Hybrid Iteration ADP Algorithm to Solve Cooperative, Optimal Output Regulation Problem for Continuous-Time, Linear, Multiagent Systems: Theory and Application in Islanded Modern Microgrids With IBRs
Hybrid Iteration ADP Algorithm to Solve Cooperative, Optimal Output Regulation Problem for Continuous-Time, Linear, Multiagent Systems: Theory and Application in Islanded Modern Microgrids With IBRs
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DOI：
10.1109/tie.2023.3247734
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发表时间：
2024-01
影响因子：
7.7
通讯作者：
Omar Qasem;M. Davari;Weinan Gao;Daniel R. Kirk;Tianyou Chai
中科院分区：
文献类型：
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作者：
Omar Qasem;M. Davari;Weinan Gao;Daniel R. Kirk;Tianyou Chai
本文提出了一种新的自适应动态规划(ADP)算法，称为混合迭代算法(HI)，用于求解连续时间、线性、多智能体系统的协作最优输出调节问题(Rp)。与传统的ADP算法，即策略迭代(PI)和数值迭代(VI)不同，HI算法不需要PI所要求的初始稳定控制策略。首先，提出了一种基于模型的HI算法来求解CO^{2}$RP问题。基于所提出的HI算法，设计了一种数据驱动的自适应最优控制器，用于在不利用任何系统物理信息的情况下解决协调、自适应和最优输出调节问题。取而代之的是，使用沿动态系统的轨迹收集的状态/输入信息。将所提出的数据驱动的HI应用于基于逆变器资源的孤岛现代微电网的自适应最优二次电压控制(也称为电压恢复控制)。仿真结果表明，与VI算法和PI算法相比，所提出的HI算法显著地节省了所部署的中央处理器(也称为CPU)的收敛时间，减少了学习迭代次数，并消除了对初始稳定控制策略的要求。通过对比实验，验证了该方法的实用性和优越性。
In this article, we propose a novel adaptive dynamic programming (ADP) algorithm, named hybrid iteration (HI), to solve the cooperative, optimal output regulation problem (CO$^{2}$RP) for continuous-time, linear, multiagent systems. Unlike the traditional ADP algorithms, i.e., policy iteration (PI) and value iteration (VI), HI does not need an initial stabilizing control policy required by PI. At the same time, it maintains a faster convergence rate compared with VI. First, a model-based HI algorithm is proposed to solve the CO$^{2}$RP. Based on the proposed HI algorithm, a data-driven, adaptive, optimal controller is developed to solve the cooperative, adaptive, and optimal output regulation problem without using any information about the physics of the system. Instead, the states/input information collected along the trajectories of the dynamic system is employed. The proposed data-driven HI is applied to the adaptive, optimal secondary voltage control (also known as voltage restoration control) of an islanded modern microgrid based on the inverter-based resources. Compared with the VI and PI algorithms, comparative simulation results demonstrate that the proposed HI approach is significantly able to save the convergence time of the central processing unit (also known as CPU) deployed, reduce the number of learning iterations, and remove the requirement of the initial stabilizing control policy. Comparative experiments reveal the practicality and superiority of the proposed methodology.