Low-Computation Adaptive Fuzzy Tracking Control of Unknown Nonlinear Systems With Unmatched Disturbances

This paper investigates the tracking control problem for a family of strict-feedback systems with unknown nonlinear functions as well as unmatched disturbances. A low-computation adaptive fuzzy control strategy combined with a constraint-handling technique is proposed to achieve accurate trajectory tracking and boundedness of the closed-loop signals. In contrast to the existing results: first, without the expense of introducing auxiliary filters, iterative calculation of virtual control signal derivatives at each step of the backstepping design that may cause the explosion of complexity issue is obviated; second, the need for disturbance observers and robust compensators to suppress disturbances and approximation errors that require a mass of online learning parameters for estimation is evaded; and third, a small number of closed-loop signals are incorporated into the input space of fuzzy logic systems for approximation. The result from a comparative simulation further illustrates the superiority of the presented approach.