Stochastic model predictive control method for microgrid management

Advanced driver assistance control faces great challenges in cooperating with the nearby vehicles. The assistance controller of an intelligent vehicle has to provide control efforts properly to prevent possible collisions without interfering with the drivers. This paper proposes a novel driver assistance control method for intelligent ground vehicles to cooperate with the nearby vehicles, using the stochastic model predictive control algorithm. The assistance controller is designed to correct the drivers’ steering maneuvers when there is a risk of possible collisions, so that the drivers are not interfered. To enhance the cooperation between the vehicles, the nearby vehicle motion is predicted and included in the assistance controller design. The position uncertainties of the nearby vehicle are considered by the stochastic model predictive control approach via chance constraints. Simulation studies are conducted to validate the proposed control method. The results show that the assistance controller can help the drivers avoid possible collisions with the nearby vehicles and the driving safety can be guaranteed.

Download Full-text

Stochastic model predictive control for wind turbines with doubly fed induction generators

2016 IEEE Power and Energy Society General Meeting (PESGM) ◽

10.1109/pesgm.2016.7741108 ◽

2016 ◽

Author(s):

Peng Kou ◽

Deliang Liang ◽

Lin Gao

Keyword(s):

Model Predictive Control ◽

Stochastic Model ◽

Predictive Control ◽

Wind Turbines ◽

Induction Generators ◽

Doubly Fed Induction Generators ◽

Doubly Fed ◽

Stochastic Model Predictive Control

Download Full-text

Reference tracking stochastic model predictive control over unreliable channels and bounded control actions

Automatica ◽

10.1016/j.automatica.2021.109512 ◽

2021 ◽

Vol 127 ◽

pp. 109512

Author(s):

Prabhat K. Mishra ◽

Sanket S. Diwale ◽

Colin N. Jones ◽

Debasish Chatterjee

Keyword(s):

Model Predictive Control ◽

Stochastic Model ◽

Predictive Control ◽

Bounded Control ◽

Reference Tracking ◽

Control Actions ◽

Stochastic Model Predictive Control

Download Full-text

Stochastic Model Predictive Control of Energy Management Systems with Human in the Loop

2020 IEEE 9th Global Conference on Consumer Electronics (GCCE) ◽

10.1109/gcce50665.2020.9291914 ◽

2020 ◽

Author(s):

Shogo Shimamoto ◽

Koichi Kobayashi ◽

Yuh Yamashita

Keyword(s):

Model Predictive Control ◽

Stochastic Model ◽

Energy Management ◽

Predictive Control ◽

Management Systems ◽

Human In The Loop ◽

Energy Management Systems ◽

Stochastic Model Predictive Control

Download Full-text

Energy Management of the Power-Split Hybrid Electric City Bus Based on the Stochastic Model Predictive Control

IEEE Access ◽

10.1109/access.2020.3047113 ◽

2021 ◽

Vol 9 ◽

pp. 2055-2071

Author(s):

Rong Yang ◽

Xiaohu Yang ◽

Wei Huang ◽

Song Zhang

Keyword(s):

Model Predictive Control ◽

Stochastic Model ◽

Energy Management ◽

Predictive Control ◽

City Bus ◽

Power Split ◽

Hybrid Electric ◽

Stochastic Model Predictive Control

Download Full-text

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

The ANZIAM Journal ◽

10.1017/s1446181121000031 ◽

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.

Download Full-text