Neuro-fuzzy dynamic pole placement control of nonlinear discrete-time systems

An algorithm for control of nonlinear discrete-time systems is presented in the paper. Controlled system is linearized by dynamic output feedback so that the linearized closed loop system is equivalent to a predefined discrete-time transfer function representing reference model of the control system. Choice of the reference model provides placement of zeroes and poles of the closed loop system. In the proposed approach at least one of the poles is not fixed and moves during the time of transient process. Evolution of the pole occurs according to certain rules formalized and implemented in the algorithm by means of fuzzy logic. Therefore, the parameters of the transfer function of the linearized closed loop system may be understood as nonlinear functions which depend on the current control error and its derivative. Thus the poles of the closed loop system are placed dynamically according to the predefined rules providing necessary behavior of the control system. Controlled system has to be represented by a nonlinear model with no couplings between different time instances what can be performed by training an Artificial Neural Network of the specific structure. The developed theory and control algorithm are illustrated by means of numerical example.