Design And Research Of Manipulator's Finite Time Control Under Constraints

With the development of human society,human beings have higher demands and expectations for daily life and production.The wide application of robots and artificial intelligence have changed people's lives and improved the efficiency of production,and the researches of robot are closely related to the field of robot intelligence.Moreover,the researches of robot manipulator have attracted more and more people's attention all over the world.In the actual complex applications,we cannot ignore the existence of friction and material loss of the system,the model parameters of the system are difficult to accurately obtain.Meanwhile,the manipulator is under nonlinear constraints,and the existence of nonlinear characteristics will make the manipulator unable to achieve the desired control effect and affect the control performance.Therefore,the nonlinear constraints have become a difficult problem in the design and research of manipulator control.In addition,as the application environment of the manipulator becomes more and more complex,the control performance of the manipulator is put forward higher requirements and greater challenges.In this paper,the control problem of the manipulator with unknown dynamic information and constraints is studied.In order to solve the problems caused by the uncertainties of the system model parameters and nonlinear constraints,we apply neural network technology to the design of the controller.Because of the fast learning convergence speed and good fitting performance of the neural network,it can effectively solve the uncertainty and nonlinear problems.In this paper,the control problem of the manipulator with unknown dynamic model information and constraints is studied,and the adverse effects of three nonlinear characteristics of input saturation,input dead zone and unknown hysteresis on the control system are compensated,and the finite time control method is designed and studied respectively.Meanwhile,we apply the finite time control theory in the design and propose the neural network controller based on the finite time theory,and we use the Lyapunov stability theory for stability analysis.In addition,we also designed a simulation experiment to verify the feasibility and effectiveness of the designed controller.From the analysis of the results,it shows that under the designed controller,the system achieves satisfactory track tracking results,and its tracking error converges quickly to a small neighborhood near zero.The system has good convergence performance and transient performance.It shows that the designed controller can also eliminate the actuator nonlinear effect in a finite time,so that the closed-loop system has good control performance.Compared with the existing research methods,this paper does not need the precise dynamic model of the manipulator and the nonlinear characteristic information of the actuator.Considering that the control system parameter information is often difficult to obtain,the research method used in this paper is easier to realize in engineering.In this paper,the theory of finite time stability is introduced into the design of control strategy,which makes the adaptive parameters of neural network and other designs converge to the corresponding real value in finite time.And the designed control strategy ensures the finite time convergence performance of the manipulator system,which improves the transient performance.