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arXiv 2503.00529eess.SYcs.SY
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用于输入约束实时非线性最优控制的协状态神经网络

Co-state Neural Network for Real-time Nonlinear Optimal Control with Input Constraints

Lihan Lian, Uduak Inyang-Udoh

AI总结:

提出协状态神经网络(CoNN),基于庞特里亚金极小值原理学习状态到最优协状态的映射,并通过求解二次规划满足输入约束,实现非线性最优控制问题的实时求解。

摘要:

本文提出一种利用神经网络(NNs)实时求解带有控制输入约束的非线性最优控制问题(OCPs)的方法。我们引入了被称为协状态神经网络(CoNN)的模型,该模型基于庞特里亚金极小(极大)值原理(PMP),学习从任意给定状态值到其对应最优协状态轨迹的映射。本质上,CoNN对由PMP针对各种初始状态产生的两点边值问题(TPBVP)进行了参数化。CoNN使用无约束OCPs的TPBVP数值解生成的数据进行训练,以学习从状态到对应最优协状态轨迹的映射。为了获得更好的泛化能力,CoNN还被训练以遵循一阶最优性条件(系统动力学)。通过在给定预测最优协状态的情况下求解二次规划(QP),满足了控制输入约束。我们在带有无约束和有约束控制输入的数值示例中,证明了基于CoNN的控制器在反馈方案中的有效性。我们还验证了该控制器能够有效处理未知扰动。

英文摘要:

In this paper, we propose a method to solve nonlinear optimal control problems (OCPs) with constrained control input in real-time using neural networks (NNs). We introduce what we have termed co-state Neural Network (CoNN) that learns the mapping from any given state value to its corresponding optimal co-state trajectory based on the Pontryagin's Minimum (Maximum) Principle (PMP). In essence, the CoNN parameterizes the Two-Point Boundary Value Problem (TPBVP) that results from the PMP for various initial states. The CoNN is trained using data generated from numerical solutions of TPBVPs for unconstrained OCPs to learn the mapping from a state to its corresponding optimal co-state trajectory. For better generalizability, the CoNN is also trained to respect the first-order optimality conditions (system dynamics). The control input constraints are satisfied by solving a quadratic program (QP) given the predicted optimal co-states. We demonstrate the effectiveness of our CoNN-based controller in a feedback scheme for numerical examples with both unconstrained and constrained control input. We also verify that the controller can handle unknown disturbances effectively.

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。

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