Design of Adaptive Control Approach Based on Neural Network to Nonlinear Systems in the Presence of Uncertainties

In the investigation presented with a focus on the applicability of adaptive control based on neural network to nonlinear systems in the presence of uncertainties, an adaptive control in association with intelligent tools with the key goal of adjusting nonlinear system’s parameters under control is designed. For having a novel idea in this area, at first, an exact consideration is made in one such case concerning the similar works that have all provided to deal with the similar systems under control and then the proposed control approach is designed based on the neural networks. Using Lyapunov theory, adaptive laws suitable for the convergence of the closed loop system is provided. Using this theory, it is proved that all signals in the closed loop system are bounded and the error signal converges to zero as asymptotically. At the end, the results of simulation programs via MATLAB verifies the effectiveness of the control approach proposed.