Abstract
In actual work, the system parameters of the robot joints will change in real time or cannot be measured, and the coupling relationship between the various subsystems and the existence of modeling errors make the system model difficult to determine. Based on such problems, a neural network observation is proposed Decentralized control method for robot joints of the robot. In the actual control of the manipulator, firstly, the model of each joint subsystem is established by the decentralized control theory, and the nonlinear function approximation ability of the neural network is used to approach the uncertain part of the manipulator subsystem online through input and output data. The design observer can estimate the state of the system, and use the estimated state to design a sliding mode controller to dynamically estimate and compensate the unknown model dynamics of each independent joint, and realize the self-control of the system when the speed information and model information are unknown. Adaptive control greatly enhances the robustness and adaptability of the robotic arm system. Finally, the stability criterion of the neural network observer and the sliding mode controller is given by the Lyapunov function method, and the simulation results prove the effectiveness of the design method.
Highlights
Robot joints are widely used in a series of environments such as high-risk operations, military, medical, service, agricultural production and processing [1,2]
Many parameters and states of the robot joint system will change or Unable to measure, there is a coupling relationship between the various subsystems and is susceptible to external interference, and the system is affected by modeling errors, which makes it difficult to establish a model of the system [4]
Traditional computational torque control (CTC) and proportional-integral-derivative (PID) control methods have been difficult to meet the needs of high precision and high stability of the system, and the control algorithm with poor robustness will make the system move in an unpredictable direction Development, and even accidents, have higher requirements for the adaptability and robustness of the controller
Summary
Robot joints are widely used in a series of environments such as high-risk operations, military, medical, service, agricultural production and processing [1,2]. Considering that neural networks have a good approximation effect on nonlinear functions [5], many researchers are committed to combining neural networks with modern control methods to solve the problem of excessive dependence on models and system state information. When the crosslinking term meets the constraint conditions, the RBF neural network is used to observe the device estimates and compensates the system in real time, which greatly simplifies the dynamic model of the robotic arm.
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