Adaptive Neural Networks Based Robust Output Feedback Control for Nonlinear System

The performance of the control system is reduced by uncertain nonlinearities behaviors, which can be enhanced by implementing an adaptive approach represented by the robust output-feedback control and artificial neural network, which is proposed in this paper and utilized for identification and control of a nonlinear system. The Cart Pole System (CPS) is treated as a multi-body dynamical system, and the nonlinear swing-up problem is handled by designing an adaptive neural network which trained using a modified conventional controller called Linear Quadratic Optimal State Estimator with Integral Control (LQOSEIC). In this paper, the nonlinear system CPS stabilized utilizing robust output feedback control called LQOSEIC, this controller allows a linearized system to act as a model reference for the original nonlinear system, but they are only valid for a limited range of operations and will fail if the plant characteristics are unknown or uncertainty. An adaptable neural network is used to overcome this challenge., in which the adaptive neuro controller is trained offline using LQOSEIC to get the initial weights of neurons for layers network, after finished the training the LQOSEIC will be replaced by adaptive neural control. The real advantage of a neuro-controller is its ability to update online depending on the error signal. The neuro-controller demonstrates that when any disturbance or uncertainty arises in a non-linear system, neural networks characterized by online learning compensate for the effect of unpredictable conditions. The suggested adaptive neural network improves control performance and ensures the closed-loop control system's robust stability. Finally, numerical simulations are used to demonstrate the efficacy of the proposed controllers.