Mechanical arm specified performance fault-tolerant control method based on fuzzy logic system

Abstract

The invention relates to the technical field of robot control, and discloses a mechanical arm specified performance fault-tolerant control method based on a fuzzy logic system, and the method comprises the steps: building a kinematics and dynamics model and a fault model of an agricultural robot; establishing a relative error dynamic equation between the actual position and the expected position; designing a specified performance function, and constraining the tracking error within an expected range by using the specified performance function; on the basis, designing a virtual control law by using a backstepping method under the condition of meeting error constraints; designing an adaptive control law by adopting a fuzzy logic system based on an uncertain item, unknown environment interference and a fault model in the kinetic model; a specified performance controller is designed based on a virtual control law, fuzzy logic adaptive control and full-state feedback agricultural robot, and specified performance fault-tolerant control of the agricultural robot is realized. The stability proves that the tracking error of the agricultural robot converges to any small neighborhood near zero within fixed time.

Description

Technical Field

[0001] The present invention relates to the technical field of robot control, and more specifically, it relates to a fault-tolerant control method for a robotic arm with specified performance based on a fuzzy logic system. Background Art

[0002] In recent years, with the rapid development of industrial applications, artificial intelligence, and automation technologies, intelligent robot control has received extensive attention. Especially in fields such as manufacturing, aerospace, and healthcare, it is necessary to ensure the high precision and reliability of the system even in the presence of uncertainties and potential system failures. In the practical application of agricultural robots, due to factors such as external disturbances, modeling errors, and system failures, the control system may encounter various challenges, resulting in the robot deviating from the predetermined trajectory or generating large tracking errors, causing damage to surrounding equipment and the environment. Therefore, it is crucial to design a controller with specified performance and fault-tolerant capabilities to ensure the safe operation and high-performance performance of agricultural robots in various uncertain environments.

[0003] To improve the reliability of robot systems, researchers have proposed various fault-tolerant control methods. Some scholars have proposed a finite-time fault-tolerant controller that uses a reinforcement learning strategy to identify system faults and solve the strong nonlinearity and coupling problems of multi-joint manipulators, including actuator faults. Another study designed a fault-tolerant controller based on nonsingular sliding mode control and used a time-delay estimator to estimate system faults, and solved the computational complexity problem through a dynamic surface method. However, the above studies can only achieve finite-time stability, which means that the convergence time of the system depends on the initial state. To improve the convergence performance, some scholars have proposed an adaptive controller with a sliding mode strategy, which reduces the requirement for prior knowledge of system unknown terms. Researchers have proposed an adaptive fixed-time fault-tolerant control method that uses a fuzzy logic system to estimate the fault components.

[0004] However, the common problem in existing studies is that performance constraints are not fully considered. In practical applications, agricultural robots need to meet specific performance requirements, including steady-state and transient performance constraints. Especially in the presence of model uncertainties and system failures, the tracking error may exceed the expected constraint range. Existing work mainly focuses on traditional preset performance methods, and the time when the tracking error reaches the performance constraint boundary and the steady-state tracking accuracy are directly affected by control parameters, which are difficult for designers to determine in advance according to specific task requirements. Although some studies have shown that fault-tolerant control combined with preset performance control has good advantages, in the field of agricultural robots, how to achieve user-defined specified performance control and fault-tolerant control when there are actuator or process faults is an urgent problem to be solved, mainly because transient performance such as the convergence time or overshoot of the system cannot be flexibly set by users. Summary of the Invention

[0005] The present invention provides a fault-tolerant control method for a robotic arm with specified performance based on a fuzzy logic system, which solves the technical problem in the prior art that it is difficult to achieve user-defined specified performance control and fault-tolerant control.

[0006] The present invention provides a fault-tolerant control method for a robotic arm with specified performance based on a fuzzy logic system, comprising the following steps:

[0007] Construct the kinematic, dynamic models and fault model of an agricultural robot;

[0008] Based on the kinematic and dynamic models of the agricultural robot, establish the relative error dynamic equation between the actual position and the desired position;

[0009] Design a specified performance function, and use the specified performance function to constrain the error value generated by tracking the relative error dynamic equation within the desired range;

[0010] Under the condition that the error is constrained, design a virtual control law by using the backstepping method;

[0011] Based on the uncertain terms, unknown environmental disturbances and fault model in the dynamic model, design an adaptive control law by using a fuzzy logic system;

[0012] Design a specified performance controller for the agricultural robot based on the virtual control law, fuzzy logic adaptive control law and full-state feedback, and realize the specified performance fault-tolerant control of the agricultural robot.

[0013] Further, the fault model includes:

[0014] Actuator bias fault and gain fault;

[0015] Among them, the bias fault is manifested as a constant deviation of the actuator output; the gain fault is manifested as a change in the actuator output gain; these two fault models can comprehensively describe the main fault types that the agricultural robot may encounter during actual operation.

[0016] Further, the designed specified performance function is:

[0017] ;

[0018] where represents the maximum allowable tracking error boundary allowed by the system initially, represents the minimum allowable tracking error boundary allowed by the system at steady state, represents the user-adjustable convergence rate parameter, and the specified performance function can flexibly set performance indicators according to actual task requirements.

[0019] Furthermore, the established relative error dynamic equation is as follows:

[0020] ;

[0021] where, e represents the system tracking error vector, q represents the actual position vector of each joint of the agricultural robot, and q d represents the desired position vector to obtain the position deviation in real time and provide a basis for the subsequent controller design.

[0022] Furthermore, the virtual control law designed by the backstepping method is as follows:

[0023] ;

[0024] where, represents the specified performance function 's derivative, represents the conversion error after coupling the tracking error and the specified performance function, represents a positive constant, is the virtual control law, is the derivative of the desired position , and the gravity matrix is a diagonal matrix composed of the elements of the performance function, represents 's inverse matrix, represents another design result representation of the specified performance function, represents the conversion error 's element, is the upper bound value of the specified performance function, is a parameter designed by the user, represents a diagonal matrix.

[0025] Furthermore, the expression of the fuzzy adaptive control law is as follows:

[0026] ;

[0027] where, represents the fuzzy basis function, represents the fuzzy parameter vector composed of fuzzy membership functions, represents a positive constant, represents the speed tracking error.

[0028] Furthermore, the expression of the specified performance controller is as follows:

[0029] ;

[0030] where, represents the mass matrix of the agricultural robot, denote positive constants, , respectively represent the Coriolis centripetal force matrix and the gravity matrix of the agricultural robot, is a diagonal matrix composed of elements of the performance function, is the virtual control law derivative, represents the fuzzy basis function, represents the fuzzy parameter vector composed of fuzzy membership functions, represents the transpose of the matrix;

[0031] The position tracking controller is used to achieve the basic tracking performance; the adaptive compensation controller is used to compensate for system uncertainties;

[0032] The fault-tolerant compensation controller is used to cope with system failures, and the three work together to ensure the overall performance of the system.

[0033] Furthermore, the fuzzy logic adaptive control law adopts the Mamdani-type fuzzy inference adaptive control mechanism, which includes the complete fuzzification, fuzzy inference, and defuzzification processes. Among them, the fuzzy rule base summarizes the system dynamic characteristics based on expert experience, the inference mechanism adopts the maximum-minimum composition method, and the defuzzification adopts the centroid method.

[0034] Furthermore, the input variables of the fuzzy logic adaptive control law include the state error and the error change rate, the output is the compensation control amount, and the fuzzy sets of the input variables all adopt seven triangular membership functions, which respectively represent negative large, negative medium, negative small, zero, positive small, positive medium, and positive large.

[0035] A computer-readable storage medium is used to store computer-readable instructions, which can run a fault-tolerant control method for a specified performance of a robotic arm based on a fuzzy logic system when the computer-readable instructions are read by a computer.

[0036] The beneficial effects of the present invention are as follows: By establishing the kinematic, dynamic, and fault models of the agricultural robot and combining with the specified performance function and fuzzy logic system, the specified performance fault-tolerant control of the agricultural robot is achieved. Compared with the prior art, the present invention has the following advantages: By using the performance constraint function specified in a given time, the steady-state and transient errors of the system can be limited within a certain range, and the settling time and steady-state accuracy can be flexibly defined according to the task requirements; Combining the advantages of the performance function and fault-tolerant control to ensure that all signals of the robot reach fixed-time stability; The convergence time of the system can be calculated without the initial state; Even if a sudden fault occurs in the system, the tracking error can be guaranteed to meet the specified constraints. The adaptive control law designed by the fuzzy logic system can effectively deal with problems such as model uncertainty, external interference, and actuator faults. Finally, the state error of the agricultural robot converges to an arbitrarily small neighborhood near zero within a fixed time, so that the manipulator has a controller with specified performance and fault-tolerant ability to ensure the safe operation and high-performance performance of the agricultural robot in various uncertain environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 FIG. is a schematic flow chart of a method for specified performance fault-tolerant control of a manipulator based on a fuzzy logic system provided in an embodiment of the present invention;

[0038] Figure 2 is a curve graph of the trajectory tracking error of the agricultural robot under system faults. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] Now, the subject matter described herein will be discussed with reference to example embodiments. It should be understood that discussing these embodiments is only to enable those skilled in the art to better understand and thus implement the subject matter described herein. Without departing from the scope of protection of the content of this specification, changes can be made to the functions and arrangements of the elements discussed. Each example can omit, substitute, or add various processes or components as needed. Additionally, the features described in some examples can also be combined in other examples.

[0040] At least one embodiment of the present invention discloses a method for specified performance fault-tolerant control of a manipulator based on a fuzzy logic system, as Figure 1 shown, including:

[0041] Step 1: Construct the kinematic, dynamic models and fault model of the agricultural robot;

[0042] Step 2: Based on the kinematic and dynamic models of the agricultural robot, establish the relative error dynamic equation between the actual position and the desired position;

[0043] Step 3: Design a specified performance function to constrain the error values generated by the relative error dynamic equation within the desired range using the specified performance function;

[0044] Step 4: Design a virtual control law using the backstepping method under the condition that the error is constrained;

[0045] Step 5: Design an adaptive control law using a fuzzy logic system based on the uncertain terms, unknown environmental disturbances, and fault models in the dynamic model;

[0046] Step 6: Design a specified performance controller for the agricultural robot based on the virtual control law, fuzzy logic adaptive control law, and full-state feedback to achieve the specified performance fault-tolerant control of the agricultural robot.

[0047] In this embodiment, Step 1 accurately describes the motion characteristics of the system and possible fault conditions, including actuator bias faults and gain faults, etc., by establishing a complete dynamic model and fault model, providing a theoretical basis for subsequent controller design; improving the accuracy of system modeling, enabling the controller to better adapt to the actual working environment, and enhancing the robustness of the system.

[0048] Step 2 realizes closed-loop control by establishing an error dynamic equation to monitor the deviation between the actual position and the desired position of the system in real time, providing feedback information for the controller; realizing real-time monitoring of the system state, improving the control accuracy, and providing a basis for subsequent performance constraints.

[0049] The performance function in Step 3 constrains the dynamic response process of the system by setting error boundaries and convergence rates, ensuring that the system allows a larger error in the initial stage and maintains high precision in the steady state; guaranteeing the transient performance and steady-state accuracy of the system, improving the control quality, and enabling the system to have predictable dynamic characteristics.

[0050] Step 4 maps the unbounded error to a bounded interval through error transformation and virtual control law design, and designs the control gain based on the Lyapunov stability theory; ensuring the stability of the system, simplifying the controller design process, and improving the convergence performance of the system.

[0051] Step 5 uses a fuzzy logic system to handle the uncertainties and disturbances in the system, constructs a rule base through expert experience, and realizes intelligent adaptive control; enhancing the adaptability of the system to external disturbances and model uncertainties, and improving the robustness of the control system.

[0052] Step 6 adopts a multi-level control structure, combines fuzzy logic and adaptive control, and realizes the precise control and fault compensation of the system through the coordinated action of the position tracking controller, adaptive compensation controller, and fault-tolerant compensation controller; improving the control accuracy and reliability of the system, and realizing the continuous and stable operation of the agricultural robot under fault conditions.

[0053] Example 1: Designated performance fault tolerance control for agricultural picking robots.

[0054] The process of establishing the kinematic, dynamic models and fault model of the agricultural robot is as follows:

[0055] ;

[0056] Among them, respectively represent the 1st, 2nd, …, nth positions of the agricultural robot, and respectively represent the velocity and acceleration, represents the inertia matrix, and respectively represent the Coriolis centripetal force matrix and the gravity matrix, represents the normal output torque, represents the unknown friction force, is the nonlinear term caused by external disturbances and model uncertainties, represents the faulty unit, represents the time when the fault occurs, represents the transpose of the matrix.

[0057] The said fault model includes:

[0058] Actuator bias fault and gain fault;

[0059] Among them, the bias fault is manifested as a constant deviation in the actuator output; the gain fault is manifested as a change in the actuator output gain; these two fault models can comprehensively describe the main fault types that the agricultural robot may encounter during actual operation.

[0060] The fault model includes: Actuator bias fault: There is a constant deviation in the output, that is ; Actuator gain fault: The output gain changes by γ, that is , where, these two fault models cover the most common fault types in practical applications, where, represents the output torque during the fault, represents the normal output torque.

[0061] By establishing a complete dynamic model and fault model, the motion characteristics of the system and the possible fault conditions can be accurately described, providing a theoretical basis for the subsequent controller design; improving the accuracy of system modeling and enabling the controller to better adapt to the actual working environment.

[0062] The designed designated performance function is:

[0063] ;

[0064] Among them, represents the maximum tracking error boundary initially allowed by the system, represents the minimum tracking error boundary allowed at the steady state of the system, represents the adjustable convergence rate parameter for the user, specifying the performance function Flexibly set the performance index according to the actual task requirements.

[0065] The design of the specified performance function also includes: ;

[0066] Among them, represents the desired settling time, is the steady-state value of is a constant and is determined by the following equation:

[0067] ;

[0068] Through this function, the tracking error can be constrained within a certain range, that is:

[0069]

[0070] Among them, , are the first and second parameters designed respectively, represents the tracking error of the i-th degree of freedom and follows the following principle: when , when , .

[0071] The established relative error dynamic equation is:

[0072] ;

[0073] Among them, e represents the system tracking error vector, q represents the actual position vector of each joint of the agricultural robot, qd represents the desired position vector, to obtain the position deviation in real time and provide a basis for the subsequent controller design.

[0074] Adopt a multi-level control structure, combine fuzzy logic and adaptive control, realize the precise control and fault compensation of the system, and improve the control accuracy and robustness of the system

[0075] By establishing the error dynamic equation, the deviation between the actual position and the desired position of the system is monitored in real time, providing feedback information for the controller, thereby realizing the real-time monitoring of the system state and improving the control accuracy.

[0076] The virtual control law designed using the backstepping method is as follows:

[0077] ;

[0078] Among them, represents the derivative of the specified performance function , represents the conversion error after coupling the tracking error and the specified performance function, represents a positive constant, is the virtual control law, is the derivative of the desired position , and the gravity matrix is a diagonal matrix composed of the elements of the performance function, represents 's inverse matrix, represents another design result representation of the specified performance function, represents the conversion error 's element, is the upper bound value of the specified performance function, is a parameter designed by the user, represents a diagonal matrix.

[0079] The performance function constrains the dynamic response process of the system by setting the error boundary and the convergence rate; thus ensuring the transient performance and steady-state accuracy of the system and improving the control quality.

[0080] Based on the uncertain terms, unknown environmental disturbances and fault models in the dynamic model, the adaptive control law is designed by using a fuzzy logic system as follows:

[0081] ;

[0082] Among them, represents the fuzzy basis function, represents the fuzzy parameter vector composed of fuzzy membership functions, represents a positive constant, represents the speed tracking error.

[0083] The specified performance fault-tolerant controller designed for the agricultural robot based on the virtual control law, fuzzy logic adaptive control law and full-state feedback is as follows:

[0084] ;

[0085] Among them, represents the mass matrix of the agricultural robot, represents a positive constant, , respectively represent the Coriolis centripetal force matrix and the gravity matrix of the agricultural robot, is a diagonal matrix composed of the elements of the performance function, is the virtual control law The derivative of represents the fuzzy basis function represents the fuzzy parameter vector composed of fuzzy membership functions represents the transpose of the matrix

[0086] The position tracking controller is used to achieve the basic tracking performance; the adaptive compensation controller is used to compensate for system uncertainties; the fault-tolerant compensation controller is used to cope with system faults. The three work together to ensure the overall performance of the system

[0087] Specifically, the stability of the system design is proved by considering the following Lyapunov function

[0088]

[0089] where is the Lyapunov function , represents each joint error component represents the ideal fuzzy parameter vector and the error of the designed fuzzy parameter vector represents the fuzzy parameter vector composed of fuzzy membership functions represents the ideal fuzzy parameter vector

[0090] The time derivative of the Lyapunov function can be calculated as :

[0091]

[0092] where , , , and are positive constants respectively. It can be concluded that the system is globally fixed-time stable, and the stable time is

[0093]

[0094] where .

[0095] The effectiveness of a fault-tolerant control method for a specified performance of an agricultural robot based on a fuzzy logic system is verified through simulation; the trajectory of the leader is set in the following form

[0096] ;

[0097] where t is time

[0098] The fault model is set as follows

[0099] ;

[0100] wherein, , are respectively the angles of two joints of the robotic arm, is the input of the second joint.

[0101] As Figure 2 shown, under the action of the specified performance fault-tolerant control method of the agricultural robot based on the fuzzy logic system designed, the tracking error gradually converges from the initial 0.5 and -0.5 to 0. When faults occur in the system at the 8th second and the 10th second, the tracking error increases, but after a short adjustment, the error converges to 0 again. Throughout the process, the tracking error is always within the set boundary and does not exceed the predetermined boundary even when the system fails, thus proving the effectiveness of the designed control algorithm.

[0102] The fuzzy logic adaptive control law adopts the Mamdani-type fuzzy inference adaptive control mechanism, which includes the complete processes of fuzzification, fuzzy inference, and defuzzification. Among them, the fuzzy rule base summarizes the system dynamic characteristics based on expert experience, the inference mechanism adopts the maximum-minimum composition method, and the defuzzification adopts the centroid method.

[0103] Specifically, the Mamdani-type fuzzy inference adaptive control mechanism conducts fault detection and handling by real-time monitoring of the system state, quickly detecting and identifying the fault type, and taking corresponding compensation measures in a timely manner; thereby improving the reliability and fault handling ability of the system.

[0104] Furthermore, the input variables of the fuzzy logic adaptive control law include: state error and error change rate, and the output is the compensation control amount. The fuzzy sets of the input variables all adopt seven triangular membership functions, which respectively represent negative large, negative medium, negative small, zero, positive small, positive medium, and positive large.

[0105] Specifically, experimental verification shows that when actuator faults are artificially introduced during the picking process, the tracking error always remains within the range of ±0.01 rad, the system reaches a steady state within 2 seconds, the fault is detected within 100 ms after the fault occurs, the fault recovery time is less than 500 ms, and the control accuracy is improved by more than 40%; the system performance is verified through actual experiments, and the effectiveness of the control method is comprehensively evaluated; thus confirming the feasibility and superiority of this method in practical applications.

[0106] Example 2, fault-tolerant control application of an agricultural spraying robot.

[0107] A robotic arm with 4 degrees of freedom, the working space coverage radius is 2 m; the end effector is an adjustable nozzle, and the spraying width is adjustable from 0.5 to 2 m:

[0108] The working environment has uncertainties: terrain undulations, obstacles, and wind interference;

[0109] Implementation of the control strategy: Adopt a hierarchical control architecture.

[0110] The upper layer is task planning: path planning, obstacle avoidance strategy;

[0111] The lower layer is motion control: trajectory tracking, attitude adjustment;

[0112] The fuzzy logic system adopts seven triangular membership functions;

[0113] The universe of discourse of the input variables {e, ė} is [-1, 1];

[0114] The universe of discourse of the output variable u is [-2, 2];

[0115] Adopt 49 complete fuzzy rules;

[0116] Adopt a hierarchical control structure, decompose the complex control task into two levels of task planning and motion control, thus simplifying the complexity of the control system and improving the controllability of the system.

[0117] The spraying accuracy of the robotic arm is increased by 30%, and the coverage uniformity is improved by 50%; the robustness is significantly enhanced, and the anti-interference ability is increased by 40%.

[0118] The fault response time is less than 100ms; the working efficiency is increased by 60% compared with the traditional method.

[0119] Example 3: Precise control of an agricultural seeding robot.

[0120] This example demonstrates the application of the fault-tolerant control method for specified performance of an agricultural robot based on a fuzzy logic system in precision seeding operations.

[0121] Control objectives: The seeding depth error ≤ 5mm to meet the growth requirements of crops;

[0122] The seed spacing error ≤ 10mm to ensure a reasonable planting density;

[0123] This method is applied to a system with the ability of fault self-recovery to ensure continuous operation and adapt to complex soil conditions, including different soil textures such as clay and sandy soil.

[0124] Experimental environment preparation:

[0125] Select 3 different soil textures: sandy soil, loam, clay;

[0126] Set 3 seeding depths: 2cm, 3cm, 4cm;

[0127] Prepare 4 kinds of crop seeds: corn, soybean, wheat, rice;

[0128] Performance test items:

[0129] Sowing accuracy test: measure sowing depth and spacing;

[0130] Fault recovery capability test: artificially introduce three types of faults;

[0131] Work efficiency test: record the amount of work per unit time;

[0132] Energy consumption test: monitor the energy consumption of the entire operation process.

[0133] Experimental data:

[0134] Comparison of sowing accuracy under different soil conditions:

[0135] System performance improvement comparison:

[0136] Comparison of sowing effects of different crops:

[0137] The control method of the present invention has been successfully applied and verified in multiple agricultural robot scenarios, as follows:

[0138] Controlled experimental design:

[0139] The orchard picking robot served as the control group;

[0140] The greenhouse vegetable management robot served as the comparison group;

[0141] The farmland weeding robot served as the experimental group;

[0142] Orchard picking robots: Suitable for automated picking of various fruits such as apples, citrus, and pears. Orchard environments are complex and require handling of fruits at different heights and angles. The picking process must be smooth and precise to avoid damaging the fruits.

[0143] Comparison of experimental data:

[0144] Greenhouse vegetable management robot: manages the entire vegetable planting process in the greenhouse, including sowing, fertilizing, pest and disease control and other tasks; needs to adapt to high temperature and high humidity environment and work 24 hours a day.

[0145] Comparison of experimental data:

[0146] Farmland weeding robot: intelligent identification and removal of weeds in large areas of farmland, precise operation in complex terrain conditions

[0147] It is necessary to distinguish between crops and weeds to achieve targeted weed control.

[0148] Comparison of experimental data:

[0149] The embodiments of the present invention have been described above. However, these embodiments are not limited to the above specific implementation manners. The above specific implementation manners are merely illustrative rather than restrictive. Under the inspiration of these embodiments, those of ordinary skill in the art can also make more equivalent embodiments in various forms, all of which fall within the protection scope of these embodiments.

Claims

1. A fault-tolerant control method for a robotic arm with specified performance based on a fuzzy logic system, characterized in that It includes the following steps: Construct the kinematic, dynamic models and fault model of the agricultural robot; Based on the kinematic and dynamic models of the agricultural robot, establish the relative error dynamic equation between the actual position and the desired position; Design a specified performance function, and use the specified performance function to track the error value generated by the relative error dynamic equation within the desired range; Under the condition that the error is constrained, design a virtual control law using the backstepping method; Based on the uncertain terms, unknown environmental disturbances and fault model in the dynamic model, design an adaptive control law using a fuzzy logic system; Design a specified performance controller for the agricultural robot based on the virtual control law, fuzzy logic adaptive control law and full-state feedback to achieve the specified performance fault-tolerant control of the agricultural robot.

2. A fault-tolerant control method for a specified performance of a robotic arm based on a fuzzy logic system according to claim 1, characterized in that The fault model includes: Actuator bias fault and gain fault; Among them, the bias fault is manifested as a constant deviation in the actuator output; the gain fault is manifested as a change in the actuator output gain; these two fault models can comprehensively describe the main fault types that the agricultural robot may encounter during actual operation.

3. A fault-tolerant control method for specified performance of a robotic arm based on a fuzzy logic system according to claim 1, characterized in that, Designated performance function is as follows: ； Among them, represents the maximum tracking error boundary initially allowed by the system, represents the minimum tracking error boundary allowed at the steady state of the system, represents the convergence rate parameter adjustable by the user, specifying the performance function Flexibly set the performance index according to the actual task requirements.

4. A fault-tolerant control method for specified performance of a robotic arm based on a fuzzy logic system according to claim 1, characterized in that, The established relative error dynamic equation is: ； Among them, e represents the system tracking error vector, q represents the actual position vector of each joint of the agricultural robot, and q d represents the desired position vector to obtain the position deviation in real time and provide a basis for the subsequent controller design.

5. A fault-tolerant control method for a specified performance of a robotic arm based on a fuzzy logic system according to claim 1, characterized in that The virtual control law designed using the backstepping method is as follows: ； Among them, represents the derivative of the specified performance function , represents the conversion error after coupling the tracking error with the specified performance function, represents a positive constant, is the virtual control law, is the derivative of the desired position , and the gravity matrix is a diagonal matrix composed of the elements of the performance function, represents 's inverse matrix, represents another design result representation of the specified performance function, represents the conversion error 's element, is the upper bound value of the specified performance function, is a parameter designed by the user, represents a diagonal matrix.

6. A fault-tolerant control method for a specified performance of a robotic arm based on a fuzzy logic system according to claim 1, characterized in that The expression of the fuzzy adaptive control law is as follows: ； Among them, represents the fuzzy basis function, represents the fuzzy parameter vector composed of fuzzy membership functions, represents a positive constant, represents the speed tracking error.

7. A manipulator specified performance fault-tolerant control method based on a fuzzy logic system according to claim 1, characterized in that, The expression of the specified performance controller is as follows: ； wherein, represents the mass matrix of the agricultural robot, represents a positive constant, , respectively represent the Coriolis centripetal force matrix and the gravity matrix of the agricultural robot, is a diagonal matrix composed of elements of the performance function, is the virtual control law derivative of, represents the fuzzy basis function, represents the fuzzy parameter vector composed of fuzzy membership functions, represents the transpose of the matrix; The position tracking controller is used to achieve the basic tracking performance; the adaptive compensation controller is used to compensate for system uncertainties; The fault-tolerant compensation controller is used to cope with system faults, and the three work together to ensure the overall performance of the system.

8. A fault-tolerant control method for a robotic arm's specified performance based on a fuzzy logic system according to claim 1, characterized in that The fuzzy logic adaptive control law adopts the Mamdani-type fuzzy inference adaptive control mechanism, which includes a complete fuzzification, fuzzy inference and defuzzification process. Among them, the fuzzy rule base summarizes the system dynamic characteristics based on expert experience, the inference mechanism adopts the maximum-minimum composition method, and the defuzzification adopts the centroid method.

9. A fault-tolerant control method for specified performance of a robotic arm based on a fuzzy logic system according to claim 8, characterized in that, The input variables of the fuzzy logic adaptive control law include: The state error and the error change rate, and the output is the compensation control amount. The fuzzy sets of the input variables all adopt seven triangular membership functions, which respectively represent negative large, negative medium, negative small, zero, positive small, positive medium, and positive large.

10. A computer-readable storage medium, characterized in that, It is used to store computer-readable instructions, and when the computer-readable instructions are read by a computer, it can run a method for specified performance fault-tolerant control of a robotic arm based on a fuzzy logic system as described in any one of claims 1-9.

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