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.
We consider fixed-interval smoothing problems for counter-adversarial autonomous systems. An adversary deploys an autonomous filtering and control system that i) measures our current state via a noisy sensor, ii) computes a posterior estimate (belief) and iii) takes an action that we can observe. Based on such observed actions and our knowledge of our state sequence, we aim to estimate the adversary's past and current beliefs -- this forms a foundation for predicting, and counteracting against, future actions. We derive the optimal smoother for the adversary's beliefs (we treat the problem in …
Robust Filtering and Smoothing with Gaussian Processes
University of Washington（华盛顿大学）；University of Cambridge（剑桥大学）；AGT Group (R&D) GmbH（AGT集团（研发）有限公司）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Max Planck Institute for Biological Cybernetics（马克斯·普朗克生物控制论研究所）
High penetration of solar power introduces new challenges in the operation of distribution systems. Considering the highly volatile nature of solar power output due to changes in cloud coverage, maintaining the power balance and operating within ramp rate limits can be an issue. Great benefits can be brought to the grid by smoothing solar power output at individual sites equipped with flexible resources such as electrical vehicles and battery storage systems. This paper proposes several approaches to a solar smoothing application by utilizing battery storage and EV charging control in a "Nanog…
LP-MPPI: Low-Pass Filtering for Efficient Model Predictive Path Integral Control
Institute of Robotics and Machine Intelligence, Poznan University of Technology, Poznan, Poland（机器人与机器智能研究所，波兹南技术大学，波兰波兹南）；IDEAS Research Institute, Warsaw, Poland（IDEAS研究所，波兰华沙）
