グラフ探索と機械学習に基づく二輪走行車両のモデル予測制御器設計

抄録

The configuration of a two-wheeled vehicle, such as Segway, cannot be stabilized by continuous and time-invariant state feedback due to its non-holonomic constraints. Because of the nonlinear nature of the nonholonomic constraints, the realization of a model predictive control (MPC) for this class of vehicles is a difficult task.
This paper proposes a MPC method that can achieve long prediction horizon and quick computation. At the first step, the optimization of an input (i.e., velocity and steering) sequence is formulated as a graph search problem by restricting the inputs to discrete values. Next, in the second step, the optimized control result is learned by machine learning method, such as SVM.
A longer horizon MPC compared to that with nonlinear optimization can be realized. The advantages of the proposed method are demonstrated with simulation and experimental results.