In this study, we propose a method of stability analysis for a GA-Based reference ANNC which is capable of handling these types of problems in a nonlinear system. First of all, radial basis function networks are utilized to well approximate an uncertain and nonlinear plant, for the tracking of a reference trajectory. Next, the initial values of the consequent parameter vector are decided via a genetic algorithm (GA), after a modified adaptive law is derived based on Lyapunov stability theory for the purpose of controlling the nonlinear system which is used for tracking a user-defined reference model. The requirement of the Kalman-Yacubovich lemma is fulfilled. A boundary-layer function is introduced into these updating laws to cover parameter and modeling errors, and to guarantee that the state errors converge within a specified error bound. After this, an adaptive neural network controller (ANNC) is derived to simultaneously stabilize and control the system. Finally, a numerical simulation is carried out. The simulation results show the rapidity and efficiency with which the control methodology can control nonlinear systems.
INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL