顺序动作控制：非线性与非光滑系统的闭环最优控制
Sequential Action Control: Closed-Form Optimal Control for Nonlinear and Nonsmooth Systems
AI总结：
本文提出一种基于模型的算法，用于在线闭环计算非线性系统的预测最优控制。通过高阶模型和轨迹目标控制混合脉冲、欠驱动和约束系统，采用闭式表达式优化短期控制动作，保证全局唯一性和稳定性。
摘要：
本文提出了一种新的基于模型的算法，用于在线闭环计算传统上具有挑战性的非线性系统的预测最优控制。示例展示了该算法如何使用仅有的高层模型和轨迹目标来控制混合脉冲、欠驱动和约束系统。与迭代优化有限时间域控制序列不同，本文推导出个体控制动作的闭式表达式，即可以应用于短时间的控制值，以在长时间域内最优改善跟踪目标。在温和假设下，这些动作在平衡点附近变为线性反馈律，允许稳定性分析和基于性能的参数选择。全局上，最优动作保证存在性和唯一性。通过在线递推时间域的方式对这些动作进行序列化，所提出的控制器提供了一种最小-最大约束响应，避免了通常需要施加控制约束的开销。基准示例显示，该方法能够避免局部极小值，并在跟踪性能方面优于非线性最优控制器和最近的特定案例方法，且速度比传统方法快多个数量级。
英文摘要：
This paper presents a new model-based algorithm that computes predictive optimal controls on-line and in closed loop for traditionally challenging nonlinear systems. Examples demonstrate the same algorithm controlling hybrid impulsive, underactuated, and constrained systems using only high-level models and trajectory goals. Rather than iteratively optimize finite horizon control sequences to minimize an objective, this paper derives a closed-form expression for individual control actions, i.e., control values that can be applied for short duration, that optimally improve a tracking objective over a long time horizon. Under mild assumptions, actions become linear feedback laws near equilibria that permit stability analysis and performance-based parameter selection. Globally, optimal actions are guaranteed existence and uniqueness. By sequencing these actions on-line, in receding horizon fashion, the proposed controller provides a min-max constrained response to state that avoids the overhead typically required to impose control constraints. Benchmark examples show the approach can avoid local minima and outperform nonlinear optimal controllers and recent, case-specific methods in terms of tracking performance, and at speeds orders of magnitude faster than traditionally achievable.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
