Adaptive Optimal Sliding Mode Controller of Affine Nonlinear Systems

This thesis presents an adaptive optimal control algorithm (AOCA) dedicated to solve the optimal sliding mode control problems of affine nonlinear systems by sequential optimization. Concerning with sliding model control of nonlinear systems, designers are used to linearize the system model about an operating point in order to solve for the sliding mode controller under linear environment. However, the linearization may introduce large model error while shifting between operating points that, as a result, leads to bad control performance or even fails to work in systems with severe nonlinearity. In contrast, the AOCA deals with the nonlinear model directly to optimize the equivalent control law in terms of minimizing a specified cost function. The AOCA organizes the optimality conditions derived from the minimum principle in the architecture of reinforcement learning to achieve sequential optimization. The proposed design is dedicated to use in affine nonlinear systems, and the effectiveness has been investigated in several bench-mark examples by computer simulations. The results show that the design can find out the optimal equivalent control laws of sliding model control systems.