Neuro – Fuzzy Control Schemes Based on High Order Neural Network Function Approximators (original) (raw)

Artificial Higher Order Neural Networks for Computer Science and Engineering

The indirect or direct adaptive regulation of unknown nonlinear dynamical systems is considered in this chapter. Since the plant is considered unknown, we first propose its approximation by a special form of a fuzzy dynamical system (FDS) and in the sequel the fuzzy rules are approximated by appropriate high order neural networks (HONN's). The system is regulated to zero adaptively by providing weight updating laws for the involved HONN's, which guarantee that both the identification error and the system states reach zero exponentially fast. At the same time, all signals in the closed loop are kept bounded. The existence of the control signal is always assured by introducing a novel method of parameter hopping, which is incorporated in the weight updating laws. The indirect control scheme is developed for square systems (number of inputs equal to the number of states) as well as for systems in Brunovsky canonical form. The direct control scheme is developed for systems in square form. Simulations illustrate the potency of the method and comparisons with conventional approaches on benchmarking systems are given.

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