Adaptive Identification and Control of Dynamical Systems Using Neural Networks

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The focus of this research is on the adaptive identification and control of complex nonlinear dynamical systems using neural networks. From the theoretical point of view the main aim is to generate stable adaptive laws for the adjustment of the parameters of the system. From the practical viewpoint the aim is to generate control methods which yield high performance in complex environments. The nonlinear nature of the neural networks makes stability analysis of the overall system and extremely difficult task. Hence, it is essential to confine our attention to a class of systems which have been shown at least empirically to perform successfully. The research will consequently be carried out in the following four stages: (i) determination of suitable dynamic structures for identification and control models which are applicable to a wide class of nonlinear plants, (ii) development of dynamic back propagation methods for adjusting controller parameters, (iii) use of such methods to carry out extensive computer simulations on the identification and control of complex systems (iv) stability analysis of the class of systems which prove efficient in (iii).