Proportional intelligent control method and device for electric power inspection robot under slippage disturbance

Abstract

The invention provides a proportional intelligent control method and device for an electric power inspection robot under slippage disturbance, and relates to the technical field of intelligent electric power inspection robot control, and the method comprises the steps: introducing slippage parameters to construct a kinetic model containing slippage disturbance based on the kinematics principle, wheel parameters and longitudinal slippage characteristics of a wheeled robot; discretizing the model to obtain a discretized kinetic model; determining a trajectory tracking control target (an actual position converges to an expected trajectory along with iteration) and defining an expected inspection trajectory; designing a proportional iterative learning control algorithm to dynamically correct control input; and verifying algorithm convergence characteristics based on a stability theory. According to the invention, the trajectory tracking precision under slippage disturbance can be ensured, and the control robustness is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of control of an intelligent electric power inspection robot, and in particular to a proportional intelligent control method and device for an electric power inspection robot under slip disturbance. Background Art

[0002] Intelligent inspection robots, with their superior mobility, are widely used in modern industry, particularly in power inspection scenarios. Their core goal is to efficiently identify and address system hazards and faults, preventing major incidents such as power outages caused by equipment problems. Compared to manual inspections, intelligent power inspection robots can meet the daily needs of power grid inspections, enabling intelligent, scheduled inspections around the clock, significantly improving the efficiency and safety of inspections.

[0003] Most current intelligent power inspection robots are wheeled, and their accuracy along a given trajectory is a key indicator of inspection task completion. Early technical solutions for wheeled robot inspection control primarily relied on Proportional-Integral-Derivative (PID) control. By coordinating the proportional, integral, and derivative control elements, this approach ensures both tracking accuracy and robot stability. This approach offers the advantages of a simple structure and ease of engineering implementation. It also performs reliably in ideal inspection environments, such as indoor environments, laying a foundation for the advancement of power grid security.

[0004] However, as power plants continue to expand, the working environment of inspection robots is becoming increasingly complex. Slippery roads, muddy areas, and curves are common, leading to problems such as longitudinal slippage and even rollover accidents. Unfortunately, most existing research on wheeled robot control relies solely on the idealized assumption of "pure rolling without slip." This assumption requires ideal ground hardness, friction coefficient, robot speed, and acceleration, completely ignoring the inevitable slip disturbances in real applications. This significantly reduces the control performance of traditional PID control algorithms in slip-prone scenarios, making it difficult to meet actual inspection needs.

[0005] Specifically, the existing wheeled robot inspection trajectory tracking control technology has the following key defects:

[0006] 1. The ideal assumptions are out of touch with the actual environment: Although the control strategies proposed in existing studies (such as the speed-based global progressive stability control strategy, virtual speed control strategy, and sliding mode control strategy) can achieve good trajectory tracking effects in an ideal "pure rolling, no-slip" environment, in actual inspection scenarios, wet, muddy, and turning conditions will inevitably cause slippage, resulting in a serious decrease in the robot's trajectory tracking accuracy, or even loss of control, making it impossible to complete the inspection task according to the predetermined trajectory.

[0007] 2. Dependence on precise models and insufficient robustness: Existing control methods mostly rely on precise dynamic models of robotic systems. However, in actual applications, the dynamic behavior of robotic systems is highly nonlinear and strongly coupled, and the model parameters change dynamically with the environment, making accurate modeling difficult. As a result, existing control methods are insufficiently robust in dynamic environments and cannot adapt to complex inspection conditions.

[0008] 3. Alternative solutions have significant limitations: To address the slip disturbance problem, some studies have attempted to use adaptive control, fuzzy control, neural networks and other methods for optimization, but these solutions still have obvious shortcomings: adaptive control requires the system structure to be known in advance and has poor adaptability to unknown or dynamically changing systems; fuzzy control has difficulty balancing stability and robustness, and the control effect is easily affected by rule design; neural networks require a large amount of computing resources and training data support, and are difficult to deploy efficiently on inspection robots with limited resources, limiting their practical application value.

[0009] Judging from existing patented technologies, relevant solutions have also failed to effectively break through the above bottlenecks:

[0010] Patent publication number CN116719320A proposes a wheeled robot trajectory tracking method based on PID control. Although it explains the influence of PID parameters on trajectory tracking performance, it has strict requirements on the robot structure and does not consider the impact of slip disturbance. It is only applicable to ideal working environments such as indoors. It cannot cope with the longitudinal slip problem that is common in power inspection scenarios, and it is difficult to ensure the robot's driving accuracy along the inspection trajectory.

[0011] Patent publication number CN116166013A proposes a virtual reference trajectory tracking control method that combines disturbance observation and trajectory reconstruction. This method uses a tracking differentiator to estimate the sliding disturbance and superimposes it on the original reference trajectory, achieving trajectory-level disturbance precompensation. However, this method relies on real-time disturbance observation, requires manual adjustment of the controller gain, and lacks self-learning capabilities. Long-term changes in the sliding disturbance characteristics require parameter recalibration, limiting its applicability in power inspection scenarios subject to repetitive disturbances and long-term self-adaptation.

[0012] In summary, the existing intelligent power inspection robot control technology cannot effectively compensate for the impact of longitudinal slip disturbances, and it is difficult to ensure the robot's stability and trajectory tracking accuracy in complex dynamic environments. How to solve this problem has become a key technical bottleneck that urgently needs to be broken through in the field of intelligent power inspection. Summary of the Invention

[0013] To this end, an embodiment of the present invention provides a proportional intelligent control method and device for an electric power inspection robot under slip disturbance, which is used to solve problems such as trajectory deviation and insufficient control robustness caused by longitudinal slip of wheeled robots in intelligent electric power inspection scenarios.

[0014] In order to solve the above problems, an embodiment of the present invention provides a proportional intelligent control method for a power inspection robot under slip disturbance, the method comprising: Based on the kinematic principles of wheeled robots, combined with preset wheel parameters and longitudinal slip characteristics, a dynamic model of wheeled robots with slip disturbance is constructed by introducing a slip parameter that reflects the difference between the expected and actual speeds of the robot wheels. Based on a preset sampling period, the dynamic model of the wheeled robot with slip disturbance is discretized to obtain a discretized dynamic model; According to the power inspection task requirements, the robot trajectory tracking control target is determined, and the expected inspection trajectory that meets the control target is defined. The control target is to make the actual position of the robot converge to the expected inspection trajectory with the number of iterations; Based on the discretized dynamic model and the desired inspection trajectory, a proportional iterative learning control algorithm is designed, wherein the proportional iterative learning control algorithm is used to dynamically correct the control input based on the robot trajectory tracking error; Based on the preset stability theory, the inspection trajectory tracking performance of the proportional iterative learning control algorithm is analyzed to verify the convergence characteristics of the robot's actual position with the number of iterations to ensure the trajectory tracking accuracy under slip disturbance.

[0015] Preferably, the step of constructing a dynamic model of a wheeled robot with slippage interference specifically includes: The slip parameter is defined as a function of time. The slip parameter relates the actual speed of the left and right wheels of the robot to the desired speed and satisfies the following conditions: when the slip parameter is 1, the robot is in a non-slip state; when the slip parameter is greater than 1, the actual speed of the robot is less than the desired speed, and the larger the slip parameter, the higher the degree of slip. In combination with the slip parameters, wheel spacing and wheel radius, a mapping relationship between the robot's linear velocity, angular velocity and control input is established, and then the dynamic model of the wheeled robot with slip interference is constructed.

[0016] Preferably, the dynamic model of the wheeled robot with slip interference is: ; Where, for The time derivative of the pose at the moment; is the robot wheel radius; is the slip parameter; Half the wheel spacing; For robots The heading angle at the moment; and is the left and right wheel speed.

[0017] Preferably, the discretization processing of the dynamic model of the wheeled robot with slip interference is specifically carried out by adopting a processing method based on a preset numerical discretization rule, and the numerical discretization rule includes the Euler discretization rule; The discretized dynamic model obtained after the discretization process represents the mapping relationship between the robot state quantity and the control input in the form of a system matrix and an input matrix.

[0018] Preferably, the discretized dynamics model is: ; Where, 、 The robots are Moment and The position at the moment; is the step size coefficient, where is the robot wheel radius, is the slip parameter; is the system matrix, where is the sampling time, Half the wheel spacing, For robots The heading angle at the moment; For The control input at time and is the left and right wheel speed.

[0019] Preferably, the robot trajectory tracking control target is: ; Where, For the The actual position of the iteration; The expected inspection trajectory; When the number of iterations The limit as it approaches infinity; The expected inspection trajectory is: ; Where, for Expected inspection trajectory at all times; is the step length coefficient; is the system matrix; is the desired control input.

[0020] Preferably, the control law of the proportional iterative learning control algorithm is: ; Where, 、 Respectively 、 Iteration The actual control input at the moment; 、 are all gain matrices; For the The iteration Tracking error at each moment; For the The iteration Tracking error at any moment.

[0021] Preferably, the gain matrix satisfy: ; Where, is the identity matrix; is the step length coefficient; is the system matrix; is the norm operator; is the convergence rate constant and satisfies ; The system matrix Actual location Satisfies the Lipschitz condition, that is, there exists a constant Make ,in 、 are all system matrices, and The robot is at any position during the first and second tracking processes respectively. The actual location at the moment.

[0022] An embodiment of the present invention further provides a proportional intelligent control device for a power inspection robot under slip disturbance, which is used to implement the above-mentioned proportional intelligent control method for a power inspection robot under slip disturbance, specifically comprising: The model building module is used to construct a wheeled robot dynamic model with slip disturbance based on the kinematic principles of the wheeled robot, combined with preset wheel parameters and longitudinal slip characteristics, by introducing a slip parameter that reflects the difference between the expected speed and the actual speed of the robot wheel; A discretization module is used to discretize the dynamic model of the wheeled robot with slip interference based on a preset sampling period to obtain a discretized dynamic model; The trajectory definition module is used to determine the robot trajectory tracking control target according to the power inspection task requirements and define the expected inspection trajectory that meets the control target. The control target is to make the actual position of the robot converge to the expected inspection trajectory with the number of iterations; A control algorithm design module is used to design a proportional iterative learning control algorithm based on the discretized dynamic model and the expected inspection trajectory, wherein the proportional iterative learning control algorithm is used to dynamically correct the control input based on the robot trajectory tracking error; The performance analysis module is used to analyze the inspection trajectory tracking performance of the proportional iterative learning control algorithm based on a preset stability theory, verify the convergence characteristics of the robot's actual position with the number of iterations, and ensure the trajectory tracking accuracy under slip disturbances.

[0023] An embodiment of the present invention further provides a power inspection robot, comprising the above-mentioned proportional intelligent control device for the power inspection robot under slip disturbance.

[0024] It can be seen from the above technical solutions that the present invention has the following beneficial effects: (1) Existing technologies often design control strategies based on the ideal scenario of "pure rolling without slip," completely ignoring longitudinal slip in wet and muddy conditions during power inspections, resulting in a sharp drop in trajectory tracking accuracy. This invention introduces slip parameters and combines the kinematic principles of wheeled robots with wheel parameters to construct a dynamic model with slip disturbance. This model quantitatively incorporates slip disturbance into the robot's motion equations, making the model highly compatible with the complex actual environment of power inspections, and fundamentally resolving the technical pain point of the disconnect between ideal assumptions and reality.

[0025] (2) Existing control methods rely on accurate nonlinear and strongly coupled dynamic models of the robot system and have poor adaptability to dynamic changes in parameters. The proportional iterative learning control (algorithm designed in this paper) is a control law that only dynamically corrects the control input based on the previous iteration tracking error and the current iteration error, without knowing the precise dynamic parameters of the system; at the same time, through the constraint gain matrix satisfy , and using the system matrix The Lipschitz characteristic ensures stable control even in scenarios with time-varying slip parameters and dynamic environmental changes. This feature significantly improves the robot's robustness in complex power inspection conditions, avoiding loss of control due to model inaccuracies.

[0026] (3) The present invention defines the control objective as the actual position converges to the desired trajectory when the number of iterations tends to infinity, and verifies the convergence through the Lyapunov stability theory: as the number of iterations increases, the input error and position error both tend to 0. The simulation results further confirm that: under fixed slip, the tracking error is close to 0 after 1500 iterations; under time-varying slip, 1000 iterations can achieve high-precision fitting of the actual trajectory and the desired trajectory; while under the same slip scenario, the traditional PID control has serious trajectory deviation and the error cannot converge. This iterative convergence characteristic perfectly solves the core problem of trajectory deviation caused by slip disturbance, meeting the stringent requirements of power inspection on trajectory accuracy.

[0027] (4) Compared with alternatives such as fuzzy control and neural networks, the P-ILC algorithm of the present invention has a simple structure (containing only a gain matrix and an error term) and does not require complex computational units. The discretization model adopts the Euler discretization rule, and the sampling period is easy to implement, which can adapt to the limited hardware resources of power inspection robots. At the same time, the algorithm is effective for different slip scenarios such as fixed slip and time-varying slip, and does not require manual parameter adjustment, which greatly reduces the difficulty and cost of engineering deployment. It can be widely applied to various types of wheeled power inspection robots, solving the problem of limited practicality of alternative solutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the implementation cases of the present invention or the technical solutions in the prior art, the following briefly describes the drawings required for use in the embodiments. By referring to the drawings, the features and advantages of the present invention will be more clearly understood. The drawings are for illustration only and should not be construed as limiting the present invention in any way. Those skilled in the art can derive other drawings based on these drawings without inventive effort. Among them: Figure 1 This is a flow chart of a proportional intelligent control method for an electric power inspection robot under slip disturbance provided by the present invention; Figure 2 For the present invention The trajectory tracking effect of the wheeled robot after 300 iterations; Figure 3 For the present invention The trajectory tracking effect of the wheeled robot after 500 iterations; Figure 4 For the present invention The trajectory tracking effect of the wheeled robot after 1000 iterations; Figure 5 For the present invention Tracking error graph of the wheeled robot after 1000 iterations; Figure 6 For the present invention The trajectory tracking effect of the wheeled robot after 1500 iterations; Figure 7 For the present invention The trajectory tracking effect of the wheeled robot after 300 iterations; Figure 8 For the present invention The trajectory tracking effect of the wheeled robot after 500 iterations; Figure 9 For the present invention The trajectory tracking effect of the wheeled robot after 1000 iterations; Figure 10 For the present invention Tracking error graph of the wheeled robot after 1000 iterations; Figure 11 For the present invention The trajectory tracking effect diagram of the wheeled robot controlled by PID; Figure 12 For the present invention Tracking error diagram of the wheeled robot controlled by PID; Figure 13 This is a block diagram of a proportional intelligent control device for an electric power inspection robot under slip disturbance provided by the present invention. DETAILED DESCRIPTION

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0030] Example 1: In order to solve the problems of trajectory deviation and insufficient control robustness caused by longitudinal slip of wheeled robots in intelligent power inspection scenarios, Figure 1 As shown, the present invention proposes a proportional intelligent control method for a power inspection robot under slip disturbance, the method comprising: S1: Based on the kinematic principles of wheeled robots, combined with preset wheel parameters and longitudinal slip characteristics, a dynamic model of wheeled robots with slip disturbance is constructed by introducing a slip parameter that reflects the difference between the desired and actual wheel speeds of the robot. S2: Based on a preset sampling period, the dynamic model of the wheeled robot with slip disturbance is discretized to obtain a discretized dynamic model; S3: Determine the robot trajectory tracking control target based on the power inspection task requirements and define the expected inspection trajectory that meets the control target. The control goal is to make the robot's actual position converge to the expected inspection trajectory as the number of iterations increases. S4: Based on the discretized dynamic model and the desired inspection trajectory, a proportional iterative learning control algorithm is designed. The proportional iterative learning control algorithm is used to dynamically correct the control input based on the robot's trajectory tracking error. S5: Based on the preset stability theory, the inspection trajectory tracking performance of the proportional iterative learning control algorithm is analyzed to verify the convergence characteristics of the robot's actual position with the number of iterations to ensure the trajectory tracking accuracy under slip disturbances.

[0031] From the above technical solution, it can be seen that the present invention proposes a proportional intelligent control method for an electric power inspection robot under slip disturbance. Based on the kinematic principle of the wheeled robot, preset wheel parameters and longitudinal slip characteristics, a slip parameter reflecting the difference between the expected and actual wheel speeds is introduced to construct a dynamic model with slip interference, breaking through the ideal assumption of "pure rolling without slip" and making the model fit the actual inspection slip scenario; based on the preset sampling period, the dynamic model with slip interference is discretized to obtain a discretized dynamic model, and the continuous model is converted into a discrete form suitable for engineering deployment; according to the requirements of the electric power inspection task, the robot trajectory tracking control target is determined (so that the actual position converges to the expected trajectory with the number of iterations) and the expected inspection trajectory is defined, the control direction is clarified, and a target basis is provided for subsequent algorithm design; A proportional iterative learning control algorithm is designed based on the discretized dynamic model and the expected inspection trajectory. The algorithm can dynamically correct the control input based on the trajectory tracking error, reduce the dependence on the precise model, and improve the robustness in dynamic environments. The inspection trajectory tracking performance of the proportional iterative learning control algorithm is analyzed based on the preset stability theory, and the convergence characteristics of the actual position with the number of iterations are verified to ensure the trajectory tracking accuracy under slip disturbance.

[0032] In step S1, based on the kinematic principle of the wheeled robot, combined with the preset wheel parameters and longitudinal slip characteristics, a wheeled robot dynamic model with slip interference is constructed by introducing a slip parameter that reflects the difference between the expected speed and the actual speed of the robot wheel.

[0033] Specifically, we first establish a kinematic model of a two-wheeled robot for a wheeled robot: (1) Where, for The time derivative of the posture at the moment corresponds to the instantaneous velocity of the robot, where is the linear velocity of the robot in the X-axis direction in the Cartesian coordinate system, is the linear velocity of the robot in the Y-axis direction in the Cartesian coordinate system, is the angular velocity of the robot; For robots The heading angle at the moment; is the linear velocity of the robot; is the angular velocity of the robot, and satisfies the following relationship: .

[0034] Adding longitudinal slip on this basis still has , because the lateral direction is not affected at this time. In order to describe the longitudinal slip in detail, the parameter , ,two The value corresponds to the left and right wheel speed, is the expected speed, is the actual speed. According to the law of conservation of energy, under the premise of slip loss, the expected speed must be greater than the actual speed. It can intuitively show the slippage degree of the wheeled robot. When , it corresponds to the no-slip state. The larger the value, the greater the actual speed loss and the more serious the car slips. Next, we will analyze the kinematic model of the wheeled robot with longitudinal slip. First, we define the slip parameter It's about time Function , the left and right wheel speeds are , , then the relationship between linear velocity, angular velocity and left and right wheel speeds can be obtained as: (2) Then, combined with the no-slip model, the kinematic model of the wheeled robot dynamics model with slip interference can be obtained: (3) Where, for The time derivative of the pose at the moment; is the robot wheel radius; is the slip parameter; Half the wheel spacing; For robots The heading angle at the moment; and is the left and right wheel speed.

[0035] In step S2, based on a preset sampling period, the dynamic model of the wheeled robot with slip interference is discretized. Specifically, a processing method based on preset numerical discretization rules is adopted, and the numerical discretization rules include the Euler discretization rule. The discretized dynamic model obtained after the discretization processing represents the mapping relationship between the robot state quantity and the control input in the form of a system matrix and an input matrix.

[0036] Specifically, define the sampling time , using Euler method we can get: (4) After simplification, we can get: (5) Where, 、 The robots are Moment and The position at the moment; is the step length coefficient; is the system matrix, where is the sampling time, Half the wheel spacing; For Control input at any moment.

[0037] In step S3, according to the power inspection task requirements, the robot trajectory tracking control target is determined, and the expected inspection trajectory that meets the control target is defined. The control target is to make the actual position of the robot converge to the expected inspection trajectory with the number of iterations.

[0038] Specifically, for the trajectory tracking problem of the robot system, the most important task is to achieve high-precision fitting of the final inspection trajectory and the expected inspection trajectory to solve the impact of slip on the system. The robot inspection process is a repetitive motion process. In order to gradually improve the inspection trajectory tracking accuracy during the repeated operation, in step S3, by defining the number of iterations , combined with step S2, we can get the robot's first The trajectory of this time is: (6) Where, For the The actual position of the iteration; When the number of iterations The limit as it approaches infinity.

[0039] make To achieve the desired inspection trajectory, the ultimate goal of the system is to find a suitable So that under the given time conditions the final , that is, the control target is: , so assuming there is a desired input control Ability to achieve a given expected inspection trajectory have: (7) in, for Expected inspection trajectory at all times; is the system matrix.

[0040] In step S4, a proportional iterative learning control algorithm is designed based on the discretized dynamic model and the desired inspection trajectory. The proportional iterative learning control (P-ILC) algorithm is used to dynamically correct the control input based on the robot trajectory tracking error.

[0041] Specifically, the control law of the proportional iterative learning control algorithm is: (8) Where, 、 Respectively 、 Iteration The actual control input at the moment; 、 are gain matrices, they are all bounded and are no greater than , ; For the The iteration Tracking error at each moment; For the The iteration Tracking error at any moment. Corresponding to The ILC algorithm adopted by the robot system can achieve high-precision tracking of the inspection trajectory by only using the tracking error of the robot's previous inspection and the current tracking error, that is, it only relies on the robot's actual position information and expected position information and does not rely on the robot model to modify the control input by itself.

[0042] In step S5, based on the Lyapunov stability theory, the inspection trajectory tracking performance of the proportional iterative learning control algorithm is analyzed to verify the convergence characteristics of the robot's actual position with the number of iterations to ensure the trajectory tracking accuracy under slip disturbance.

[0043] Specifically, first define The input error of the iteration is , combined with the control law designed by equation (7) in step S4, we can obtain: (9) Among them, , , according to equations (6) and (7) in step S3: (10) Combining the above formula (9) and formula (10) we can get: (11) Therefore, taking the norms of equations (11) and (10) respectively, we can obtain: (12) (13) ( is a bounded parameter, thus avoiding complete slip and loss of control, so there is At the same time, enter Boundedness is the premise of controllable system, so there is In addition, the robot operation process is a non-mutation continuous operation process, so the parameter right Satisfies the Lipschitz condition, that is, there exists a constant Make In the present invention, the control gain is designed to meet the following conditions to ensure the safety and reliability of the inspection process: ,in is the identity matrix; is the step length coefficient; is the system matrix; is the norm operator; is the convergence rate constant and satisfies .

[0044] Therefore, Substituting the above conditions into equations (12) and (13) yields: (14) (15) definition , assuming that the initial tracking state satisfies , substituting into formula (15) we can get: (16) Substituting formula (16) into formula (14), we have: (17) Furthermore, define , then formula (17) can be expressed as: (18) Multiply both sides of formula (18) by , then we can get: (19) set up , take the normed inequality as: (20) The formula can be simplified to: (twenty one) in, .

[0045] Formula (21) can be further simplified as: (twenty two) when When it is big enough, , then taking the limit of formula (22) yields: (twenty three) Similarly, take equation (16) The norm can be obtained: (twenty four) Finally, combining equation (23), taking the limit of equation (24) yields: (25) The above process shows that under given gain conditions, the actual position of the robot system As the number of task executions The increase of gradually converges to the desired reference trajectory. This shows that in the presence of slip, the ILC algorithm designed by the present invention can still ensure the reliability of robot inspection, and the slip parameter Changing the specific value does not affect the final result.

[0046] To further illustrate the advantages of the method of the present invention, a simulation experiment was designed and conducted. The following are the specific steps of the simulation experiment.

[0047] S1: Establish a dynamic model of a wheeled robot with slip disturbance: (26) Wheel spacing is , the wheel radius is , let the introduced slip parameter , the corresponding slip parameter in the time-varying case is set as .

[0048] S2: Discretize the dynamic model of step S1: The simplified discrete kinematic model is as follows: (27) in, , is the system matrix, is the control input of the system, the sampling time , the initial robot position is selected as .

[0049] S3: Determine the robot control target and define the expected inspection trajectory : The expected inspection trajectory is set to .in, , , .

[0050] S4: Design a model-free proportional iterative learning control algorithm: (28) Set the gain matrix to , , Corresponding to The state error of the iteration.

[0051] S5: Analyze inspection trajectory tracking performance: First define the The input error of the next iteration is have: (29) Among them , have: (30) Combining the above formula (29) and formula (30) we can get: (31) Therefore, taking the norms of equations (31) and (30) respectively, we can obtain: (32) (33) Among them, , , , .Will Substituting the above conditions into equations (32) and (33) yields: (34) (35) definition , tracking the initial state satisfies , substituting into formula (35) we can get: (36)

[0052] Substituting formula (36) into formula (34), we have: (37) definition , then formula (37) can be expressed as: (38) Multiply both sides of formula (38) by , then we can get: (39) set up , take the normed inequality as: (40) The formula can be simplified to: (41) in .

[0053] Formula (41) can be further simplified: (42) when When it is big enough, , then taking the limit of formula (42) yields: (43) Similarly, take equation (36) The norm can be obtained: (44) Finally, combining equation (43), taking the limit of equation (44) yields: (45) The above process shows the actual position of the wheeled inspection robot system under given gain conditions. As the number of task executions The increase of gradually converges to the desired reference trajectory.

[0054] The following simulations are performed using MATLAB to verify the performance of the method of the present invention in fixed slip disturbance and time-varying slip disturbance scenarios, and compared with the PID control method.

[0055] Figures 2 to 6 The inspection trajectory tracking effect and tracking error variation of the wheeled robot under fixed slip parameters are shown. As can be seen, the inspection trajectory tracking error gradually decreases with increasing task execution times. After 500 executions, the robot's actual trajectory is close to the expected inspection trajectory. Furthermore, the tracking error decreases rapidly as the task execution times (i.e., the number of iterations) increase. Achieving a high-precision fit between the actual and expected trajectories requires more iterations. This demonstrates that slippage can affect the performance of the actual system and complicate the original task.

[0056] Figures 7 to 10 The inspection trajectory tracking effect and tracking error variation of the wheeled robot under time-varying slip parameters are shown. , we can find that: when When the slip parameter is continuously expanded from 2 to 4.5, the results are similar to those of the fixed slip parameter. Compared with the time-varying method, the actual trajectory deviates more from the expected inspection trajectory with the same number of iterations, that is, the error is larger. However, with a sufficient number of iterations, the tracking error can be reduced to a smaller range. This shows that whether it is fixed longitudinal slip or time-varying longitudinal slip, in a wheeled robot system with longitudinal slip, the P-ILC algorithm proposed in this paper has the ability to adapt to the slip amplitude and change frequency, and can effectively compensate for the impact of slip on the inspection trajectory tracking performance, thereby enabling the robot to track the inspection trajectory with high precision.

[0057] Figure 11 and Figure 12 The performance comparison between the proposed method and the PID control method is presented. The system model and initial parameters remain unchanged, and only the iterative control algorithm is replaced with PID control, with P = 1.2, I = 0.05, and D = 0.01. The final results show that under this slip, even with the enhanced PID control algorithm, PID control is unlikely to be effective. The deviation from the actual results is significant, and the effect of optimizing the PID control algorithm itself becomes very weak. In this case, using the ILC algorithm and increasing the number of iterations is a better approach.

[0058] Example 2: Figure 13 As shown, the present invention provides a proportional intelligent control device for a power inspection robot under slip disturbance, which is used to implement the proportional intelligent control method for a power inspection robot under slip disturbance of the above embodiment 1, specifically comprising: The model building module 100 is used to build a wheeled robot dynamic model with slip disturbance based on the kinematic principles of the wheeled robot, combined with preset wheel parameters and longitudinal slip characteristics, by introducing a slip parameter that reflects the difference between the desired speed and the actual speed of the robot wheel; The discretization module 200 is used to discretize the dynamic model of the wheeled robot with slip disturbance based on a preset sampling period to obtain a discretized dynamic model; The trajectory definition module 300 is used to determine the robot trajectory tracking control target based on the power inspection task requirements and define the expected inspection trajectory that meets the control target. The control target is to make the actual position of the robot converge to the expected inspection trajectory as the number of iterations increases; A control algorithm design module 400 is used to design a proportional iterative learning control algorithm based on a discretized dynamic model and a desired inspection trajectory. The proportional iterative learning control algorithm is used to dynamically correct the control input based on the robot trajectory tracking error. The performance analysis module 500 is used to analyze the inspection trajectory tracking performance of the proportional iterative learning control algorithm based on a preset stability theory, verify the convergence characteristics of the robot's actual position with the number of iterations, and ensure the trajectory tracking accuracy under slip disturbance.

[0059] The present embodiment provides a proportional intelligent control device for an electric power inspection robot under a slip disturbance, which is used to implement the aforementioned proportional intelligent control method for the electric power inspection robot under a slip disturbance. Therefore, the specific implementation methods of the proportional intelligent control device for the electric power inspection robot under a slip disturbance can be seen in the embodiment part of the proportional intelligent control method for the electric power inspection robot under a slip disturbance in the foregoing text. For example, the model building module 100, the discretization module 200, the trajectory definition module 300, the control algorithm design module 400, and the performance analysis module 500 are respectively used to implement steps S1, S2, S3, S4, and S5 in the aforementioned proportional intelligent control method for the electric power inspection robot under a slip disturbance. Therefore, its specific implementation methods can refer to the descriptions of the corresponding embodiments of each part. In order to avoid redundancy, they will not be repeated here.

[0060] Example 3: An embodiment of the present invention provides an electric power inspection robot, including the proportional intelligent control device of the electric power inspection robot under slip disturbance of the above-mentioned Example 2. Its specific implementation method can refer to the description of the corresponding various part embodiments. In order to avoid redundancy, it will not be repeated here.

[0061] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0062] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0063] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0064] Obviously, the above embodiments are merely examples for clarity of explanation and are not intended to limit the implementation methods. Those skilled in the art will appreciate that other variations or modifications can be made based on the above description. It is not necessary and impossible to enumerate all implementation methods here. Obvious variations or modifications arising therefrom remain within the scope of protection of the present invention.

Claims

1. A proportional intelligent control method for an electric power inspection robot under slip disturbance, characterized in that: include: Based on the kinematic principles of wheeled robots, combined with preset wheel parameters and longitudinal slip characteristics, a dynamic model of wheeled robots with slip disturbance is constructed by introducing a slip parameter that reflects the difference between the expected and actual speeds of the robot wheels. Based on a preset sampling period, the dynamic model of the wheeled robot with slip disturbance is discretized to obtain a discretized dynamic model; According to the power inspection task requirements, the robot trajectory tracking control target is determined, and the expected inspection trajectory that meets the control target is defined. The control target is to make the actual position of the robot converge to the expected inspection trajectory with the number of iterations; Based on the discretized dynamic model and the desired inspection trajectory, a proportional iterative learning control algorithm is designed, wherein the proportional iterative learning control algorithm is used to dynamically correct the control input based on the robot trajectory tracking error; Based on the preset stability theory, the inspection trajectory tracking performance of the proportional iterative learning control algorithm is analyzed to verify the convergence characteristics of the robot's actual position with the number of iterations to ensure the trajectory tracking accuracy under slip disturbance.

2. The proportional intelligent control method of the power inspection robot under slip disturbance according to claim 1 is characterized in that: The construction of the wheeled robot dynamics model with slip disturbance specifically includes: The slip parameter is defined as a function of time. The slip parameter relates the actual speed of the left and right wheels of the robot to the desired speed and satisfies the following conditions: when the slip parameter is 1, the robot is in a non-slip state; when the slip parameter is greater than 1, the actual speed of the robot is less than the desired speed, and the larger the slip parameter, the higher the degree of slip. In combination with the slip parameters, wheel spacing and wheel radius, a mapping relationship between the robot's linear velocity, angular velocity and control input is established, and then the dynamic model of the wheeled robot with slip interference is constructed.

3. The proportional intelligent control method of the electric power inspection robot under slip disturbance according to claim 1 or 2, characterized in that: The dynamic model of the wheeled robot with slip disturbance is: ； Where, for The time derivative of the pose at the moment; is the robot wheel radius; is the slip parameter; Half the wheel spacing; For robots The heading angle at the moment; and is the left and right wheel speed.

4. The proportional intelligent control method of the power inspection robot under slip disturbance according to claim 1 is characterized in that: The discretization processing of the wheeled robot dynamic model with slip interference is specifically performed using a processing method based on a preset numerical discretization rule, wherein the numerical discretization rule includes an Euler discretization rule; The discretized dynamic model obtained after the discretization process represents the mapping relationship between the robot state quantity and the control input in the form of a system matrix and an input matrix.

5. The proportional intelligent control method of the power inspection robot under slip disturbance according to claim 1 or 4, characterized in that: The discretized dynamic model is: ； Where, 、 The robots are Moment and The position at the moment; is the step size coefficient, where is the robot wheel radius, is the slip parameter; is the system matrix, where is the sampling time, Half the wheel spacing, For robots The heading angle at the moment; For The control input at time and is the left and right wheel speed.

6. The proportional intelligent control method of the power inspection robot under slip disturbance according to claim 1 is characterized in that: The robot trajectory tracking control target is: ； Where, For the The actual position of the iteration; The expected inspection trajectory; When the number of iterations The limit as it approaches infinity; The expected inspection trajectory is: ； Where, for Expected inspection trajectory at all times; is the step length coefficient; is the system matrix; is the desired control input.

7. The proportional intelligent control method for the electric power inspection robot under slip disturbance according to claim 1 is characterized in that: In step S4, the control law of the proportional iterative learning control algorithm is: ； Where, 、 Respectively 、 Iteration The actual control input at the moment; 、 are all gain matrices; For the The iteration Tracking error at each moment; For the The iteration Tracking error at any moment.

8. The proportional intelligent control method for the electric power inspection robot under slip disturbance according to claim 7 is characterized in that: The gain matrix satisfy: ； Where, is the identity matrix; is the step length coefficient; is the system matrix; is the norm operator; is the convergence rate constant and satisfies ; The system matrix Actual location Satisfies the Lipschitz condition, that is, there exists a constant Make ,in 、 are all system matrices, and The robot is at any position during the first and second tracking processes respectively. The actual location at the moment.

9. A proportional intelligent control device for an electric power inspection robot under slip disturbance, characterized in that: The device is used to implement the proportional intelligent control method of the power inspection robot under slip disturbance according to any one of claims 1 to 8, specifically comprising: The model building module is used to construct a wheeled robot dynamic model with slip disturbance based on the kinematic principles of the wheeled robot, combined with preset wheel parameters and longitudinal slip characteristics, by introducing a slip parameter that reflects the difference between the expected speed and the actual speed of the robot wheel; A discretization module is used to discretize the dynamic model of the wheeled robot with slip interference based on a preset sampling period to obtain a discretized dynamic model; The trajectory definition module is used to determine the robot trajectory tracking control target according to the power inspection task requirements and define the expected inspection trajectory that meets the control target. The control target is to make the actual position of the robot converge to the expected inspection trajectory with the number of iterations; A control algorithm design module is used to design a proportional iterative learning control algorithm based on the discretized dynamic model and the expected inspection trajectory, wherein the proportional iterative learning control algorithm is used to dynamically correct the control input based on the robot trajectory tracking error; The performance analysis module is used to analyze the inspection trajectory tracking performance of the proportional iterative learning control algorithm based on a preset stability theory, verify the convergence characteristics of the robot's actual position with the number of iterations, and ensure the trajectory tracking accuracy under slip disturbances.

10. A power inspection robot, characterized in that: It includes the proportional intelligent control device of the power inspection robot under slip disturbance as described in claim 9.

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