determines the optimal sliding mode coefficient based on the grinding dynamic model and Lyapunov function, realizes the adaptive optimization of system parameters, ensures the convergence and robustness of the control system through theoretical analysis, and shows good stability in practical applications, and can maintain a good pressure tracking effect even when grinding curved workpieces.

[0012] In combination with some embodiments of the first aspect, in some embodiments, after converting the comprehensive control instructions into drive signals of each joint motor and transmitting the drive signals to the robotic arm to perform grinding and polishing based on sliding mode control, the method also includes: collecting surface quality data of the workpiece surface during the grinding process; determining the key grinding area of the workpiece surface based on the surface quality data and a preset grinding judgment threshold; and determining the posture angle, local residence time and grinding speed of the grinding tool based on the key grinding area.

[0013] In the above embodiment, the control system monitors the surface quality in real time and dynamically adjusts the grinding parameters, realizing intelligent optimization of the machining process. It adaptively adjusts the tool posture, dwell time and grinding speed according to the actual machining effect, ensuring the consistency of machining quality in different areas and improving grinding efficiency.

[0014] In combination with some embodiments of the first aspect, in some embodiments, the steps of determining the posture angle, local residence time and grinding speed of the grinding tool according to the key grinding area specifically include: constructing a dynamic model of the grinding process including the material removal rate according to the key grinding area; calculating the local residence time of each local interval based on the dynamic model; calculating the trajectory curvature of each local interval in combination with the surface features of the workpiece, and determining the posture angle and speed constraints of the grinding tool according to the trajectory curvature; solving the speed planning scheme that meets the speed constraints to obtain the grinding speed.

[0015] In the above embodiment, the control system establishes a material removal rate model, optimizes grinding parameters based on the workpiece surface features, achieves precise speed and posture planning, fully considers the workpiece geometric characteristics, and ensures the uniformity and stability of machining quality.

[0016] In combination with some embodiments of the first aspect, in some embodiments, before the step of collecting the force signal of the force sensor at the end of the robotic arm and decomposing the force signal into a normal force component perpendicular to the surface of the workpiece, the method also includes: controlling the robotic arm to contact the workpiece surface at a preset speed for a preset movement, and collecting the force-controlled displacement characteristic curve during the contact process; determining the stiffness coefficient of the workpiece surface based on the force-controlled displacement characteristic curve; determining the material damping coefficient of the workpiece surface based on the contact transient response characteristics, and generating a material property model based on the material damping coefficient and stiffness coefficient; and determining the force control gain coefficient and the preset target pressure based on the material property model.

[0017] In the above embodiment, the control system pre-identifies the surface characteristics of the workpiece and establishes a material model, thereby achieving optimal configuration of control parameters. Appropriate control parameters are determined through force-controlled displacement characteristic analysis, thereby improving the system's adaptability to workpieces of different materials.

[0018] In combination with some embodiments of the first aspect, in some embodiments, the step of determining the force control gain coefficient and the preset target pressure based on the material property model specifically includes: constructing a grinding database including material properties and grinding parameters based on the material property model; collecting real-time operation data including force data and position data during the grinding process, and determining the grinding quality index based on the real-time operation data; when the grinding quality index is lower than the preset qualified index, extracting the optimal grinding parameters of similar workpiece materials from the grinding database; and determining the force control gain coefficient and the preset target pressure based on the optimal grinding parameters.

[0019] In the above embodiment, the control system constructs a polishing database and optimizes control parameters in real time, thereby achieving dynamic optimization of the machining process and ensuring timely optimization of the control strategy based on historical data and real-time feedback.

[0020] In a second aspect, an embodiment of the present application provides a control system comprising: one or more processors and a memory; the memory is coupled to the one or more processors, the memory being used to store computer program code, the computer program code comprising computer instructions, the one or more processors calling the computer instructions to enable the control system to execute the method described in the first aspect and any possible implementation of the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product comprising instructions, which, when the computer program product is run on a control system, enables the control system to execute the method described in the first aspect and any possible implementation of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium comprising instructions, which, when executed on a control system, causes the control system to execute the method described in the first aspect and any possible implementation of the first aspect.

[0023] It is understandable that the control system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the methods provided in the embodiments of the present application. Therefore, the beneficial effects that can be achieved can be referenced to the beneficial effects of the corresponding methods and will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0025] 1. Due to the adoption of a force / position hybrid control strategy based on sliding mode control, combined with technical means such as force sensor signal decomposition, sliding surface construction and orthogonal decomposition, it is possible to achieve effective coordination between force control and position control, effectively solving the problems of impedance control response lag and large pressure fluctuations in the existing technology, thereby achieving high-precision and high-stability grinding and polishing process control.

[0026] 2. Due to the adoption of an adaptive control solution based on real-time surface quality monitoring, by collecting surface quality data, identifying key grinding areas, and optimizing parameters such as tool posture, dwell time, and grinding speed accordingly, the grinding strategy can be dynamically adjusted according to the actual processing effect, effectively solving the problems of poor grinding uniformity and low efficiency in existing technologies, and thus realizing differentiated grinding control.

[0027] 3. Due to the adoption of a parameter optimization scheme based on material property identification, by conducting force-controlled displacement characteristic tests in advance, determining the stiffness coefficient and damping coefficient, and establishing a material property model, the force control gain and target pressure can be accurately set, effectively solving the problem of strong experience-based dependency and poor adaptability of parameter settings in existing technologies, and thus realizing adaptive control of different workpiece materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 (a) is a control block diagram based on impedance control during the grinding and polishing process of the robotic arm in an embodiment of the present application;

[0029] Figure 1 (b) is a control block diagram of a conventional force / position hybrid control during the grinding and polishing process of a robotic arm in an embodiment of the present application;

[0030] Figure 1 (c) is a control block diagram of the improved force / position hybrid control based on sliding mode control during the grinding and polishing process of the robotic arm in an embodiment of the present application;

[0031] Figure 2 (a) is a schematic diagram of a scene in which a robotic arm grinds and polishes a flat workpiece surface in an embodiment of the present application;

[0032] Figure 2 (b) is a schematic diagram of a scene in which a robotic arm grinds and polishes a curved workpiece in an embodiment of the present application;

[0033] Figure 3 (a) is a schematic diagram of the control curves of position and force in various directions of a robotic arm for grinding and polishing a flat workpiece surface using a force / position hybrid control method based on impedance control in an embodiment of the present application;

[0034] Figure 3 (b) is a schematic diagram of the control curves of position and force in various directions of the robot arm grinding and polishing on a flat workpiece surface based on force / position hybrid control in an embodiment of the present application;

[0035] Figure 3 (c) is a schematic diagram of the control curves of position and force in various directions of the robot arm for grinding and polishing on a flat workpiece surface based on force / position hybrid control of sliding mode control in an embodiment of the present application;

[0036] Figure 4 (a) is a schematic diagram of the control curves of position and force in various directions of polishing of a curved workpiece surface by a robotic arm based on impedance control in an embodiment of the present application;

[0037] Figure 4 (b) is a schematic diagram of the control curves of position and force in various directions of the robot arm grinding and polishing on the surface of a curved workpiece based on force / position hybrid control in an embodiment of the present application;

[0038] Figure 4 (c) is a schematic diagram of control curves of position and force in various directions during grinding and polishing of a curved workpiece surface by a robotic arm using force / position hybrid control based on sliding mode control in an embodiment of the present application;

[0039] Figure 5 1 is a flow chart of a grinding and polishing control method based on sliding mode control in an embodiment of the present application;

[0040] Figure 6 is another flow chart of the grinding and polishing control method based on sliding mode control in an embodiment of the present application;

[0041] Figure 7 It is a schematic diagram of the structure of a physical device of the control system in an embodiment of the present application. DETAILED DESCRIPTION

[0042] The terms used in the following examples of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification of the present application, the singular expressions "a", "an", "above", "the", and "this" are intended to include plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations of one or more of the listed items.

[0043] In the following, the terms "first" and "second" are used for descriptive purposes only and should not be understood to imply or suggest relative importance or implicitly indicate the number of the technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of this application, unless otherwise specified, "plurality" means two or more.

[0044] To facilitate understanding, the following introduces multiple control methods for the grinding and polishing process of the robotic arm in the scenarios corresponding to the embodiments of the present application.

[0045] See also Figure 1 (a), Figure 1 (a) is a control block diagram based on impedance control during the grinding and polishing process of the robotic arm in an embodiment of the present application; this control block diagram shows the implementation structure of the traditional impedance control method. Figure 1 In (a), the inputs include the desired position xd and the desired force Fd. Control is achieved by calculating the deviation from the actual position x and actual force F. The block diagram (IK) represents the inverse kinematics module, which converts the Cartesian position command into joint space. The transfer function (z^2 - 1.9801z + 0.9801)^-1 represents the designed impedance characteristic, which establishes the dynamic relationship between position and force. Two PID controllers are used for closed-loop control of the position and force loops, respectively. Their outputs are superimposed and applied to the manipulator. The system dynamics equation M(q)q̈ + C(q,q̇)q̇ + G(q) = τ describes the kinematic characteristics of the manipulator, where M(q) is the inertia matrix, C(q,q̇) represents the Coriolis and centrifugal force terms, and G(q) represents the gravity term. This control scheme achieves indirect coupled force and position control by adjusting the impedance parameters, but suffers from a lag in the force control response.

[0046] See also Figure 1 (b), Figure 1 (b) is a control block diagram of traditional force / position hybrid control during the grinding and polishing process of the robotic arm in an embodiment of the present application; this control block diagram shows the implementation scheme of direct force / position hybrid control. Figure 1In (b), J represents the Jacobian matrix, and FK(q) represents forward kinematics, which are used for position feedback calculations. The position control channel includes a proportional gain Kpp (5.0) and a differential gain Kpd (0.1) to implement PD control. The force control channel includes a force feedback gain Kfp (0.001) and an integral element Kfi / s (0.2), where ±200 represents the saturation limit for force control. The outputs of the two control channels are superimposed and applied to the robotic arm. The system allocates force and position control tasks using the S matrix, achieving decoupled control in different directions. This scheme has faster force response characteristics than impedance control, but may experience large force overshoot during dynamic operation.

[0047] See also Figure 1 (c), Figure 1 (c) A control block diagram of the improved force / position hybrid control based on sliding mode control during the grinding and polishing process of the robotic arm in an embodiment of the present application; the control block diagram shows the improved force / position hybrid control scheme based on sliding mode control. Figure 1 The top of the block diagram (c) features a dynamic compensation module (CDNN(q,q̇)q̇+GSNN(q)) based on an RBF neural network (RBF neural network) to estimate and compensate for the system's nonlinear dynamic characteristics. The controller incorporates a sliding mode control term (sgn(x)) and a switching gain (λ) to enhance system robustness. The position control loop maintains a PD control structure (Kpp+Kpi / s) similar to the traditional scheme, but adds an adaptive adjustment mechanism. In addition to basic feedback control, the force control loop also incorporates a nonlinear sliding mode control term. The matrix S is selected to achieve an orthogonal decomposition of force / position control while also considering the system's dynamic characteristics (M(q)). This scheme enhances the system's ability to suppress external disturbances through sliding mode control and improves dynamic performance through neural network compensation, resulting in comprehensive improvements in force control accuracy and stability during the grinding process. The controller gain parameters (e.g., 5.0, 0.1, 0.001, 0.2, etc.) are optimized to ensure good dynamic performance while maintaining system stability.

[0048] Next, we will introduce the polishing scenarios and experimental data corresponding to the embodiments of this application. Figure 2 ,in Figure 2 (a) is a schematic diagram of a scene in which a robotic arm grinds and polishes a flat workpiece surface in an embodiment of the present application; Figure 2 (b) is a schematic diagram of a scene in which a robotic arm grinds and polishes a curved workpiece in an embodiment of the present application. Figure 2 (a) and Figure 2 (b) shows the control software simulation diagrams of the robot arm facing the flat workpiece surface and the curved workpiece surface during the grinding and polishing process. Figure 3 and Figure 4 The figure shows the control curves of the robot arm applying three control algorithms on the flat workpiece surface and the curved workpiece surface, which can intuitively display the relevant control fluctuations and responses.

[0049] See also Figure 3 ,in Figure 3 (a) is a schematic diagram of the control curves of position and force in various directions of a robotic arm for grinding and polishing a flat workpiece surface using a force / position hybrid control method based on impedance control in an embodiment of the present application; Figure 3 (b) is a schematic diagram of the control curves of position and force in various directions of the robot arm for grinding and polishing on a flat workpiece surface based on force / position hybrid control in an embodiment of the present application; Figure 3 (c) is a schematic diagram of the control curves of position and force in various directions during grinding and polishing of a robotic arm using force / position hybrid control based on sliding mode control on a flat workpiece surface in an embodiment of the present application.

[0050] correspond Figure 3 In the simulation, the compressive stress was set to 20N. Simulation experiments show that the three algorithms appear to have similar performance when applied to grinding on a flat workpiece surface. Force fluctuations during the grinding process are also minimal. This subtle difference is reflected in the pressure of the robotic arm's grinding head. Impedance control is relatively stable during initial downward pressure, with an overshoot of only approximately 18N. Classic force / position hybrid control achieves an overshoot of 230N, while SMC (sliding mode control)-based force / position hybrid control achieves an overshoot of 180N. However, due to its indirect force control, impedance control exhibits a slow response speed and unsatisfactory tracking accuracy. On a flat workpiece surface, its pressure is not precisely 20N, but rather gradually approaches 20N from a slightly lower 3N. In contrast, the deviation for classic force / position hybrid control is within ±0.05N, and for SMC-based force / position hybrid control, it is within ±1N.

[0051] See also Figure 4 ,in Figure 4 (a) is a schematic diagram of the control curves of position and force in various directions of the impedance-controlled robotic arm during polishing on a curved workpiece surface in an embodiment of the present application; Figure 4 (b) is a schematic diagram of the control curves of position and force in various directions of the robot arm for grinding and polishing on a curved workpiece surface based on force / position hybrid control in an embodiment of the present application; Figure 4 (c) is a schematic diagram of the control curves of position and force in various directions during grinding and polishing of a curved workpiece surface by a robotic arm using force / position hybrid control based on sliding mode control in an embodiment of the present application.

[0052] For grinding and polishing work on curved workpieces, the comparison between the force / position hybrid control algorithm and the impedance control algorithm immediately shows obvious superiority.

[0053] correspond Figure 4 As shown in (a), when sliding on a curved workpiece, the robot arm's pressure has difficulty consistently tracking the set force. During the downward movement, due to tracking delay, its force is less than the set force by 7N to 10N. However, during the upward movement from the valley bottom, its force exceeds the set force by 11N.

[0054] In comparison, reference Figure 4 (b) and Figure 4 (c) Force / position hybrid control, a direct force control approach, offers extremely high real-time response speed, thus ensuring tracking accuracy. While the tracking force fluctuation of classic force / position hybrid control is within ±0.5N, the tracking force fluctuation of SMC-based force / position hybrid control is only within ±2N, slightly larger than that of the classic force / position hybrid control algorithm. This is due to the inherent chattering characteristics of sliding mode control.

[0055] In summary, when grinding flat workpiece surfaces, impedance control produces the smallest downward pressure overshoot, followed by SMC-based force / position hybrid control, while classic force / position hybrid control produces the largest downward pressure overshoot. In terms of pressure smoothness, impedance control is slightly inferior to traditional force / position hybrid control and SMC-based force / position hybrid control. When grinding curved workpiece surfaces, impedance control produces the smallest downward pressure overshoot, followed by SMC-based force / position hybrid control, while classic force / position hybrid control produces the largest downward pressure overshoot. In terms of pressure smoothness, impedance control is far inferior to traditional force / position hybrid control and SMC-based force / position hybrid control.

[0056] Therefore, the grinding and polishing control method based on sliding mode control in this application, when grinding the surface of a flat workpiece, compared with impedance control, although the initial downward pressure over-impact force is slightly larger, the pressure stability is better, and the fluctuation range is only within ±1N; when grinding the surface of a curved workpiece, the pressure tracking accuracy is significantly better than impedance control, and the fluctuation range is controlled within ±2N, effectively overcoming the problem of impedance control response lag.

[0057] For ease of understanding, the following describes the process of the method provided by this implementation in combination with the above scenarios and related experimental data. Figure 5 , which is a flow chart of the grinding and polishing control method based on sliding mode control in an embodiment of the present application.

[0058] S501 , collecting a force signal from a force sensor at the end of the robotic arm, and decomposing the force signal into a normal force component perpendicular to the workpiece surface.

[0059] Among them, the force sensor at the end of the robot arm refers to a multi-dimensional force / torque sensor installed between the end effector of the robot arm and the tool, which is used to measure the contact force between the tool and the workpiece in real time; the force signal represents the three-dimensional force and three-dimensional torque data including magnitude and direction collected by the sensor; the normal force component refers to the force component perpendicular to the surface of the workpiece, which is used to indicate the magnitude of the actual grinding pressure.

[0060] The control system needs to monitor and control the grinding pressure in real time while the robotic arm is performing grinding and polishing operations. Specifically, the control system first collects force signal data from the end-of-line force sensor at a preset sampling frequency (e.g., 1kHz). Then, based on the workpiece surface normal vector, the collected three-dimensional force signal is decomposed into normal and tangential components. The normal component represents the actual grinding pressure and is used for subsequent force control.

[0061] In some embodiments, force signal acquisition and decomposition can be achieved through a variety of methods: Optionally, force signals can be acquired and decomposed through the following steps: configuring force sensor sampling parameters; acquiring raw force signal data; performing signal filtering processing; obtaining surface normal vectors based on the workpiece CAD model; and decomposing the force signal projection to obtain normal force components. Optionally, force signals can be acquired and decomposed through the following steps: real-time detection of force sensor status; acquiring raw data and calibrating it; determining the workpiece surface normal through contact detection; and calculating the projection of the force signal onto the normal direction. It is understood that other signal acquisition and processing methods can also be used to achieve force signal acquisition and decomposition, and are not limited here.

[0062] S502: Determine a deviation between the normal force component and a preset target pressure, and construct a sliding surface expression for sliding mode control based on the deviation.

[0063] Among them, the preset target pressure represents the expected grinding pressure value, which is used to ensure the processing quality; the deviation value refers to the difference between the actual pressure and the target pressure; the sliding surface expression refers to the mathematical expression that describes the dynamic characteristics of the system state deviation, which is used to construct the sliding mode control law.

[0064] The control system requires a controller based on force deviation to achieve pressure tracking control. Specifically, the control system first calculates the deviation between the actual normal force and the target pressure, and simultaneously calculates the integral term of the deviation within a preset time window. The deviation value, the integral term, and the preset sliding mode coefficient are then weighted together to construct a sliding mode surface expression that reflects the system's dynamic characteristics.

[0065] In some embodiments, the sliding surface expression can be constructed through various methods: Optionally, it can be constructed through the following steps: calculating the force deviation and the rate of change of the deviation; designing the integral sliding surface form; determining the sliding mode coefficient; and combining these steps to obtain the sliding surface expression. Alternatively, it can be constructed through the following steps: determining the sliding surface structure based on Lyapunov stability analysis; introducing an integral term to suppress steady-state error; and optimizing the sliding mode coefficient to suppress chattering. It is understood that other control theory methods can also be used to construct the sliding surface expression, which is not limited here.

[0066] S503: Determine the force control component according to the sliding surface expression and the force control gain coefficient.

[0067] The force control component represents the control output for adjusting the grinding pressure; the force control gain coefficient refers to the parameter used to adjust the response characteristics of the controller.

[0068] The control system needs to generate a force control law based on sliding mode control theory. Specifically, the control system uses the constructed sliding surface expression and combines it with the preset force control gain coefficient to design an equivalent control law and a switching control law that meet Lyapunov stability requirements. The force control component is then derived by combining the two.

[0069] In some embodiments, the force control component can be calculated using a variety of methods: Optionally, the force control component can be calculated using the following steps: designing an equivalent control term; designing a switching control term; combining these to obtain a continuous control law; and introducing a boundary layer to eliminate chattering. Optionally, the force control component can be calculated using the following steps: adjusting the gain coefficient based on an adaptive law; designing an exponential reaching law; and constructing a continuous control law. It is understood that other sliding mode control methods can also be used to determine the force control component, which is not limited here.

[0070] S504 : Determine the position deviation and speed deviation based on the collected position signals of the joint encoders and the preset grinding trajectory.

[0071] Among them, the joint encoder signal represents the actual angular position of each joint of the robotic arm; the preset grinding trajectory refers to the expected tool motion trajectory; the position deviation represents the difference between the actual position and the expected position; and the speed deviation represents the difference between the actual speed and the expected speed.

[0072] The control system needs to achieve precise trajectory tracking control. Specifically, the control system first converts the joint angles into the Cartesian space position of the end effector through forward kinematics. This is then compared with the preset trajectory to obtain the position deviation. Simultaneously, the position signal is differentiated and compared with the desired velocity to obtain the velocity deviation.

[0073] In some embodiments, position and velocity deviations can be calculated using a variety of methods. Optionally, the calculation can be performed using the following steps: acquiring encoder data; performing a forward kinematic transformation; calculating the position deviation; numerically differentiating the actual velocity; and calculating the velocity deviation. Alternatively, the calculation can be performed using the following steps: estimating the velocity based on an observer; calculating the error using coordinate transformation; and filtering to eliminate noise. It is understood that other kinematic analysis methods can also be used to calculate motion deviations, which are not limited here.

[0074] S505 : Determine the position control component according to the position deviation, the speed deviation, the position gain coefficient, and the speed gain coefficient.

[0075] Among them, the position control component represents the control output for trajectory tracking; the position gain coefficient refers to the parameter for adjusting the position response characteristics; the velocity gain coefficient refers to the parameter for adjusting the velocity response characteristics; the position deviation and velocity deviation reflect the degree of deviation between the actual motion state and the desired trajectory.

[0076] The control system needs to achieve accurate position tracking control. Specifically, the control system adopts a PD control structure. The position deviation is multiplied by the position gain coefficient to obtain the proportional term, and the velocity deviation is multiplied by the velocity gain coefficient to obtain the differential term. The two are superimposed to obtain the position control component to achieve precise control of the end position of the robot arm.

[0077] In some embodiments, the position control component can be calculated using a variety of methods: Optionally, the calculation can be performed using the following steps: adaptively adjusting the gain coefficient; calculating the proportional control term; calculating the differential control term; combining the control variable; and performing saturation limiting. Alternatively, the calculation can be performed using the following steps: adjusting the gain using fuzzy rules; introducing a nonlinear control term; and designing a feedforward compensation control. It is understood that other control algorithms can also be used to calculate the position control component, and this is not limited here.

[0078] S506: Construct a selection matrix based on the normal direction and the tangential direction of the workpiece surface, and perform orthogonal decomposition and superposition on the force control component and the position control component based on the selection matrix to obtain a comprehensive control instruction.

[0079] Among them, the selection matrix represents the matrix that distributes control weights in the normal and tangential directions; the normal direction refers to the direction perpendicular to the workpiece surface; the tangential direction refers to the direction parallel to the workpiece surface; and the comprehensive control instruction refers to the final control output combining force control and position control.

[0080] The control system needs to coordinate force and position control. Specifically, the control system first constructs a selection matrix based on the workpiece surface characteristics, prioritizing force control in the normal direction and position control in the tangential direction. The force and position control components are then orthogonally decomposed using the selection matrix. Finally, the decomposed control variables are superimposed to obtain the comprehensive control command.

[0081] In some embodiments, coordination of control components can be achieved through a variety of methods: Optionally, this can be achieved through the following steps: constructing an orthogonal projection matrix; decomposing force control components; decomposing position control components; and performing weighted superposition to obtain control instructions. Optionally, this can be achieved through the following steps: adaptively adjusting weight coefficients; constructing a hybrid control law; and optimizing the control allocation strategy. It is understood that other control allocation methods can also be used to achieve force-position hybrid control, and this is not limited here.

[0082] S507 , converting the integrated control instructions into drive signals for the motors of each joint, and transmitting the drive signals to the robotic arm to perform grinding and polishing based on sliding mode control.

[0083] Among them, the drive signal refers to the voltage or current signal that controls the movement of each joint motor; the joint motor represents the actuator that drives the movement of each joint of the robotic arm; and the comprehensive control instruction represents the desired motion and force in Cartesian space.

[0084] The control system needs to convert control instructions into actual drive signals. Specifically, the control system first converts the comprehensive control instructions in Cartesian space into motion instructions in joint space through inverse kinematics. Then, based on the characteristics of each joint motor, the motion instructions are converted into corresponding drive signals. Finally, the drive signals are transmitted to the robot arm controller via a communication interface to execute motion control.

[0085] In some embodiments, control instructions can be executed in a variety of ways: optionally, through the following steps: calculating an inverse kinematic solution; generating joint trajectories; calculating drive signals; executing motion control; and monitoring execution status. Alternatively, through the following steps: online optimal trajectory planning; dynamic compensation control; and real-time adjustment of control parameters. It is understood that other motion control methods can also be used to execute control instructions, and these are not limited here.

[0086] The following is a more detailed description of the process of the method provided by this implementation. Figure 6 , is another flow chart of the grinding and polishing control method based on sliding mode control in an embodiment of the present application.

[0087] S601 , controlling the robotic arm to contact the workpiece surface at a preset speed and perform a preset movement, and collecting a force-controlled displacement characteristic curve during the contact process.

[0088] Among them, the preset speed refers to the calibrated speed when the robot arm contacts the workpiece surface, which is used to ensure the stability of the contact process; the preset movement represents the standard test movement of the robot arm along the workpiece surface; the force-controlled displacement characteristic curve refers to the curve that records the relationship between force and displacement during the contact process, which is used to characterize the material properties of the workpiece; the contact process refers to the entire process from the tool never touching to the tool fully contacting the workpiece surface.

[0089] Before beginning the grinding operation, the control system needs to identify the workpiece material characteristics. Specifically, the control system first controls the robotic arm to approach the workpiece surface at a low speed of 0.1mm / s. After detecting contact, it performs a preset probing motion along the workpiece surface. During this process, it synchronously collects data from the force sensor and position encoder at a sampling frequency of 1kHz to generate a force-displacement curve describing the material characteristics.

[0090] In some embodiments, characteristic curves can be collected through various methods. Optionally, the following steps may be used to collect characteristic curves: setting a contact detection threshold; controlling low-speed approach motion; detecting the initial contact point; executing a preset detection trajectory; collecting force-displacement data; and filtering the data. Alternatively, the following steps may be used to collect characteristic curves: detecting motion using a multi-point grid; calibrating the sensor online; adaptively adjusting sampling parameters; and constructing a characteristic curve. It is understood that other detection methods may also be used to collect characteristic curves, and these are not intended to be limiting.

[0091] S602. Determine the stiffness coefficient of the workpiece surface according to the force-controlled displacement characteristic curve.

[0092] Among them, the stiffness coefficient refers to the parameter that characterizes the ability of the workpiece surface to resist deformation; the force-controlled displacement characteristic curve represents the deformation characteristics of the material under load; and the workpiece surface refers to the surface material of the workpiece to be processed.

[0093] The control system needs to determine the material stiffness characteristics based on the test data. Specifically, the control system performs a piecewise linear fit on the collected force-displacement curve, calculates the slope of the curve to obtain the local stiffness value, and then takes the average value within the working range as the equivalent stiffness coefficient of the workpiece surface.

[0094] In some embodiments, the stiffness coefficient can be determined through various methods: Optionally, the stiffness coefficient can be determined through the following steps: data preprocessing and denoising; piecewise linear fitting; calculation of local stiffness; and statistical analysis to determine equivalent stiffness. Alternatively, the stiffness coefficient can be determined through the following steps: establishing a nonlinear stiffness model; fitting parameters using the least squares method; and verifying model accuracy. It is understood that other parameter identification methods can also be used to determine the stiffness coefficient, and these are not limited here.

[0095] S603: Determine the material damping coefficient of the workpiece surface based on the contact transient response characteristics, and generate a material characteristic model according to the material damping coefficient and stiffness coefficient.

[0096] Among them, the contact transient response characteristics refer to the dynamic response process of the material under the action of impact load; the material damping coefficient represents the ability of the material to dissipate mechanical energy; the material property model refers to the mathematical model that describes the mechanical properties of the material, which is used to predict the dynamic response characteristics of the material during the processing process; the stiffness coefficient represents the ability of the material to resist deformation.

[0097] The control system requires a complete material dynamics model. Specifically, the control system analyzes the oscillation attenuation characteristics during contact, calculates the damping ratio using the logarithmic decay method, and, combined with the known stiffness coefficient, establishes a second-order dynamics model that includes mass, stiffness, and damping characteristics as the material property model.

[0098] It should be noted that during the training process, the material property model is based on the force-displacement characteristic curve data and contact transient response data at a preset speed. The stiffness coefficient is obtained through piecewise linear fitting of the force-displacement curve, and the damping coefficient is extracted from the oscillation response using the logarithmic decay method. The second-order dynamic model is constructed in combination with the mass parameters. The training criterion is to minimize the fitting error between the model prediction value and the measured force-displacement curve. The material property model contains the second-order dynamic equation of mass-stiffness-damping, which describes the dynamic response characteristics of the workpiece material under load. By inputting contact force and displacement, the material property model can output the dynamic response prediction of the material, which is used to determine the force control gain coefficient and the target pressure value.

[0099] In some embodiments, the material property model can be constructed using a variety of methods: Optionally, the model can be constructed using the following steps: collecting impact response data; calculating the oscillation period; determining the attenuation coefficient; identifying system parameters; and verifying model accuracy. Alternatively, the model can be constructed using the following steps: identifying parameters using frequency domain analysis; establishing a nonlinear dynamic model; optimizing model parameters; and evaluating model performance. It is understood that other modeling methods can also be used to construct the material property model, and these are not limited here.

[0100] S604: Determine a force control gain coefficient and a preset target pressure based on the material property model.

[0101] Among them, the force control gain coefficient refers to the parameter used to adjust the response characteristics of the force controller; the preset target pressure represents the desired grinding pressure value; and the material property model includes the dynamic response characteristics of the workpiece surface.

[0102] The control system needs to optimize control parameters based on material properties. Specifically, the control system performs a closed-loop stability analysis based on the material property model. Using pole placement, it calculates the force control gain range that ensures system stability. Furthermore, based on the material's hardness and strength, it determines the target pressure value that ensures machining quality without damaging the workpiece.

[0103] In some embodiments, control parameters can be determined through a variety of methods: Optionally, they can be determined through the following steps: stability analysis; calculation of critical gains; optimization of control parameters; simulation verification of system performance; and determination of a safe pressure range. Alternatively, they can be determined through the following steps: establishment of a fuzzy rule base; adaptive gain adjustment; online optimization of the target pressure; and evaluation of control effectiveness. It is understood that other parameter optimization methods can also be used to determine control parameters, and these are not limited here.

[0104] In some embodiments, the control system constructs a grinding database containing material properties and grinding parameters based on the material property model; collects real-time operation data including force data and position data during the grinding process, and determines the grinding quality index based on the real-time operation data; when the grinding quality index is lower than the preset qualified index, extracts the optimal grinding parameters of similar workpiece materials from the grinding database; based on the optimal grinding parameters, determines the force control gain coefficient and the preset target pressure.

[0105] Among them, the material property model refers to the mathematical model that describes the mechanical properties of the workpiece material; the grinding database refers to the data set that stores material properties and corresponding grinding parameters; real-time operation data refers to the force and position information collected during the grinding process; the grinding quality index is used to indicate the processing quality level; the preset qualified index refers to the standard threshold for quality assessment; the optimal grinding parameters refer to the parameter combination with the best performance in historical data.

[0106] The control system needs to continuously optimize control parameters during the grinding process. Specifically, it first establishes a database containing different material properties and corresponding grinding parameters, collects processing data in real time, and calculates the quality index. If the quality does not meet the standard, it retrieves the optimal parameters of similar cases from the database based on the current workpiece material characteristics, and dynamically adjusts the force control gain and target pressure to achieve adaptive parameter optimization.

[0107] In some embodiments, polishing parameter optimization can be achieved through a variety of methods: Optionally, optimization can be achieved through the following steps: constructing a hierarchical database structure; real-time feature extraction and matching; parameter similarity calculation; optimal parameter screening; and online parameter adjustment. Optionally, optimization can be achieved through the following steps: establishing a deep learning model; quality assessment and prediction; parameter space search; and iterative optimization and update. It is understood that other intelligent algorithms can also be used to achieve polishing parameter optimization, which is not limited here.

[0108] S605 , collecting a force signal from a force sensor at the end of the robotic arm, and decomposing the force signal into a normal force component perpendicular to the workpiece surface.

[0109] Referring to step S501 , the control system collects the force signal and determines the normal force component.

[0110] S606: Calculate the deviation between the normal force component and the preset target pressure and the integral term of the deviation within the preset time window.

[0111] Among them, the normal force component refers to the actual pressure value perpendicular to the workpiece surface; the preset target pressure represents the desired grinding pressure; the deviation value refers to the instantaneous difference between the actual pressure and the target pressure; the preset time window refers to the time range of the integral calculation, which is used to eliminate steady-state errors; the deviation integral term represents the cumulative effect of the pressure error within the time window.

[0112] The control system needs to calculate the pressure control error to construct a sliding mode controller. Specifically, the control system calculates the difference between the actual normal force and the target pressure in real time and integrates the error using a sliding time window (e.g., 0.1 seconds). This integral term improves the system's tracking accuracy and interference rejection.

[0113] In some embodiments, deviation calculation can be implemented through various methods: Optionally, calculation can be performed through the following steps: real-time force signal acquisition; filtering to eliminate high-frequency noise; calculating pressure deviation; setting an integration window; calculating the integral value; and dynamically updating the integral upper limit. Optionally, calculation can be performed through the following steps: adaptively adjusting the window size; performing integral calculation with clipping; compensating for time lag effects; and optimizing the integration strategy. It is understood that other error calculation methods can also be used to determine pressure deviation, and these are not limited here.

[0114] S607: Perform weighted combination of the deviation value, the deviation integral term, and the sliding mode coefficient to obtain a sliding surface expression.

[0115] Among them, the sliding mode coefficient refers to the weight parameter used to adjust the dynamic characteristics of the system; the sliding surface expression represents the deviation characteristics between the system state and the expected trajectory; and the weighted combination refers to the linear combination of each item according to different weights.

[0116] The control system needs to construct a suitable sliding surface to achieve robust control. Specifically, the control system linearly combines the pressure deviation, the deviation integral term, and the preset sliding mode coefficient to construct a sliding surface expression that reflects the system's dynamic characteristics and satisfies Lyapunov stability. This provides a basis for subsequent control law design.

[0117] In some embodiments, the sliding surface can be constructed using a variety of methods. Optionally, the sliding surface can be constructed using the following steps: determining the sliding surface structure; designing integral term weights; optimizing the sliding mode coefficients; verifying stability conditions; and adjusting dynamic characteristics. Alternatively, the sliding surface can be constructed using the following steps: introducing nonlinear terms; adaptively adjusting weights; constructing a composite sliding surface; and analyzing convergence performance. It is understood that other control theory methods can also be used to construct the sliding surface expression, which is not limited here.

[0118] In some embodiments, before step S607, the control system first constructs a grinding dynamic model including the surface curvature of the workpiece and the posture of the grinding tool; determines the Lyapunov function based on the grinding dynamic model; solves the derivative expression of the Lyapunov function, determines the adaptive law parameter range, and determines the sliding mode coefficient based on the adaptive law parameter range.

[0119] Among them, the polishing dynamic model refers to the mathematical model that describes the interaction process between the tool and the workpiece; the workpiece surface curvature represents the curvature information of the surface geometric features; the polishing tool posture refers to the spatial position relationship of the tool relative to the workpiece surface; the Lyapunov function is a scalar function used to represent the stability of the system; the adaptive law parameter refers to the parameter used to adjust the adaptability of the controller; the sliding mode coefficient represents the key design parameter of the sliding mode controller.

[0120] The control system must ensure system stability before constructing the sliding surface. Specifically, the control system first establishes a dynamic model that takes into account the surface curvature and tool posture. Based on this model, a Lyapunov function that reflects the energy changes of the system is constructed. By solving its derivative expression, the stability condition is obtained, and the parameter range of the adaptive law is determined. Ultimately, the sliding mode coefficient that ensures system stability is obtained.

[0121] It should be noted that the polishing dynamic model is trained based on workpiece surface curvature data and polishing tool posture parameters, combined with mechanical theory to establish dynamic equations that take geometric features into account. The model parameters are optimized through Lyapunov stability analysis. The training criteria are parameter constraints that ensure system stability. This polishing dynamic model describes the nonlinear dynamic equations of the interaction between the tool and the workpiece surface, incorporating surface geometry and tool kinematic constraints. By inputting the tool state and surface features, the polishing dynamic model outputs a prediction of the system's dynamic response, which is used for sliding mode controller design and parameter optimization.

[0122] In some embodiments, control parameters can be determined through a variety of methods: Optionally, they can be determined through the following steps: establishing nonlinear dynamic equations; selecting Lyapunov candidate functions; performing stability analysis; resolving parameter constraints; and optimizing sliding mode coefficients. Alternatively, they can be determined through the following steps: simplifying model construction; performing piecewise linearization; performing robustness analysis; and designing adaptive laws. It is understood that other control theory methods can also be used to determine parameters, and these are not limited here.

[0123] S608: Determine the force control component according to the sliding surface expression and the force control gain coefficient.

[0124] Referring to step S503 , the control system determines the force control component.

[0125] S609 , determining the position deviation and speed deviation based on the collected position signals of the joint encoders and the preset grinding trajectory.

[0126] Referring to step S504 , the control system determines the position deviation and the speed deviation.

[0127] S610: Determine a position control component according to the position deviation, the speed deviation, the position gain coefficient, and the speed gain coefficient.

[0128] Referring to step S505 , the control system determines the position control component.

[0129] S611. Construct a selection matrix based on the normal direction and the tangential direction of the workpiece surface, and perform orthogonal decomposition and superposition of the force control component and the position control component based on the selection matrix to obtain a comprehensive control instruction.

[0130] Referring to step S506 , the control system generates a comprehensive control instruction.

[0131] S612: Convert the integrated control instructions into drive signals for the motors of each joint, and transmit the drive signals to the robotic arm to perform grinding and polishing based on sliding mode control.

[0132] Referring to step S507, the control system will issue a comprehensive control instruction.

[0133] S613. Collect surface quality data of the workpiece surface during the grinding process.

[0134] Among them, surface quality data refers to the measurement data that characterizes the surface processing status of the workpiece, including parameters such as roughness and smoothness; the grinding process refers to the entire cycle of the robotic arm performing the grinding operation; the workpiece surface refers to the surface area of the workpiece to be processed, which is used to evaluate the processing effect.

[0135] The control system needs to monitor the polishing effect in real time to achieve quality control. Specifically, during the polishing process, the control system uses multimodal sensing devices such as visual sensors and force sensors to collect real-time status data on the workpiece surface, including surface topography, reflective properties, contact force characteristics, and other information. It then performs data preprocessing and feature extraction.

[0136] In some embodiments, quality data can be collected through a variety of methods. Optionally, data collection can be performed through the following steps: configuring a multimodal sensor; synchronously collecting image data; collecting force and tactile data; extracting surface features; constructing a quality feature vector; and performing data fusion processing. Alternatively, data collection can be performed through the following steps: online optical measurement; dynamic force characteristic analysis; vibration signal processing; and establishing a feature database. It is understood that other sensing methods can also be used to collect surface quality data, and these are not limited here.

[0137] S614: Determine a key polishing area on the workpiece surface based on the surface quality data and a preset polishing judgment threshold.

[0138] Among them, the preset grinding judgment threshold refers to the standard parameter used to evaluate the surface quality; the key grinding area represents the surface area that needs to be processed intensively; and the surface quality data includes various characteristic parameters of the workpiece surface.

[0139] The control system needs to identify under-processed areas to optimize the grinding strategy. Specifically, the control system compares and analyzes the collected surface quality data with the preset quality standards. Using a clustering algorithm, it identifies areas where quality parameters fall below the threshold. Based on the workpiece geometry, it determines the scope of the local areas requiring focused grinding and the processing requirements.

[0140] In some embodiments, key areas can be identified through a variety of methods: Optionally, they can be identified through the following steps: setting quality evaluation indicators; data normalization; feature clustering analysis; threshold classification; region boundary extraction; and generating a processing area map. Optionally, they can be identified through the following steps: establishing a deep learning model; feature recognition and classification; quality assessment and scoring; and determining a processing priority order. It is understood that other image processing and pattern recognition methods can also be used to identify key polishing areas, and these are not limited here.

[0141] S615: Determine the posture angle, local dwell time, and grinding speed of the grinding tool according to the key grinding area.

[0142] Among them, the posture angle refers to the spatial direction of the grinding tool relative to the workpiece surface; the local dwell time indicates the duration of processing in a specific area; the grinding speed refers to the movement speed of the tool relative to the workpiece; the key grinding area contains the location information and quality requirements that need to be focused on.

[0143] The control system needs to optimize local machining parameters to improve machining efficiency. Specifically, based on the geometric characteristics and quality requirements of the key grinding area, combined with the material removal rate model, the control system calculates the optimal tool posture to meet machining requirements. It also dynamically allocates machining time based on quality differences and performs speed planning to ensure machining results.

[0144] It should be noted that the material removal rate model is trained based on process parameters such as grinding pressure, speed, and tool posture, along with corresponding material removal data. Regression analysis is used to establish a mapping between these parameters and removal efficiency. The training criterion is to minimize the error between the predicted removal rate and the actual machining result. The model itself: A mathematical model that describes the relationship between process parameters and material removal results, taking into account multiple influencing factors such as pressure, speed, and posture. Model usage: Input the desired material removal amount and output the desired machining parameter combination, which is used to optimize local dwell time and motion planning.

[0145] In some embodiments, machining parameter optimization can be achieved through various methods: Optionally, optimization can be achieved through the following steps: establishing a material removal model; analyzing surface curvature characteristics; optimizing tool posture angles; calculating minimum dwell time; velocity trajectory planning; and simulation verification schemes. Optionally, optimization can be achieved through the following steps: constructing a machining efficiency model; performing multi-objective parameter optimization; generating spatiotemporal trajectories; and evaluating machining results. It is understood that other optimization algorithms can also be used to determine machining parameters, and these are not limited here.

[0146] In some embodiments, the control system constructs a dynamic model of the grinding process including the material removal rate based on the key grinding area; calculates the local residence time of each local interval based on the dynamic model; calculates the trajectory curvature of each local interval in combination with the workpiece surface features, and determines the posture angle and speed constraint conditions of the grinding tool based on the trajectory curvature; solves the speed planning scheme that meets the speed constraint conditions to obtain the grinding speed.

[0147] Among them, material removal rate refers to the amount of material removed per unit time; the dynamic model refers to the mathematical model that describes the dynamic characteristics of the grinding process; the local interval refers to the segmented processing area on the workpiece surface; the trajectory curvature refers to the curvature characteristics of the tool motion path; the speed constraint condition refers to the speed limit considering the process requirements; the speed planning scheme is used to guide the tool motion control.

[0148] The control system needs to optimize the motion trajectory based on the key grinding areas. Specifically, the control system establishes a dynamic model that includes material removal rate, calculates the processing time required for each interval, and considers the surface curvature of the workpiece. It determines the tool posture and speed constraints that meet the processing requirements. Finally, an optimization algorithm is used to solve the speed planning solution that meets the constraints.

[0149] In some embodiments, trajectory planning can be achieved through various methods: Optionally, planning can be achieved through the following steps: establishing a material removal model; allocating interval times; analyzing posture constraints; calculating velocity boundaries; optimizing and solving trajectories; and simulation verification. Optionally, planning can be achieved through the following steps: task decomposition and sequencing; multi-target trajectory planning; real-time collision detection; and velocity trajectory smoothing. It is understood that other path planning methods can also be used to generate motion trajectories, and these are not limited here.

[0150] In the embodiments of this application, a force / position hybrid control scheme based on sliding mode control is adopted, combined with technical means such as workpiece material characteristic identification, adaptive parameter optimization, and real-time quality monitoring. This allows for fast and accurate pressure tracking control while ensuring system stability. This effectively addresses the problems of impedance control in the prior art, such as response lag, large pressure fluctuations, and reliance on experience for parameter adjustment. This results in an intelligent grinding and polishing system that adapts to different workpiece materials and has self-learning optimization capabilities. Experimental results show that this scheme can control pressure fluctuations during curved workpiece grinding to within a range of ±2N, significantly outperforming traditional methods. Furthermore, through online quality monitoring and parameter optimization, processing efficiency and quality consistency are further improved.

[0151] The following describes the control system in the embodiment of the present invention from the perspective of hardware processing. Figure 7 , which is a schematic diagram of the structure of a physical device of the control system in an embodiment of the present application.

[0152] It should be noted that Figure 7 The structure of the control system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0153] like Figure 7 As shown, the control system includes a central processing unit (CPU) 701, which can perform various appropriate actions and processes, such as the methods described in the above embodiments, based on programs stored in a read-only memory (ROM) 702 or programs loaded from a storage unit 708 into a random access memory (RAM) 703. RAM 703 also stores various programs and data required for system operation. CPU 701, ROM 702, and RAM 703 are interconnected via a bus 704. An input / output (I / O) interface 705 is also connected to bus 704.

[0154] The following components are connected to the I / O interface 705: an input section 706 including an audio input device, push button switches, and the like; an output section 707 including a liquid crystal display (LCD), an audio output device, indicator lights, and the like; a storage section 708 including a hard disk and the like; and a communication section 709 including a network interface card such as a LAN (Local Area Network) card or a modem. The communication section 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the I / O interface 705 as needed. Removable media 711, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 710 as needed, so that computer programs read from the removable media can be installed in the storage section 708 as needed.

[0155] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for executing the methods illustrated in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via the communication section 709 and / or installed from removable media 711. When executed by the central processing unit (CPU) 701, the computer program performs the various functions defined in the present invention.

[0156] It should be noted that specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0157] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram can represent a module, program segment, or part of the code, and the above-mentioned module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings.

[0158] Specifically, the control system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, the grinding and polishing control method based on sliding mode control provided in the above embodiment is implemented.

[0159] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the control system described in the above embodiments, or may exist independently and not be incorporated into the control system. The storage medium carries one or more computer programs, which, when executed by a processor of the control system, enable the control system to implement the grinding and polishing control method based on sliding mode control provided in the above embodiments.

[0160] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0161] As used in the above embodiments, the term “when” may be interpreted to mean “if” or “after” or “in response to determining that” or “in response to detecting that”, depending on the context. Similarly, the phrases “upon determining that” or “if (stated condition or event) is detected” may be interpreted to mean “if determining that” or “in response to determining that” or “upon detecting (stated condition or event)” or “in response to detecting (stated condition or event)”, depending on the context.

[0162] Those skilled in the art will appreciate that all or part of the process steps in the above-described method embodiments can be implemented by a computer program instructing the relevant hardware. The program can be stored in a computer-readable storage medium, and when executed, the program can include the process steps in the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A grinding and polishing control method based on sliding mode control, characterized in that: Applied to a control system, the method includes: Collecting a force signal from a force sensor at the end of the robotic arm and decomposing the force signal into a normal force component perpendicular to the workpiece surface; Determining a deviation between the normal force component and a preset target pressure, and constructing a sliding surface expression for sliding mode control based on the deviation; Determining a force control component according to the sliding surface expression and the force control gain coefficient; Determine the position deviation and speed deviation based on the collected position signals of each joint encoder and the preset grinding trajectory; Determining a position control component according to the position deviation, the speed deviation, a position gain coefficient, and a speed gain coefficient; Constructing a selection matrix based on the normal direction and the tangential direction of the workpiece surface, and performing orthogonal decomposition and superposition on the force control component and the position control component based on the selection matrix to obtain a comprehensive control instruction; The integrated control instructions are converted into drive signals for the motors of each joint, and the drive signals are transmitted to the robotic arm to perform grinding and polishing based on sliding mode control.

2. The method according to claim 1, characterized in that The step of determining the deviation between the normal force component and the preset target pressure, and constructing a sliding surface expression for sliding mode control based on the deviation, specifically includes: Calculating a deviation between the normal force component and the preset target pressure and an integral term of the deviation within a preset time window; The deviation value, the deviation integral term and the sliding mode coefficient are weightedly combined to obtain a sliding mode surface expression.

3. The method according to claim 2, characterized in that Before the step of performing weighted combination of the deviation value, the deviation integral term, and the sliding mode coefficient to obtain a sliding mode surface expression, the method further includes: Construct a grinding dynamic model including the workpiece surface curvature and grinding tool posture; Determining a Lyapunov function based on the polishing dynamic model; A derivative expression of the Lyapunov function is solved to determine a range of adaptive law parameters, and a sliding mode coefficient is determined according to the range of adaptive law parameters.

4. The method according to claim 1, wherein After the step of converting the integrated control instruction into a drive signal for each joint motor and transmitting the drive signal to the robotic arm to perform grinding and polishing based on sliding mode control, the method further includes: Collect surface quality data of workpiece surface during grinding; Determining a key polishing area on the workpiece surface according to the surface quality data and a preset polishing judgment threshold; According to the key grinding area, the posture angle, local dwell time and grinding speed of the grinding tool are determined.

5. The method according to claim 4, characterized in that The step of determining the posture angle, local dwell time and grinding speed of the grinding tool according to the key grinding area specifically includes: Constructing a dynamic model of the grinding process including material removal rate according to the key grinding area; Based on the kinetic model, calculating the local residence time of each local interval; Calculating the trajectory curvature of each local interval in combination with the workpiece surface features, and determining the posture angle and speed constraint conditions of the grinding tool according to the trajectory curvature; A speed planning scheme that satisfies the speed constraint conditions is solved to obtain the polishing speed.

6. The method according to claim 1, characterized in that Before the step of collecting the force signal of the force sensor at the end of the robot arm and decomposing the force signal into a normal force component perpendicular to the surface of the workpiece, the method further includes: Control the robotic arm to contact the workpiece surface at a preset speed and perform a preset movement, and collect the force-controlled displacement characteristic curve during the contact process; determining a stiffness coefficient of the workpiece surface according to the force-controlled displacement characteristic curve; Determining a material damping coefficient of the workpiece surface based on contact transient response characteristics, and generating a material property model according to the material damping coefficient and the stiffness coefficient; Based on the material property model, a force control gain coefficient and a preset target pressure are determined.

7. The method according to claim 6, characterized in that The step of determining the force control gain coefficient and the preset target pressure based on the material property model specifically includes: Based on the material property model, constructing a polishing database including material properties and polishing parameters; collecting real-time operation data including force data and position data during the grinding process, and determining a grinding quality index based on the real-time operation data; When the polishing quality index is lower than a preset qualified index, extracting optimal polishing parameters of similar workpiece materials from the polishing database; Based on the optimal grinding parameters, a force control gain coefficient and a preset target pressure are determined.

8. A control system, characterized in that: The control system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the control system to execute the method as described in any one of claims 1 to 7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a control system, the control system is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is run on a control system, the control system is caused to execute the method according to any one of claims 1 to 7.

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