面向非线性分布式控制的神经端口哈密顿模型：一种无约束参数化方法
Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach
AI总结：
本文提出基于端口哈密顿框架的神经网络分布式控制策略，通过无约束参数化保证非线性系统闭环稳定性及有限L2增益，并在移动机器人和直流微电网中验证有效性。
摘要：
大规模信息物理系统的控制需要仅依赖与相邻智能体有限通信的最优分布式策略。然而，为非线性系统计算稳定控制器并同时优化复杂代价仍然是一个重大挑战。神经网络（NNs）以其表达力著称，可用于参数化控制策略以获得良好性能。然而，神经网络对微小输入变化的敏感性带来了使闭环系统失稳的风险。许多现有方法对控制器参数空间施加约束以保证闭环稳定性，这导致优化过程计算代价高昂。为解决这些问题，我们利用端口哈密顿系统框架，为非线性系统设计连续时间分布式控制策略，保证闭环稳定性和有限 $\mathcal{L}_2$ 或增量 $\mathcal{L}_2$ 增益，且与控制器的优化参数无关。这消除了在优化过程中约束参数的需要，允许使用基于梯度的方法等标准技术。此外，我们讨论了保持这些控制器耗散性质的离散化方案，以便在嵌入式系统上实现。所提出的分布式控制器的有效性通过受避碰约束的非完整移动机器人的一致性控制以及直流微电网中带加权功率共享的平均电压调节得到了证明。
英文摘要：
The control of large-scale cyber-physical systems requires optimal distributed policies relying solely on limited communication with neighboring agents. However, computing stabilizing controllers for nonlinear systems while optimizing complex costs remains a significant challenge. Neural Networks (NNs), known for their expressivity, can be leveraged to parametrize control policies that yield good performance. However, NNs' sensitivity to small input changes poses a risk of destabilizing the closed-loop system. Many existing approaches enforce constraints on the controllers' parameter space to guarantee closed-loop stability, leading to computationally expensive optimization procedures. To address these problems, we leverage the framework of port-Hamiltonian systems to design continuous-time distributed control policies for nonlinear systems that guarantee closed-loop stability and finite $\mathcal{L}_2$ or incremental $\mathcal{L}_2$ gains, independent of the optimzation parameters of the controllers. This eliminates the need to constrain parameters during optimization, allowing the use of standard techniques such as gradient-based methods. Additionally, we discuss discretization schemes that preserve the dissipation properties of these controllers for implementation on embedded systems. The effectiveness of the proposed distributed controllers is demonstrated through consensus control of non-holonomic mobile robots subject to collision avoidance and averaged voltage regulation with weighted power sharing in DC microgrids.
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。
