H _∞ tracking-based adaptive fuzzy sliding mode controller design for nonlinear systems

Observer-based adaptive fuzzy-neural control for a class of uncertain nonlinear systems with unknown dead-zone input

“…2, the truth value ψ i (layer III) of the antecedent part of the ith implication is calculated by (14). Among the commonly used defuzzification strategies, the outputs (layer IV) of the fuzzyneural system are expressed as (13).…”

“…In [12], Wang studied first an adaptive fuzzy control theory for a class of uncertain nonlinear SISO systems. Afterwards, some researchers devoted a lot of effort to the design of adaptive fuzzy control for uncertain nonlinear SISO systems [13][14][15][16][17] and MIMO systems [18,19]. Some researchers have also addressed the stability of uncertain nonlinear systems by using adaptive neural network control approaches [20][21][22].…”

Neural network based robust hybrid control for robotic system: an H ∞ approach

“…where η is the learning rate of neural network and Ξ = Φ(·) T B T 0 P x; And the robust controller u r in (15) is defined as (20) with…”

“…As robustness and its capability of disturbance attenuation in nonlinear systems, the approach of H ∞ optimal control has been widely discussed [19,20], also applied in robotic systems. In conventional H ∞ controls, the plant models must be known, perhaps allowing a small perturbation.…”

Adaptive fuzzy tracking control of nonlinear time-delay systems with unknown virtual control coefficients

“…In the early stage of nonlinear robust control, matching conditions are usually assumed to obtain asymptotically stable results [3,15,17,20,23]. With the development of nonlinear control technique, the restriction of matching conditions has been removed for a class of nonlinear systems, see, for example, [1,6,7,14,16], and the references therein.…”