Trajectory Tracking Controller of Mobile Robot under Time Variation Parameters based on Neural Networks and Stochastic Fractal Algorithm

摘要

This study suggests an adaptive Artificial Neural Network (ANN) controller that based on Stochastic Fractal Search algorithm (SFS), the purpose of the Adaptive Neural Controller (ANC) is to track a proposed velocities and path trajectory with the minimum required error, in the presence of mobile robot parameters time variation and dynamical system model uncertainties. The proposed ANC will consist of two sub-neural controllers; the Kinematic Neural feedback Controller (KNC) and the Dynamic Neural feedback Controller (DNC). The external feedback kinematic neural controller is responsible for generating velocity tracking signals that track the mobile robot linear and angular velocities depending on the robot posture error and the desired velocities, while the internal dynamic neural controller is used to enhance the mobile robot against parameters uncertainty, parameters time variation and disturbance noise. The stochastic fractal search algorithm is a Metaheuristic Optimization Algorithm (MOA) that has been used to optimize the Neural Networks (NNs) weight connections to has the behavior of an adaptive nonlinear trajectory tracking controller of a differential drive wheeled mobile robot. The proposed controller has the capability to prepare an appropriate dynamic control left and right torque signals to drive various mobile robot platforms using the same offline optimized weight connections. Metaheuristic optimization algorithms have been used due to theirs unique characteristics especially theirs free of derivative, ability to optimize discretely and continuous nonlinear functions and their ability to get rid of local minimum solution trapping.