论文标题
基于约束感知粒子过滤/平滑的非线性模型预测控制
Nonlinear Model Predictive Control Based on Constraint-Aware Particle Filtering/Smoothing
论文作者
论文摘要
非线性模型预测控制（NMPC）在许多应用中都广泛使用。它的表述传统上涉及重复解决在线非线性约束优化问题。在本文中，我们通过贝叶斯估计的镜头研究了NMPC，并强调蒙特卡洛采样方法可以提供一种有利的实施NMPC的方法。我们开发一种约束意识的粒子过滤/平滑方法，并利用它来实现NMPC。即使对于复杂的非线性系统，基于新的采样的NMPC算法也可以轻松有效地执行，同时可能会缓解传统研究中数值优化面临的计算复杂性和局部最小值的问题。通过模拟研究评估了所提出的算法的有效性。
Nonlinear model predictive control (NMPC) has gained widespread use in many applications. Its formulation traditionally involves repetitively solving a nonlinear constrained optimization problem online. In this paper, we investigate NMPC through the lens of Bayesian estimation and highlight that the Monte Carlo sampling method can offer a favorable way to implement NMPC. We develop a constraint-aware particle filtering/smoothing method and exploit it to implement NMPC. The new sampling-based NMPC algorithm can be executed easily and efficiently even for complex nonlinear systems, while potentially mitigating the issues of computational complexity and local minima faced by numerical optimization in conventional studies. The effectiveness of the proposed algorithm is evaluated through a simulation study.
