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This paper presents the design and comparative analysis of Linear Quadratic Control and Model Predictive Control strategies for a two-wheel self-balancing robot. A detailed mathematical model of the two-wheel self-balancing is provided. After that, the Linear Quadratic Regulator and Model Predictive Control law are designed. In Order to compare the performances of both the controllers, various performance indices such as settling time and peak overshoot value are evaluated. The robustness of these methods is tested against impulse and sinusoidal disturbance. The control signals from linear quadratic regulator and model predictive controllers are also examined. The simulation results are obtained using MATLAB wherein it is observed that model predictive control performed better in both tracing the set points and generating minimal control signal.
Mishra et al. (Sat,) studied this question.