Sliding mode learning control for SISO complex systems with T-S fuzzy models

In this paper, a sliding mode learning control scheme for a class of nonlinear systems is studied. First, the Takagi-Sugeno (T-S) fuzzy modelling technique is employed to model the nonlinear systems. Second, a sliding mode learning control is designed to drive the sliding variable to reach the sliding mode surface in a finite time, and the system states can then asymptotically converge to zero. The advantages of this scheme are that (i) the information about the uncertain system dynamics and the system model structure is not required for the design of the learning controller; (ii) the closed loop system behaves with a strong robustness with respect to uncertainties; (iii) the control input is chattering-free. The sufficient conditions for the sliding mode learning control to stabilize the global fuzzy model are discussed in detail. A simulation example on the control of an inverted pendulum cart is presented to demonstrate the effectiveness of the proposed control scheme.