Abstracts Engineering

Model predictive control of a robot using neural networks

by Zhouping Wei

The aim of the thesis is to develop a model-based control strategy, namely, the Model Predictive Control (MPC) method, for robot position control using artificial neural networks. MPC is primarily developed for process control. Therefore its application in robot control has been less reported. In addition, conventional MPC uses linear model of the system for prediction which leads to inaccuracy for highly non-linear systems, such as robot. In this thesis a simulation model of a modified PUMA robot is constructed. This model is built using both MATLAB/SIMULINK and FORTRAN languages. In this model, the full robot dynamics is used together with the realistic factors, such as the actuator effects and the gear backlash, to represent the real system accurately. All simulations throughout this thesis are carried out on this model. A model predictive control strategy for robot trajectory tracking is also introduced in this thesis. The feasibility of the proposed MPC control method is studied based on a perfect prediction model, a model with uncertainties, and when the frequency band of the MPC controller is limited. Furthermore, a new method of using neural networks for robot dynamics modelling is introduced. This method is developed on the basis of a numerical differential technique that eliminates the explicit requirement of robot joint accelerations. Therefore, this method can be easily implemented on physical systems. As the measurements of the robot joint positions, velocities, and torques collected from operating the robot can be used to train the neural network, a more accurate dynamic model can be obtained. Finally, the MPC control method and the neural network model are combined together to form a neural network based MPC controller. The validity of this method is verified by using simulation on the simulated robot system