Discrete-time CMAC NN control of feedback linearizable nonlinear systems under a persistence of excitation

The local structure of CMAC neural networks (NN) result in better and faster controllers for nonlinear dynamical systems. A CMAC neural network-based discrete-time controller which linearizes the unknown multiinput and multioutput (MIMO) nonlinear system through feedback is presented. Control action is defined in order to achieve tracking performance for this unknown nonlinear system. An efficient and localized weight addressing scheme for the CMAC NN's is described using an appropriate choice of the B-spline receptive field functions that form a basis. A uniform ultimate boundedness of the closed-loop system is given in the sense of Lyapunov using the persistency of excitation (PE) condition. Simulation results are shown to demonstrate the theoretical conclusions.