Robust adaptive fuzzy filters output feedback control of strict-feedback nonlinear systems

Abstract

In this paper, an adaptive fuzzy robust output feedback control approach is proposed for a class of single input single output (SISO) strict-feedback nonlinear systems without measurements of states. The nonlinear systems addressed in this paper are assumed to possess unstructured uncertainties, unmodeled dynamics and dynamic disturbances, where the unstructured uncertainties are not linearly parameterized, and no prior knowledge of their bounds is available. In recursive design, fuzzy logic systems are used to approximate unstructured uncertainties, and K-filters are designed to estimate unmeasured states. By combining backstepping design and a small-gain theorem, a stable adaptive fuzzy output feedback control scheme is developed. It is proven that the proposed adaptive fuzzy control approach can guarantee the all the signals in the closed-loop system are uniformly ultimately bounded, and the output of the controlled system converges to a small neighborhood of the origin. The effectiveness of the proposed approach is illustrated by a simulation example and some comparisons.

Prescribed performance adaptive fuzzy dynamic surface control of nonaffine time‐varying delayed systems with unknown control directions and dead‐zone input

“…Due to reliability and robustness of Gaussian membership functions, they are used in many articles such as in other works. 9,12,28,51,52 Choosing fuzzy membership function as…”

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“…This work avoids the singularity problem, while the proposed algorithm is challenging to be implemented. These problems are relaxed in [14], where by using an adaptive fuzzy controller the singularity problem is completely avoided. The work also guarantees the closed‐loop stability without the requirement of integral‐type Lyapunov functions.…”

Designing a ship course controller by applying the adaptive backstepping method

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