Chance-Constrained Model Predictive Control A reformulated approach suitable for sewer networks
원문 초록
In this work, a revised formulation of Chance-Constrained (CC) Model Predictive Control (MPC) is presented. The focus of this work is on the mathematical formulation of the revised CC-MPC, and the reason behind the need for its revision. The revised formulation is given in the context of sewer systems, and their weir overflow structures. A linear sewer model of the Astlingen Benchmark sewer model is utilized to illustrate the application of the formulation, both mathematically and performance-wise through simulations. Based on the simulations, a comparison of performance is done between the revised CC-MPC and a comparable deterministic MPC, with a focus on overflow avoidance, computation time, and operational behavior. The simulations show similar performance for overflow avoidance for both types of MPC, while the computation time increases slightly for the CC-MPC, together with operational behaviors getting limited
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Differentiable Nonlinear Model Predictive Control
Evaluating the Impact of Model Accuracy for Optimizing Battery Energy Storage Systems
Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning
Deep Operator Neural Network Model Predictive Control
Knot So Simple: A Minimalistic Environment for Spatial Reasoning
Efficient Configuration-Constrained Tube MPC via Variables Restriction and Template Selection
토론
이 논문과 연결된 포럼 글이 아직 없습니다.