Implementation of SNNPID optimized Neural Networks Controller for a two-wheeled mobile robot

Abstract

In this paper a new Smart PID optimized Neural Networks Approach (SNNPID) is applied to a two-wheeled differential mobile robot. The aim of this work is to optimize the parameters of a PID controller operating with the neural networks which supervise learning in order to teach the robot a desired behavior while tracking a desired trajectory. Many challenges could arise if the robot is moving in a hostile environment full of disturbances such as holes and stones. Yet, the engines that drive the robot should in no way be damaged. In fact, in the case of insurmountable disturbances, the robot must stop. The smart robot has to quickly perform tasks while ensuring stability and accuracy and must be autonomous with regards to decision making. Simulation results are obtained with the Matlab/Simulink environment and are given based on the application of load torques disturbances on the mobile robot behavior. These simulation results are satisfactory and have demonstrated the effectiveness of the proposed approach. The method applied has proven to be highly robust.