论文标题
通过投影的原始二重式零阶动力学的无模型反馈限制了优化的优化
Model-Free Feedback Constrained Optimization Via Projected Primal-Dual Zeroth-Order Dynamics
论文作者
论文摘要
在本文中，我们提出了一种无模型的反馈解决方案方法，以解决一般的约束优化问题，而不知道目标和约束函数的特定表述。该解决方案方法被称为“原始双零阶动力学”（P-PDZD），并基于预计的原始偶梯度动力学和超级寻求控制开发。特别是，可以将P-PDZD方法解释为无模型控制器，该控制器自主将未知系统驱动到仅使用输出反馈的优化问题解决方案。 P-PDZD可以正确处理硬构和渐近约束，并且当应用于多代理系统时，我们会开发P-PDZD的分散版本。此外，我们证明P-PDZD达到了半全球实用的渐近稳定性和结构鲁棒性。然后，我们将分散的P-PDZD应用于具有方形探测信号的功率分配系统中的最佳电压控制问题，仿真结果验证了P-PDZD方法的最佳，鲁棒性和适应性。
In this paper, we propose a model-free feedback solution method to solve generic constrained optimization problems, without knowing the specific formulations of the objective and constraint functions. This solution method is termed projected primal-dual zeroth-order dynamics (P-PDZD) and is developed based on projected primal-dual gradient dynamics and extremum seeking control. In particular, the P-PDZD method can be interpreted as a model-free controller that autonomously drives an unknown system to the solution of the optimization problem using only output feedback. The P-PDZD can properly handle both the hard and asymptotic constraints, and we develop the decentralized version of P-PDZD when applied to multi-agent systems. Moreover, we prove that the P-PDZD achieves semi-global practical asymptotic stability and structural robustness. We then apply the decentralized P-PDZD to the optimal voltage control problem in power distribution systems with square probing signals, and the simulation results verified the optimality, robustness, and adaptivity of the P-PDZD method.
