This paper addresses the design of a stabilizing continuous time sampled-data Nonlinear Model Predictive Control (NMPC) law to solve the Moving Path Following (MPF) motion control problem for constrained under-actuated robotic vehicles. In this scenario, the robotic vehicle is tasked to converge to a desired geometric path, expressed with respect to a moving frame of reference, while satisfying the actuation constraints. This control problem is addressed in the NMPC framework. Specifically, first a suboptimal Lyapunov-based nonlinear auxiliary control law is designed to solve the MPF problem. Then, the latter is used for the design of a suitable terminal set and terminal cost of the MPC controller to enforce closed-loop guarantees. Exploiting the properties of the auxiliary control law, we show that for a suitable selection of the input constraints, the terminal set can be removed, resulting in a global region of attraction of the proposed controller. Simulation results are provided to illustrate the proposed control strategy.
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机译：本文解决了稳定连续时间采样数据非线性模型预测控制（NMPC）定律的设计，以解决对受约束的机器人机器人车辆的移动路径（MPF）运动控制问题。在这种情况下，机器人车辆是任务收敛到所需的几何路径，相对于移动框架表示，同时满足致动约束。在NMPC框架中解决了该控制问题。具体而言，首先是基于SubOptimal Lyapunov的非线性辅助控制定律，旨在解决MPF问题。然后，后者用于设计MPC控制器的合适终端集和终端成本，以实施闭环保证。 Exploiting the properties of the auxiliary control law, we show that for a suitable selection of the input constraints, the terminal set can be removed, resulting in a global region of attraction of the proposed controller.提供了仿真结果以说明所提出的控制策略。
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