Intelligent Robust Adaptive Inverse Dynamical Controller for Nonlinear Dynamics of Vehicle Systems

Compared to the other research that focuses on the intelligent identification of nonlinear systems, the deliberated methodology evolved the inverse intelligent process as a universal controller for a class of nonlinear systems and this method has achieved a remarkably low root mean square (RMS) and tracking error rate. In the vehicle systems, the tracking of the predefined desired path is challenging to achieve, especially in presence of disturbances. The planned AIDS procedure consists of an online inverse model identifier updated using the back-propagation (BP) algorithm. In this approach, an offline identification phase provides the initial network weights. A Multilayer Perceptron (MLP) is then employed as a nonlinear controller, trained to represent the system's inverse dynamics and applied to the vehicle model. The convergence of the noisy states to the nominal ones, the robustness of the recommended designing system, and the reduction of noisy phenomena effect in the system's state are all crucial advantages of the planned AIDC. Simulation results demonstrate the promising performance of the proposed methodology.

کلیدواژه ها:

Inverse Neural Network ، Adaptive Inverse Control ، Path Planning ، Back-Propagation Algorithm ، Neural Network