Adaptive Fuzzy State/Output Feedback Control of Nonstrict-Feedback Systems: A Direct Compensation Approach.

Issues of adaptive fuzzy direct compensation-based state/output feedback control for nonstrict-feedback systems are presented. The key to its feasibility is the differentiation-free feature, which is achieved in two steps. First, with the nominal adaptive fuzzy virtual controllers as the inputs, a set of low-pass filters are constructed to avoid the explosion of complexity and the algebraic-loop problems.

Differentiation-Free Multiswitching Neuroadaptive Control of Strict-Feedback Systems.

Issues of differentiation-free multiswitching neuroadaptive tracking control of strict-feedback systems are presented. It mainly consists of a set of nominal adaptive neural network compensators plus an auxiliary switched linear controller that ensures the semiglobally/globally ultimately uniformly bounded stability when the unknown nonlinearities are locally/globally linearly bounded, respectively. In particular, the so-called explosion of complexity is annihilated in two steps.

Global tracking control of strict-feedback systems using neural networks.

Most existing adaptive neural controllers ensure semiglobally uniform ultimately bounded stability on the condition that the neural approximation remains valid for all time. However, such a condition is difficult to verify beforehand. As a result, deterioration of tracking performance or even instability may occur in real applications.