Composite adaptive neural tracking control of uncertain strict‐feedback systems

Abstract

AbstractComposite adaptive neural control of uncertain strict‐feedback systems is synthesized. It mainly consists of an adaptive neural backstepping controller, an immersion and invariance (I&I) update algorithm, and a set of state filters for conquering the in‐feasibility of the states' derivatives required in the update algorithm. For compensating the indefinite coupling terms in the update algorithm, a novel nonlinear ‐modification method is promoted. To further tackle the case with unknown input gain functions, the dynamic surface control (DSC) and the adding‐an‐integrator technique are incorporated for preventing the so‐called explosion of complexity and the algebraic‐loop problems in the virtual controller and the composite update algorithm, respectively. The proposed design ensures the semi‐globally uniformly ultimately bounded (SGUUB) stability of the closed‐loop system and, in particular, the convergence of prediction errors to the vicinity of zero without persistent excitation (PE), which in turn improves the tracking performance.