Case study 3: robust control of two-wheeled robot

Abstract

This case study presents the design and experimental evaluation of two controllers for vertical stabilization of two-wheeled robot. The first one is a conventional linear quadratic Gaussian (LQG) controller with 17th-order Kalman filter used for state estimation. This controller ensures robust stability of the closed-loop system and good nominal performance. The second one is a μ controller ensuring both robust stability and robust performance. Due to the lack of accurate analytical robot model, the controllers design is based on models derived by closed-loop identification from experimental data. The robot uncertainty is approximated by an input multiplicative uncertainty which leads to a μ controller of order 44, subsequently reduced to 30. The yaw motion is controlled by using a proportional-integral (PI) controller on the basis of yaw angle estimate obtained by a separate second order Kalman filter. A software in MATLAB®/Simulink® environment is developed for generation of control code which is embedded in the Texas Instruments Digital Signal Controller TMS320F28335. Results from the simulation of the closed-loop system as well as experimental results obtained during the real-time implementation of the designed controllers are given. The theoretical investigation and experimental results confirm that the closed-loop system achieves robustness in respect to the uncertainties related to the identified robot model.