scholarly journals Soft‐constrained model predictive control based on data‐driven distributionally robust optimization

AIChE Journal ◽  
2020 ◽  
Vol 66 (10) ◽  
Author(s):  
Shuwen Lu ◽  
Jay H. Lee ◽  
Fengqi You
Keyword(s):  
Model Predictive Control ◽  
Robust Optimization ◽  
Predictive Control ◽  
Data Driven ◽  
Distributionally Robust Optimization ◽  
Constrained Model ◽  
Constrained Model Predictive Control ◽  
Distributionally Robust

STOCHASTIC MODEL PREDICTIVE CONTROL FOR SPACECRAFT RENDEZVOUS AND DOCKING VIA A DISTRIBUTIONALLY ROBUST OPTIMIZATION APPROACH

2021 ◽  
pp. 1-19
Author(s):  
ZUOXUN LI ◽  
KAI ZHANG
Keyword(s):  
Model Predictive Control ◽  
Stochastic Model ◽  
Robust Optimization ◽  
Predictive Control ◽  
Attitude Control ◽  
Optimization Approach ◽  
Distributionally Robust Optimization ◽  
Distributionally Robust ◽  
Stochastic Model Predictive Control ◽  
Rendezvous And Docking

Abstract A stochastic model predictive control (SMPC) algorithm is developed to solve the problem of three-dimensional spacecraft rendezvous and docking with unbounded disturbance. In particular, we only assume that the mean and variance information of the disturbance is available. In other words, the probability density function of the disturbance distribution is not fully known. Obstacle avoidance is considered during the rendezvous phase. Line-of-sight cone, attitude control bandwidth, and thrust direction constraints are considered during the docking phase. A distributionally robust optimization based algorithm is then proposed by reformulating the SMPC problem into a convex optimization problem. Numerical examples show that the proposed method improves the existing model predictive control based strategy and the robust model predictive control based strategy in the presence of disturbance.

Stochastic model predictive control for spacecraft rendezvous and docking via a distributionally robust optimization approach

2021 ◽  
Vol 63 ◽  
pp. 39-57
Author(s):  
Li Zuoxun ◽  
Kai Zhang
Keyword(s):  
Model Predictive Control ◽  
Stochastic Model ◽  
Robust Optimization ◽  
Predictive Control ◽  
Attitude Control ◽  
Optimization Approach ◽  
Distributionally Robust Optimization ◽  
Distributionally Robust ◽  
Stochastic Model Predictive Control ◽  
Rendezvous And Docking

A stochastic model predictive control (SMPC) algorithm is developed to solve the problem of three-dimensional spacecraft rendezvous and docking with unbounded disturbance. In particular, we only assume that the mean and variance information of the disturbance is available. In other words, the probability density function of the disturbance distribution is not fully known. Obstacle avoidance is considered during the rendezvous phase. Line-of-sight cone, attitude control bandwidth, and thrust direction constraints are considered during the docking phase. A distributionally robust optimization based algorithm is then proposed by reformulating the SMPC problem into a convex optimization problem. Numerical examples show that the proposed method improves the existing model predictive control based strategy and the robust model predictive control based strategy in the presence of disturbance.   doi:10.1017/S1446181121000031

scholarly journals On Modeling and Constrained Model Predictive Control of Open Irrigation Canals

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Lihui Cen ◽  
Ziqiang Wu ◽  
Xiaofang Chen ◽  
Yanggui Zou ◽  
Shaohui Zhang
Keyword(s):  
Model Predictive Control ◽  
Prediction Model ◽  
Predictive Control ◽  
Hyperbolic Partial Differential Equations ◽  
Irrigation Canal ◽  
Irrigation Canals ◽  
Optimal Control Algorithms ◽  
Saint Venant Equations ◽  
Constrained Model ◽  
Constrained Model Predictive Control

This paper proposes a model predictive control of open irrigation canals with constraints. The Saint-Venant equations are widely used in hydraulics to model an open canal. As a set of hyperbolic partial differential equations, they are not solved explicitly and difficult to design optimal control algorithms. In this work, a prediction model of an open canal is developed by discretizing the Saint-Venant equations in both space and time. Based on the prediction model, a constrained model predictive control was firstly investigated for the case of one single-pool canal and then generalized to the case of a cascaded canal with multipools. The hydraulic software SICC was used to simulate the canal and test the algorithms with application to a real-world irrigation canal of Yehe irrigation area located in Hebei province.

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