The problem of nonaffine time-varying nonlinear control systems is addressed in this paper through an adaptive state-space neuro-fuzzy control scheme. It combines an eight-layered neuro-fuzzy model to approximate nonaffine nonlinear systems' dynamics with a state feedback quadratic stabilizing controller. Both the neuro-fuzzy model and controller are updated online within a constrained unscented Kalman filter framework. The proposed generalized state-space neuro-fuzzy model is shown to be an universal approximator, and stability conditions derived for time-varying closed-loop systems. Results from a benchmark multi-input and multi-output system demonstrate the effectiveness of the proposed approach.
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Gil et al. (2018) studied this question.
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