为混合系统设计MPC控制，确保长期稳定。
Model Predictive Control of Hybrid Dynamical Systems
- 基于混合时间域构建预测与控制时域，优化系统行为。
- 提出可验证的稳定性条件，保证系统渐近收敛到目标集。
- 适合需要精确稳定控制的工程系统，如机器人、电网调控。
针对使用模型预测控制（MPC）控制混合动力系统的难题，本文提出了渐近稳定性集合的充分条件。混合动力系统通过包含微分方程和差分方程的混合方程建模，涉及输入与约束。所提出的混合MPC算法借鉴混合时间域设计预测与控制时域。文章给出了混合优化问题的结构特性、可行集及价值函数的性质。提供了可检验的稳定性条件，这些条件基于阶段代价、终端代价以及静态状态反馈律的性质，并通过控制李雅普诺夫函数条件关联。文中多个例子贯穿说明了所得结果。
原文摘要 · Abstract (English)
The problem of controlling hybrid dynamical systems using model predictive control (MPC) is formulated and sufficient conditions for asymptotic stability of a set are provided. Hybrid dynamical systems are modeled in terms of hybrid equations, involving a differential equation and a difference equation with inputs and constraints. The proposed hybrid MPC algorithm uses a suitable prediction and control horizon construction inspired by hybrid time domains. Structural properties of the hybrid optimization problem, its feasible set, and its value function are provided. Checkable conditions to guarantee asymptotic stability of a set are provided. These conditions are given in terms of properties on the stage cost, terminal cost, and the existence of static state-feedback laws, related through a control Lyapunov function condition. Examples illustrate the results throughout the paper.
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