基于策略引导终端要素的经济模型预测控制

中文摘要 AI 辅助

传统模型预测控制（MPC）设计通常依赖从稳态推导得到的终端代价与约束，以保证闭环稳定性和性能。然而，对稳态假设的依赖限制了该控制方法在不存在或不期望存在固定工作点的系统中的适用性。本研究引入一种名为策略引导MPC的新框架以解决该局限。该方法利用已知的次优控制策略构建终端代价与约束：终端区域围绕策略展开计算得到的中心定义，终端代价采用对偏离该中心的惩罚项来定义。该方法无需稳态或参考轨迹，针对有限和无限时域问题均建立了相对于引导策略的闭环性能保证，通过能源管理示例的数值模拟验证了所提框架的有效性。

英文摘要

Conventional designs for model predictive control typically rely on terminal costs and constraints derived from a steady state to guarantee closed-loop stability and performance. However, this dependence on a steady-state assumption limits the applicability of this control method to systems in which such a fixed operating point is either not available or not desirable. This work introduces a novel framework, termed policy-guided MPC, to address this limitation. Our approach constructs terminal costs and constraints using a known sub-optimal control policy. Specifically, the terminal region is defined around a center determined by a rollout of the policy, and a penalty on deviation from this center is used to define the terminal cost. This method obviates the need for a steady state or reference trajectory. Closed-loop performance guarantees are established relative to the guiding policy, for both finite and infinite horizon problems. The effectiveness of the proposed framework is demonstrated through numerical simulations on an energy management example.

发表机构

• Delft University of Technology（代尔夫特理工大学）

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