Positioning control method for accurate operation of drill jumbo under complex working conditions

Abstract

The invention discloses a positioning control method for accurate operation of a drill jumbo under complex working conditions, and aims to solve the technical problems of poor positioning accuracy and weak robustness of a traditional control method under the conditions of strong rock stratum disturbance, uncertain kinetic parameters, hydraulic driving nonlinearity and the like. Through kinematics modeling and dynamics simplification, complex dynamics characteristics of the mechanical arm are decomposed into internal parameter uncertainty (such as joint abrasion, hydraulic leakage and load change) and external strong interference (such as rock reaction force and high-frequency vibration impact). For internal nonlinearity and coupling terms, an RBF neural network (RBF-NN) is adopted to carry out online learning and approximation; aiming at external interference and neural network fitting errors, designing an extended state observer (ESO) to realize real-time compensation; and in combination with a self-adaptive Backstepping control strategy and a tracking differentiator (TD), a control framework of internal and external disturbance joint estimation and compensation is constructed, and high-precision trajectory tracking of an end effector of the mechanical arm is ensured. The method is suitable for precise control of high-degree-of-freedom engineering equipment in severe environments such as mines and tunnels.

Description

Technical Field

[0001] This invention belongs to the field of automated control technology for engineering machinery, specifically relating to the positioning control of rock drilling rigs under complex working conditions, and is particularly suitable for the precise operation control of a seven-degree-of-freedom rock drilling robotic arm in drill-and-blast tunnel construction. Its application scope covers various underground engineering projects such as railways, highways, water conservancy and hydropower, mining, and underground space development. It can effectively solve the problem of insufficient positioning accuracy of robotic arms caused by harsh working conditions such as variable geological conditions, narrow spaces, high dust levels, high humidity, and strong vibration and impact in tunnel excavation and mineral mining operations, providing core control technology support for the automated and efficient operation of rock drilling rigs. Background Technology

[0002] my country is a superpower in tunnel construction, with over 80% of its tunnels constructed using the drill-and-blast method. The drilling rig, as the core excavation equipment in drill-and-blast tunnel construction, is widely used in various underground projects such as railways, highways, water conservancy and hydropower, mining, and underground space development, undertaking key operational tasks such as tunnel excavation and mineral extraction.

[0003] The rock drilling rig, equipped with a seven-degree-of-freedom robotic arm, automates and efficiently performs the entire process of drilling layout, rock drilling, and blasting hole construction. The robotic arm consists of two prismatic joints (such as a telescopic arm and a slide) and five rotary joints (each joint rotates), providing the multi-degree-of-freedom spatial operation capability required to cover the working face. This allows it to flexibly adjust its posture on complex and changing working surfaces to meet the needs of drilling operations from multiple angles and directions.

[0004] As shown in Figure 1, the construction environment of the rock drilling rig is characterized by variable geological conditions, narrow space, and irregular working surfaces. It also presents harsh working conditions such as high dust levels, high humidity, and strong vibration and impact. These factors not only place higher demands on the structural strength and stability of the robotic arm itself, but also pose a significant challenge to the precise positioning of the rock drilling operation.

[0005] In complex working conditions, insufficient drilling positioning accuracy of the rock drilling rig's robotic arm can lead to problems such as hole position deviation, hole depth error, and hole direction deviation. These issues directly affect the blasting effect, resulting in over-excavation or under-excavation of the surrounding rock, impacting support safety, reducing tunneling efficiency, and increasing the difficulty and cost of subsequent construction. Therefore, precise positioning control technology for a seven-degree-of-freedom robotic arm under complex working conditions has become one of the key technologies to ensure efficient, safe, and low-consumption operation of rock drilling rigs.

[0006] Existing rock drilling rigs mostly rely on manual operation or simple semi-automatic control methods. Operation depends heavily on operator experience, resulting in low positioning efficiency and significant errors, making it difficult to meet the demands for high-precision, high-consistency drilling under complex geological conditions. Therefore, developing a precise positioning control method for rock drilling rig robotic arms that is suitable for complex working conditions and possesses high robustness and adaptability has significant engineering application value and broad prospects for widespread adoption.

[0007] In summary, the existing control methods for rock drilling rigs have the following limitations: 1. The dynamic model of the seven-DOF manipulator of a rock drilling rig is highly nonlinear, with severe coupling between the joints. Traditional control methods based on accurate models are difficult to describe this complex dynamic relationship. Existing model predictive control (MPC) relies on accurate dynamic models, which can lead to model inaccuracies under the strong coupling and nonlinear characteristics of the manipulator. It also suffers from high computational cost and insufficient real-time performance.

[0008] 2. The construction environment contains significant time-varying external disturbances such as rock reaction forces and high-frequency vibrations. Traditional PID control has poor adaptability to these time-varying disturbances, resulting in a significant decrease in positioning accuracy. The single extended state observer (ESO) does not specifically address the randomness of strong external disturbances, and its compensation effect is limited.

[0009] 3. Joint wear, hydraulic leakage, load changes, etc. cause internal parameter drift. The redundant structure of the seven-degree-of-freedom system makes the influence of parameter uncertainty more complex, and traditional fixed gain control strategies are difficult to adapt to. Single disturbance estimation methods (such as using only ESO) do not distinguish between internal parameter uncertainty and external disturbances, ignore the nonlinear characteristics of internal parameter changes, and have limited estimation accuracy.

[0010] 4. The redundancy of degrees of freedom means that the same end pose corresponds to multiple joint angle combinations. Traditional control methods (such as PID and MPC) ignore the influence of model uncertainty and disturbance on the optimal solution, resulting in non-optimal positioning paths and increased cumulative errors. Summary of the Invention

[0011] This invention relates to a positioning control method for a seven-degree-of-freedom rock drilling robot suitable for complex working conditions. Addressing the technical problems of poor positioning accuracy and weak robustness of traditional multi-degree-of-freedom robot control under conditions such as strong rock disturbance, uncertain dynamic parameters, and nonlinear hydraulic drive, this invention proposes an adaptive control method based on joint estimation and compensation of internal and external disturbances, which significantly improves the positioning accuracy and stability of drilling operations at the end of the robot arm.

[0012] A positioning and control method for precise operation of a rock drilling rig under complex working conditions includes the following steps: Step 1: Establish a kinematic model; Step 2: Simplify the dynamic model; Step 3: Design a joint estimation of internal and external disturbances; Step 4: Design the tracking differentiator (TD); Step 5: Design an adaptive backstepping control law; Step 6: Perform trajectory planning; Step 7: Conduct simulation experiments.

[0013] Furthermore, step S2 specifically includes: Step 2.1: Establish a standard dynamic model of the robotic arm using the Lagrange method; Step 2.2: Establish the actual dynamic model of the robotic arm; Step 2.3: Design a controller independently for each joint.

[0014] Furthermore, step S3 specifically includes: Step 3.1: Estimate the internal uncertainty; Step 3.2: Compensate for external disturbances and fitting errors.

[0015] Furthermore, step S7 specifically includes: Step 7.1: Set simulation conditions; Step 7.2: Simulation results and comparison.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. The RBF neural network (RBF-NN) is used to approximate the internal nonlinearity and coupling terms online, eliminating the need for an accurate global model and solving the model complexity problem caused by multi-degree-of-freedom coupling. The adaptive backstepping controller design reduces the dependence on the accuracy of the dynamic model and can maintain control accuracy even in scenarios with parameter drift or model simplification, breaking through the strong dependence of traditional methods on accurate models.

[0017] 2. An extended state observer (ESO) is designed to compensate for external disturbances (such as rock reaction force and high-frequency vibration) and neural network fitting errors in real time, so as to achieve rapid suppression of strong external disturbances. The joint compensation mechanism for internal and external disturbances combines ESO and RBF-NN, which breaks through the bottleneck that a single ESO or PID cannot adapt to strong external disturbances and ensures stability under strong disturbances.

[0018] 3. Disturbance and parameter uncertainty separation estimation: By learning and approximating internal parameter uncertainties (such as joint wear and hydraulic nonlinearity) online through RBF-NN, the nonlinear changes of internal parameters are accurately captured, solving the problem that single methods are insufficient in capturing internal characteristics; the adaptive backstepping control strategy ensures that the neural network weight estimation is bounded through the projection operator update law, adapts to parameter drift, and avoids the limitations of traditional fixed gain control.

[0019] 4. By combining a tracking differentiator (TD) to achieve smooth tracking of the desired trajectory, a continuous and differentiable reference signal is provided for trajectory planning under redundant degrees of freedom, reducing errors caused by non-optimal paths; the joint compensation mechanism for internal and external disturbances ensures that the joint angle combination of redundant degrees of freedom can still converge to the optimal solution when disturbances exist, reducing accumulated errors and improving trajectory tracking accuracy.

[0020] From the perspective of industry development, if this invention is promoted nationwide, it will provide core technical support for key projects such as railways, highways, water conservancy and hydropower, and underground space development, and help achieve the goals of intelligent, efficient, and safe development in my country's tunnel and underground engineering field. It has a positive and far-reaching significance for promoting the high-quality development of major infrastructure construction and the overall improvement of intelligent manufacturing level, and has significant economic, social, and strategic demonstration value. Attached Figure Description

[0021] Figure 1 These are the construction drawings for the rock drilling rig. Figure 2 This is a flowchart of the RBF-NN_ADRC control proposed in this patent. Figure 3 This is a 3D structural model of a seven-degree-of-freedom rock drilling robot arm in a three-arm rock drilling rig. Figure 4 This is a schematic diagram of the coordinate system of each link of a rock drilling robot arm established based on the DH method. Figure 5 This is a hole layout diagram for rock drilling operations. Figure 6 This is a curve comparing the actual trajectory and the desired trajectory of joint one when using the RBF-NN_ADRC control method of this invention. Figure 7 This is a curve comparing the actual trajectory of joint one with the desired trajectory when using the traditional PID control method. Figure 8 This is a curve comparing the actual trajectory and the desired trajectory of joint one when using a single ESO-ADRC control method. Figure 9 This is a comprehensive comparison chart of the actual and desired trajectories for three control methods (traditional PID, single ESO-ADRC, and the RBF-NN_ADRC of this invention). Detailed Implementation

[0022] like Figure 2 As shown, a positioning control method for precise operation of a rock drilling rig under complex working conditions includes the following steps: Step 1: Establish a kinematic model Clarify the conversion relationship between joint space (joint angle / velocity) and operating space (end position / attitude) so that the controller can control joint movement through the tool center point on the end effector determined by the hole diagram.

[0023] Construct a three-dimensional structural model of one of the seven-degree-of-freedom rock drilling robotic arms in a three-arm rock drilling rig (see...). Figure 3 The model clearly defines the main object to be controlled. It includes a support arm, seven joints (five rotary joints and two prismatic joints), and a connected end effector (drill bit). The joints are connected in sequence to form a complete series structure, providing the necessary structural basis for subsequent establishment of kinematic and dynamic models and analysis of motion and force characteristics.

[0024] Establish the coordinate system of each link using the DH method (see...) Figure 4 This lays the foundation for the coordinate system used in constructing the kinematic equations. By defining the coordinate system, the relative positions and attitude relationships between the links are clearly described, providing an important prerequisite for forward and inverse kinematic analysis and helping to accurately derive the conversion relationship between joint variables and end effector pose. Table 1 shows the parameters of each link in the rock drilling robot arm.

[0025] Table 1 Parameters of each link in the rock drilling robot arm

[0026] in:

[0027]

[0028]

[0029] For the link coordinate system Perform link transformation, link coordinate system Compared to Transformation This is called link transformation. General formula:

[0030] The link transformation between adjacent coordinate systems can be obtained using the general formula for link transformation:

[0031]

[0032] Transformation matrix T of rock drilling robot arm

[0033]

[0034]

[0035]

[0036]

[0037]

[0038]

[0039]

[0040]

[0041]

[0042]

[0043]

[0044]

[0045] Step 2: Simplify the dynamic model Step 2.1: Establish a standard dynamic model of the robotic arm using the Lagrange method. A standard dynamic model of the rock drilling robot (unloaded state) is established using the Lagrange method to describe its dynamic characteristics under unloaded and drilling conditions:

[0046] in Joint angle vector Symmetric positive definite mass (inertia) matrix Centrifugal force and Coriolis force coefficient matrix Gravity matrix : Joint input torque vector.

[0047] Step 2.2: Establish the actual dynamic model of the robotic arm The dynamic model of the robotic arm in the unloaded state does not contain disturbance terms and is the dynamic equation under ideal conditions.

[0048] Considering that the robotic arm is subjected to time-varying external disturbances (rock reaction force, high-frequency impact) and internal parameter uncertainties (wear, load variation, hydraulic nonlinearity, etc.) during drilling, the actual dynamic model of the robotic arm is expressed as follows:

[0049] in: Parameter uncertainty

[0050]

[0051] Mass / inertia perturbations (load changes, machining errors) Centrifugal force coefficient error Gravity compensation deviation Nonlinear friction ;

[0052] : Coefficient of viscous friction Coulomb friction Stribeck effect (frictional abrupt change at low speeds) Hydraulic drive hysteresis ;

[0053] Hydraulic gain (time-varying, affected by oil temperature and leakage) : Control input voltage / current : Hysteresis (described using the Bouc-Wen model):

[0054] Elastic deformation interference ;

[0055] Shaft torsional stiffness matrix Equilibrium position Structural damping matrix Step 2.3: Design an independent controller for each joint. The rock drilling robot arm is a strongly coupled multi-input multi-output system, requiring an independent controller design for each joint. Taking joint one as an example (the same applies to the other joints), its dynamic characteristics are extracted from the global equations mentioned above:

[0056] This dynamic equation reveals the strong coupling characteristics caused by the multi-degree-of-freedom nature of the rock drilling robot arm, the strong nonlinearity of friction, the randomness of external disturbances caused by real-time changes in rock strata and high-frequency vibration transmission, and the hysteresis brought about by hydraulic drive. The superposition of these factors makes it difficult to accurately construct the dynamic model of each joint.

[0057] This invention abandons the direct solution of the above-mentioned globally accurate model, and instead simplifies the dynamic equations. Specifically, it simplifies the coupling terms. Centrifugal force Gravity terms and nonlinear friction Uncertainties attributed to the model are approximated by RBF-NN, while rock reaction force, high-frequency vibration disturbance, hydraulic hysteresis, and RBF-NN fitting error are attributed to external disturbances and estimated by ESO. Taking joint one as an example, the logic for the other joints follows the same pattern.

[0058] Simplified dynamic model of joint one:

[0059] Convert to the standard form of ADRC:

[0060] in For joint angle Characterizes internal nonlinear dynamics (such as gravity, Coriolis force, centrifugal force). To control the gain and

[0061] External disturbances (rock reaction force, vibration, etc.)

[0062] After simplification of the dynamic model, the system only needs to consider the main diagonal terms. Estimate this value and use it as the adaptive gain parameter. The initial values ​​can be obtained without precisely solving the global dynamics matrix. This approach effectively reduces the dependence on parameter calibration while preserving the system's robustness against internal coupling and external disturbances.

[0063] Given that subsequent simulation experiments will use the tracking performance of the joint controller as an example to verify the controller performance, this section only calculates the parameters required for designing the joint control law. Designing other joint control laws requires The same method is used for calculation.

[0064] Define joint 1 as rotating about the z-axis of the base coordinate system (i.e., coordinate system 2), and define the main diagonal elements of the mass matrix. This represents the equivalent moment of inertia of the entire robotic arm about the z-axis of the base. According to the parallel axis theorem and the additivity of moments of inertia, the moment of inertia of the entire robotic arm about the z-axis of the base is equal to the sum of the moments of inertia of each link about the z-axis of the base.

[0065]

[0066] in: It is the z-component of the inertia tensor of link k in the base coordinate system (moment of inertia about the z-axis). Table 2 shows the moment of inertia of each link about the z-axis of the base, obtained from the mass properties of the 3D simulation model. , Table 2. Moments of inertia of each link about the z-axis of the base.

[0067] Substituting the data from Table 2 into the above formula, we get: (Retain two decimal places to meet control accuracy requirements).

[0068] Step 3: Design joint estimation of internal and external disturbances Step 3.1: Estimate internal uncertainty Radial basis function neural networks (RBF-NN) are used to approximate the internal nonlinear function. ,Right now

[0069] in For optimal weights, For radial basis function vectors, This represents the fitting error.

[0070] By leveraging the nonlinear approximation capability of RBF-NN, we can adaptively estimate the internal uncertainties caused by parameter variations and model simplification.

[0071] Step 3.2: Compensate for external disturbances and fitting errors Design an Extended State Observer (ESO) to estimate external disturbances. and RBF-NN fitting error The observer structure is as follows:

[0072] in , , For observation status, For observation error, , , For observer gain, and These are the estimates of the control gain and the neural network weights, respectively. Step 4: Design the tracking differentiator (TD) To improve the smoothness of the desired trajectory signal and suppress high-frequency noise, and to ensure the continuous differentiability of the input signal of the adaptive backstepping controller, a tracking differentiator (TD) based on the extended state observer concept is used to generate and filter the desired trajectory and its first derivative online.

[0073] If the desired trajectory Therefore, the core design equation of the tracking differentiator is:

[0074] in, To determine the desired trajectory The tracking output, To estimate the first derivative (velocity) of the desired trajectory, To adjust the gain parameters for tracking speed and filtering performance, This is the Han sliding mode function, used for smoothing nonlinear tracking. h is the step size of the differentiator.

[0075] Han function expression:

[0076] Where d is the threshold of the small error interval. It is a symbolic function.

[0077] This tracking differentiator enables smooth tracking of the desired trajectory and its derivative, effectively suppressing high-frequency oscillations caused by measurement noise or numerical differentiation, avoiding chattering of the input signal on the controller, and ensuring the stability and accuracy of the control law.

[0078] In this invention, the main function of the tracking differentiator is to provide the adaptive backstepping controller with continuously differentiable desired joint angles and their derivative signals, and to achieve rapid observation and compensation of disturbances in combination with ESO, thereby further improving the trajectory tracking performance.

[0079] Step 5: Design an adaptive backstepping control law An adaptive controller is designed based on the backstepping method, introducing tracking error. ( Using the reference trajectory and virtual control variables, the control input u is derived step by step:

[0080] in This is an estimate of the reciprocal of the control gain. Radial basis function vector It is a positive gain constant. This is a virtual control signal.

[0081] Design an adaptive law update based on projection operators. and To ensure that the parameter estimates are bounded:

[0082] in , For adaptive parameters, the projection operator is constrained. Within the physically feasible range.

[0083] Step 6: Perform trajectory planning To achieve precise movement of the end effector of the rock drilling robot along a predetermined hole trajectory, a forward kinematics model is established, the spatial coordinates of the hole position are obtained, and the inverse kinematics equations are solved. Based on Figure 5 The hole layout diagram was used, and hole positions 1 and 2 were selected as verification points for the positioning accuracy control of the robotic arm.

[0084] Hole position 1:

[0085] Hole position 2:

[0086] To ensure trajectory continuity, the drill bit is planned to move along a straight interpolation trajectory from hole position 1 to hole position 2, with an interpolation time of 15 seconds. The desired trajectory of the end position over time is defined as follows:

[0087] Where: T=15s Based on the robot arm's DH parameters and the forward kinematics model, the end-effector position can be obtained as a function of joint variables:

[0088] To determine the angular trajectory of link 1 (joint 1), the inverse kinematics solution of the above equations is required. Taking the first three joints of the rock drilling robot arm primarily for coarse end-effector positioning (the latter four joints for fine-tuning attitude and redundancy allocation), the angle of joint 1 (horizontal rotation of the base) is uniquely determined by the projection of the desired end-effector position onto the horizontal plane (XY plane):

[0089] Substituting the coordinates of hole position 1 and hole position 2, we get:

[0090]

[0091] Therefore, the ideal trajectory of the first joint is:

[0092] Therefore, at the initial moment:

[0093] At the end:

[0094] Therefore, the angle of connecting rod one (base) smoothly changes from 42° to 55° to achieve a straight trajectory for the drill bit to move from hole position 1 to hole position 2.

[0095] Step 7: Conduct simulation experiments The effectiveness of the proposed adaptive backstepping control method based on joint estimation and compensation of internal and external disturbances using RBF-NN and ESO was verified. A complete Simulink simulation model was constructed, including the dynamics of the rock drilling robot arm, trajectory planning, coupling of internal and external disturbances, and the controller. Comparative experiments were conducted with traditional PID control and single ADRC control methods.

[0096] Step 7.1: Set simulation conditions Taking joint 1 of the rock drilling robot arm as an example, the simplified dynamic equation derived in step 2 of this paper is used, and the main diagonal terms of the mass matrix are... Based on the actual CAD quality attributes and the parallel axis theorem, the following calculations are taken: This serves as the dynamic model; The drill bit end effector moves along a straight line from hole position 1 (1500mm, 1350mm, 1500mm) to hole position 2 (800mm, 1145mm, 2000mm), with a trajectory interpolation time of 15 seconds. The horizontal rotation angle of the base joint changes by approximately 42° to 55°, which is used as the target trajectory. In the simulation, typical rock drilling operation external disturbances (periodic impact + random disturbance) are superimposed to simulate the rock strata reaction force; the internal nonlinear terms are represented by an inaccurate known model superimposed with random offsets, which serve as the external disturbances. Compare the performance of the three control strategies under the same trajectory input and the same disturbance conditions: Traditional PID control (fixed gain, tuned for simplified linear models); ADRC with a single ESO (does not distinguish between internal and external disturbances, uses a unified extended state observer for compensation); the RBF-NN_ADRC control method with joint compensation for internal and external disturbances as proposed in this invention.

[0097] Step 7.2: Simulation Results and Comparison like Figure 6 As shown, under the same trajectory input, using the RBFNN_ADRC joint adaptive control method described in this invention, the actual trajectory (solid line) of the joint is highly consistent with the desired trajectory (dashed line), the average tracking error is less than 0.3°, and the steady-state error is close to zero. When subjected to high-frequency impacts and random external disturbances, the system can quickly suppress disturbances without significant overshoot and oscillation.

[0098] like Figure 7 As shown, when using traditional PID control, the joint-tracking trajectory exhibits significant overshoot and continuous oscillation, making it impossible to maintain stable and accurate tracking when external shocks or disturbances occur.

[0099] like Figure 8 As shown, when using a single ESO-ADRC control, external disturbances can be partially suppressed, but due to the lack of separation of internal model uncertainties, there is still a steady-state deviation of more than 0.6°, and small oscillations occur under high-frequency shocks.

[0100] Figure 9 The actual and expected trajectories under three different control methods are shown, which can more intuitively demonstrate the control performance of different control methods.

[0101] Table 3 shows the error indices extracted from the simulation results of the three control methods. A quantitative comparison of the performance of traditional PID control, single ESO-ADRC control, and the RBF-NN_ADRC control method of this invention was conducted using specific numerical values ​​(average tracking error, peak error, and steady-state error). The results clearly show that the control method of this invention outperforms the other two methods in all error indices, providing strong data support for demonstrating that the control method of this invention can significantly improve positioning accuracy and stability.

[0102] Table 3

[0103] The simulation comparisons above demonstrate that the adaptive backstepping control method based on joint estimation and compensation of internal and external disturbances proposed in this invention has significant advantages under complex working conditions. Compared to traditional PID and single ESO-ADRC methods, the method of this invention can effectively separate and compensate for internal parameter uncertainties and strong external disturbances, achieving high-precision and stable tracking of the joint angles of the rock drilling robot arm. This method is particularly suitable for complex environments such as high impact, high vibration, and slowly changing parameters in mining rock drilling operations, and can significantly improve drilling positioning accuracy and operational efficiency.

[0104] To further verify the engineering feasibility and practical application effect of the seven-degree-of-freedom rock drilling robot arm positioning control method under complex working conditions proposed in this invention, on-site test verification will be carried out in conjunction with a mining tunnel construction project.

[0105] The plan is to conduct actual drilling operations on a typical drill-and-blast section using a three-arm drilling rig equipped with the aforementioned control method. The field test is planned to include the following steps: 1. Based on the actual construction hole layout diagram, plan multiple sets of drilling positions and inter-hole linear interpolation trajectories to generate work tasks; 2. According to the trajectory planning results, the drilling rig automatically performs drilling operations, while sensors monitor and record in real time the angle of each joint, the position of the end drill bit, and its deviation from the designed hole position; 3. After the test is completed, use a laser rangefinder or 3D scanning equipment to measure the completed borehole and obtain data on the borehole depth, position and orientation. 4. Compare and analyze the measurement results with those of traditional manual operation or operation without the control method of this invention, focusing on comparing hole position error, hole orientation deviation and system stability; 5. Analyze the test data to verify the adaptability, robustness, and improvement effect of the proposed adaptive backstepping control method with joint estimation and compensation of internal and external disturbances in complex geological and strongly disturbed environments.

[0106] This field test is expected to further demonstrate the feasibility and promotional value of the control method of the present invention in actual engineering, and provide reliable data support for its subsequent application in complex operating scenarios such as mine tunnels and underground engineering.

Claims

1. A positioning control method for precise operation of a complex working condition drill jumbo, characterized in that, Comprising the following steps: Step 1: Establish kinematics model; Step 2: Simplify dynamics model; Step 3: Design joint disturbance estimation; Step 4: Design tracking differentiator (TD); Step 5: Design adaptive backstepping control law; Step 6: Trajectory planning; Step 7: Simulation experiment.

2. The positioning control method for precise operation of a complex condition jumbo as claimed in claim 1, characterized in that, Step S2 is specifically: Step 2.1: Establish standard dynamics model of manipulator by Lagrange method; Step 2.2: Establish actual dynamics model of manipulator; Step 2.3: Design controller for each joint independently.

3. The positioning control method for precise operation of a complex condition jumbo as claimed in claim 1, characterized in that, Step S3 is specifically: Step 3.1: Estimate internal uncertainty; Step 3.2: Compensate external disturbance and fitting error.

4. The positioning control method for precise operation of a complex condition jumbo as claimed in claim 1, characterized in that, Step S7 is specifically: Step 7.1: Set simulation conditions; Step 7.2: Simulation results and comparison.

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