Modelling and control of a two-wheeled robot manipulator

This thesis presents Adaptive Model Predictive Control approach for a two-wheeled robot manipulator with varying mass. The mass variation corresponds to the robot picking and placing objects or loads from one place to another. A Linear Parameter Varying model of the system is derived consisting of local linear models of the system at different values of the varying parameter. An Adaptive Model Predictive Control controller is designed to control the fast-varying center of gravity angle in the inner loop. The reference for the inner loop is generated by a slower outer loop controlling the linear position using an Linear Quadratic Gaussian regulator. The control system is simulated on the nonlinear model of the robot, and the closed-loop performance of the proposed scheme is compared with a system having Proportional Derivative Integral controllers for both loops, Feedback Linearization Controller for inner loop, as well as one having Linear Quadratic Gaussian controllers for both loops. It is seen that Adaptive Model Predictive Control shows mostly superior and otherwise very good performance when compared to these two benchmarks in terms of reference tracking and robustness to mass parameter variations. Finally, comparison of two controllers Adaptive Model Predictive Control and Model Predictive Control comparison is presented in order to verify the leadership of the adaptive one for the future real-time implementation.