Low-Computation Adaptive Fuzzy Tracking Control for Nonlinear Systems via Switching-Type Adaptive Laws

Traditional and current event-based adaptive control usually require designing integral-type adaptive laws to estimate a number of unknown parameters. This process may excessively occupy limited computational resources and take a large amount of computational time, which may reduce the performance of the system, particularly in the case of online updating of parameter estimates. In this paper, to address this drawback, a novel event-triggered mechanism is designed which results in switching-type adaptive laws rather than the traditional integral-type ones. Through nonsmooth Lyapunov analysis, it is shown that the preset tracking precision is achieved and the boundedness of all the closed-loop signals is guaranteed. Finally, simulation results demonstrate the efficiency of the proposed algorithm.