LMI-based robust constrained model predictive control of two-wheeled inverted pendulum

摘要

Two-Wheeled Inverted Pendulum (TWIP) robot is considered as an unstable and underactuated system affected by physical and environmental constrains. In addition, due to the model uncertainties, a robust control approach is needed to stabilize the posture of the TWIP. Robust Model Predictive Control (RMPC) based on linear matrix inequality (LMIs) is addressed to an optimization problem of the “worst-case” objective function over infinite moving horizon, subject to input and output constraints. In this paper, LMI-based RMPC is applied to a TWIP in spite of model uncertainty and motor torque constraint. A parametric poly-topic uncertainty model is used to describe the robot. Maximum torque produced by motors is defined as control input constraint. The efficiency of the RMPC is verified by comparing with nominal MPC.