Mobile Robot Trajectory Tracking Using Neural Networks
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This work primarily addresses the design and implementation of a neural network based controller for the trajectory tracking of a differential drive mobile robot. Two different neural network based tracking control algorithms are proposed and their simulation and experimental results are presented. The first algorithm is a control structure that makes possible the integration of a backstepping controller and a neural network (NN) computed-torque controller for a nonholonomic mobile robot. Integration of a neural network controller and the kinematic based controller gives the advantage of dealing with unmodeled and unstructured uncertainties and disturbances to the system. The second proposed control algorithm is an NN-based adaptive controller which tunes the gains of the backstepping controller online according to the robot reference trajectory and its initial posture. In this method, a neural network is needed to learn the characteristics of the plant dynamics and make use of it to determine the future inputs that will minimize error performance index so as to compensate the backstepping controller gains. The advantages and disadvantages of the two proposed control algorithms will be discussed in each section with illustrations. Comprehensive system modeling including robot kinematics, dynamics and actuator modeling has been done. The dynamic modeling is done using Newtonian and Lagrangian methodologies for nonholonomic systems and the results are compared to verify the accuracy of each method. Simulation of the robot model and different controllers has been done using Matlab and Matlab Simulink. The proposed trajectory tracking controllers has been tested on ERA-MOBI mobile robot platform which is a full featured industrial mobile robot in AUS Mechatronics center and all trajectory tracking results with different reference trajectories are presented.