Modelling and control of nonlinear, operating point dependent systems via associative memory networks

This paper presents a novel approach to the modelling and control of a specific class of nonlinear systems whose parameters are unknown nonlinear functions of the measurable operating points. An associative memory network is used to identify each nonlinear function, whose inputs are the measurable operating points and output being the estimated value of the parameter. Two different cases are considered; the first being those systems where the networks can exactly model the nonlinear functions, whereas the second case considers those systems which can only approximate the nonlinear functions to a known accuracy. The first type of system is referred to as a matching system and the second is called a mismatching system. During the modelling phase, the weights for each network are trained in parallel using the normalised back-propagation algorithm for matching system, and the modified recursive least squares algorithm for mismatching systems. It has been shown that these algorithms together with Goodwin's technical lemma lead to a stable d-step-ahead control scheme for matching systems and a pole assignment control strategy for mismatching systems.