The Sliding Mode Control Algorithm Research For Mechanical Arm

Robot is a highly complicated system with time-varying,strong coupling and nonlinear dynamic characteristics.It also contains various uncertain factors,such as unmodeled dynamics,parameter measurement errors,load changes,external disturbances and the friction between joints,etc.So,it is unable for us to obtain the accurate dynamic model of robot.These uncertainties will have effect on the control performance and dynamic quality of the system seriously.Especially on the high precision,high performance and high speed robot system,Researches on how to control robot with modeling error and external disturbance are particularly important.Having investigated the trajectory tracking control for uncertain robot with modeling error and external disturbance,some effective control strategies and methods are presented;the main contents are as follows:First,the dynamic model and basic properties of the rigid robot are analyzed,and the necessary mathematical preparation basis for follow-up research is given.Then,to solve the trajectory tracking control problems of the rigid robot system,three effective control strategies are designed as following.(1)A dual adaptive sliding mode robust control strategy is proposed.It uses adaptive fuzzy control to approximate robot's own nonlinear friction amount,and adjusts the modeling error of the system online with the self-correcting model parameters,then introduces robust controls to eliminate the effects of approximation error and ensure the stability of the tracking ability of the system.The simulation validates the effectiveness of the algorithm.(2)A sliding mode control strategy based on adaptive neural network is proposed.The calculated torque control is used for the nominal model of the robot arm so that the nominal system gradually tracks the desired trajectory;the neural network is used to compensate for the centralized uncertainties of the sliding control;the adaptive algorithm is used to identify the weight of the neural network in the system.This not only guarantees the robust performance of the system,but also avoids using the estimated uncertain upper bound value to make the controller too conservative and the actuator saturation.The adaptive control algorithm has fewer adjustment parameters and simple structure,which ensures the global asymptotic convergence of the system under the action of external disturbances and unmodeled dynamics.The simulation results prove the good tracking ability of the algorithm.(3)A sliding mode control strategy based on fuzzy switching gain adjustment is proposed.Utilizing the strong fault tolerance and the ability to adapt to the changes in the dynamic characteristics and external environmental conditions of the controlled object of the fuzzy logic,the mainly insufficiency of the mapping function due to the center of the hidden basis function of the neural network has been avoided.The fuzzy integral method is used to approximate the central uncertainties in the non-standard part sliding mode control of the manipulators.Global stability of the system is achieved with the global sliding mode function.Finally,the specific control algorithms and simulation analysis are given.Finally,the main research contents and innovation points are summarized,and the problems for further research are prospected.