Operator-based Nonlinear Robust Control For Manipulator

With the continuous progress of technology, there are more and more robot in our daily life. Among these robots, There is no doubt that the most widely used robot is industrial robot, namely robotic manipulator. Thus it is very important to research robotic manipulator.For robotic manipulator, many control strategies are proposed now. Like adaptive control, iterative learning control, sliding mode control. Where adaptive control is very strict in real time,when there is uncertainty, it is difficult to guarantee the stability of system. Iterative learning control has self learning property, when external environment changes, it needs learn again. Sliding mode control has chattering, which is easy to cause machine wear. It is difficult for us investigating robotic manipulator. Thus, in this paper an operator based right robust factorization approach is proposed, for investigating manipulator.The main work of this paper are, first the basic knowledge of nonlinear system and operator theory are introduced. Then, the dynamic model of manipulator is derived by using Lagrange equation, the right factorization is given for the model, based on this,according to robust stability condition, right co-prime factorization controller is designed.At the same time, in order to guarantee tracking performance of robotic manipulator,tracking controller is designed, simulation shows tracking performance is good, control error need reduce.Consider the uncertainties of manipulator, an operator observer based tracking feedback control system is designed, the uncertainties of the manipulator is compensated by an operator observer, Simulation results show that the proposed method significantly improves the control accuracy of the system.