Adaptive robust optimal predictive control of robot manipulators

In this paper, we firstly propose a robust optimal predictive control (ROPC) scheme. In the ROPC scheme proposed, an optimal predictive control (OPC) is combined with a robust control that is constructed on the Lyapunov min-max approach. Since the control design of a real system may often be made on the basis of the imperfect knowledge about the model, it is an important trend to design the robust control law that guarantees the desired properties of the system under uncertain elements. By introducing the OPC technique in robust control here, we can find out much more deterministic controller for both the stability and the performance of uncertain nonlinear systems. Secondly, we propose the adaptive version of the ROPC scheme (AROPC). In the AROPC scheme proposed here, the adaptation law estimates the bounds of uncertainties of the system, and the robust control uses these estimates. This scheme is strongly useful in the case that the bounds of uncertainties are not known or known roughly. The analysis of the stability in the sense of Lyapunov is performed. We apply these algorithms to a simple type of 2-Iink robot manipulator and perform the simulations.