Synthesis of a discrete-time feedback neural controller

Real plants, generally nonlinear, are not satisfactorily controlled by linear methods. For this reason, a great part of the research centred on control theory is currently devoted to nonlinear and adaptive control. In this context, neural network methods are attractive because of their intrinsic abilities to identify nonlinear functions. After a presentation of neural networks and of learning methods, we propose an original control scheme inspired by “specialised learning”. This scheme allows an online computation of a neural controller that cancels tracking error in accordance with an imposed reference model. The ways in which learning is performed are discussed and a canonical representation of the control scheme is presented. Tested in simulation, this control scheme exhibits satisfactory properties especially for time convergence and nonlinear abilities