Adaptive Neural Networks Based Robust Output Feedback Controllers for Nonlinear Systems

The performance of the nonlinear control system that is subjected to uncertainty, can be enhanced by implementing an adaptive approach by using the robust output-feedback control and the artificial intelligence neural network. This paper seeks to utilize output feedback control for nonlinear system using artificial intelligence employing neural network. The Two Wheel Mobile Robot (TWMR) is treated as a multi-body dynamic system. The nonlinear swing-up problem is handled by designing an adaptive neural network, which is trained using a modified conventional controller called Linear Quadratic Optimal State Estimator with Integral Control (LQOSEIC). In this paper, the nonlinear system TWMR is stabilized utilizing a robust output feedback control called LQOSEIC. This controller allows a linearized model to emulate a model reference for the original nonlinear system. However, it works for a limited range of operations and will fail if the plant characteristics are unknown or uncertain. An adaptive neural network is used to overcome this problem. The adaptive neural controller is trained offline using LQOSEIC to obtain the initial weights of neurons for the network's hidden layers. After finishing the training, the LQOSEIC will be replaced by the adaptive neural controller. The main advantage of a neuro-controller is its ability to update the weights online depending on the error signal. If there are any disturbances or uncertainties that arises within the concerned nonlinear system, the neuro-controller will be able to handle it because of online learning that compensates for the effect of unpredictable conditions. The proposed adaptive neural network improves control performance and ensures the robust stability of the closed-loop control system. Finally, numerical simulations are used to demonstrate the efficacy of the proposed controllers.