scholarly journals Polynomial chaos explicit solution of the optimal control problem in Model Predictive Control

2017 ◽  
Author(s):  
T. Lefebvre ◽  
F. De Belie ◽  
G. Crevecoeur
Keyword(s):  
Optimal Control ◽  
Optimal Control Problem ◽  
Model Predictive Control ◽  
Control Problem ◽  
Predictive Control ◽  
Explicit Solution ◽  
Polynomial Chaos

scholarly journals Self-Triggered Model Predictive Control for Perturbed Underwater Robot Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zhongxian Xu ◽  
Lile He ◽  
Ning He ◽  
Lipeng Qi
Keyword(s):  
Optimal Control ◽  
Optimal Control Problem ◽  
Model Predictive Control ◽  
Control Problem ◽  
Predictive Control ◽  
Information Transfer ◽  
Control Method ◽  
Kinematic Model ◽  
Underwater Robot ◽  
Robot Systems

Aiming at solving the control problem of a constrained and perturbed underwater robot, a control method was proposed by combining the self-triggered mechanism and the nonlinear model predictive control (NMPC). The theoretical properties of the kinematic model of the underwater robot, as well as the corresponding MPC controller, are first studied. Then, a novel technique for determining the next update moment of both the optimal control problem and the system state is developed. It is further rigorously proved that the proposed algorithm can (1) stabilize the closed-loop underwater robot system, (2) reduce the time of solving the optimal control problem and (3) save the information transfer resources. Finally, a case study is provided to show the effectiveness of the developed researched scheme.

Choosing the number of time intervals for solving a model predictive control problem of nonlinear systems

2021 ◽  
pp. 014233122110073
Author(s):  
Jasem Tamimi
Keyword(s):  
Optimal Control ◽  
Optimal Control Problem ◽  
Model Predictive Control ◽  
Control Problem ◽  
Predictive Control ◽  
Optimization Problem ◽  
The State ◽  
Nonlinear Program ◽  
Box Constraints ◽  
Time Intervals

Model predictive control (MPC) is a control strategy that can handle state and control multi-variables at same time. To use the MPC using direct methods for solving the a dynamic optimization problem, one needs, for example, to transform the optimization problem into a nonlinear programming (NLP) problem by dividing the prediction horizon into equal time intervals. In this work, we suggest a tool and procedures for helping to choose a ‘compromise’ number of time intervals with a needed accuracy, objective cost, number of turned NLP iterations and computational time. On the other hand, we offer a simplified nonlinear program to ensure the convergence of a class of finite optimal control problem by modifying the state box constraints. In particular, a special type of box constraints were used to the constrained optimal control problem to enforce the state trajectories to reach the desired stationary point. These box constraints are characterized by some parameters that are easily optimized by our proposed nonlinear program. Our proposed tools are tested using two case studies; nonlinear continuous stirred tank reactor (CSTR) and nonlinear batch reactor.

An Economic Model Predictive Control Approach for Wind Power Smoothing and Tower Load Mitigation

2018 ◽  
Author(s):  
Mohamed M. Alhneaish ◽  
Mohamed L. Shaltout ◽  
Sayed M. Metwalli
Keyword(s):  
Optimal Control ◽  
Optimal Control Problem ◽  
Model Predictive Control ◽  
Wind Power ◽  
Predictive Control ◽  
Economic Model ◽  
Control Algorithm ◽  
Fatigue Load ◽  
Economic Model Predictive Control ◽  
Control Framework

An economic model predictive control framework is presented in this study for an integrated wind turbine and flywheel energy storage system. The control objective is to smooth wind power output and mitigate tower fatigue load. The optimal control problem within the model predictive control framework has been formulated as a convex optimal control problem with linear dynamics and convex constraints that can be solved globally. The performance of the proposed control algorithm is compared to that of a standard wind turbine controller. The effect of the proposed control actions on the fatigue loads acting on the tower and blades is studied. The simulation results, with various wind scenarios, showed the ability of the proposed control algorithm to achieve the aforementioned objectives in terms of smoothing output power and mitigating tower fatigue load at the cost of a minimal reduction of the wind energy harvested.

An Economic Model Predictive Control Approach for Wind Power Smoothing and Tower Load Mitigation

2020 ◽  
Vol 142 (6) ◽  
Author(s):  
Mohamed L. Shaltout ◽  
Mohamed M. Alhneaish ◽  
Sayed M. Metwalli
Keyword(s):  
Optimal Control ◽  
Energy Storage ◽  
Optimal Control Problem ◽  
Model Predictive Control ◽  
Wind Power ◽  
Predictive Control ◽  
Economic Model ◽  
Control Algorithm ◽  
Fatigue Load ◽  
Economic Model Predictive Control

Abstract Wind power intermittency represents one of the major challenges facing the future growth of grid-connected wind energy projects. The integration of wind turbines and energy storage systems (ESS) provides a viable approach to mitigate the unfavorable impact on grid stability and power quality. In this study, an economic model predictive control (MPC) framework is presented for an integrated wind turbine and flywheel energy storage system (FESS). The control objective is to smooth wind power output and mitigate tower fatigue load. The optimal control problem within the model predictive control framework has been formulated as a convex optimal control problem with linear dynamics and convex constraints that can be solved globally. The performance of the proposed control algorithm is compared to that of a baseline wind turbine controller. The effect of the proposed control actions on the fatigue loads acting on the tower and blades is investigated. The simulation results, with various wind scenarios, showed the ability of the proposed control algorithm to achieve the aforementioned objectives in terms of smoothing output power and mitigating tower fatigue load with negligible effect on the wind energy harvested.

Experimental Study on Optimal Tracking Control of a Micro Ground Vehicle

2017 ◽  
Vol 29 (4) ◽  
pp. 757-765 ◽  
Author(s):  
Soichiro Watanabe ◽  
◽  
Masanori Harada
Keyword(s):  
Experimental Study ◽  
Optimal Control ◽  
Optimal Control Problem ◽  
Control Problem ◽  
Predictive Control ◽  
Tracking Control ◽  
Reference Trajectory ◽  
Ground Vehicle ◽  
Optimal Tracking ◽  
Tracking Controller

This paper investigates the application of optimal control to a micro ground vehicle (MGV) experimentally. The model predictive control (MPC) technique is used for the overall tracking controller during the maneuver. The reference trajectory for MPC is preliminarily obtained by numerical computation of the optimal control problem, which is prescribed as a minimum-time maneuver. The results provide nominal tracking performance and validate the feasibility of the approach.

A distributed model predictive control approach to cooperative adaptive cruise control of the heterogeneous platoon

2021 ◽  
pp. 095440702110705
Author(s):  
Guangming Nie ◽  
Bo Xie ◽  
Zixu Hao ◽  
Hangwei Hu ◽  
Yantao Tian
Keyword(s):  
Optimal Control ◽  
Model Predictive Control ◽  
Control Problem ◽  
Predictive Control ◽  
Control Algorithm ◽  
Sampling Period ◽  
Distributed Model ◽  
Cruise Control ◽  
Control Approach ◽  
Distributed Model Predictive Control

This paper presents a distributed model predictive control algorithm to solve the cruise control problem of a heterogeneous platoon. Each following vehicle in the platoon can use the communication equipment to receive the information of the leading vehicle and its preceding adjacent one. The vehicles in the platoon are dynamically decoupled and have different dynamic parameters. Each vehicle solves a local optimal control problem independently. The cost function of each vehicle’s local optimal control algorithm is designed with traceability as the control objective, and its asymptotic stability is guaranteed by using the terminal constraint method. In addition, the timestamps of all vehicles in the platoon are synchronous, which means that in each sampling period, a specific vehicle in the platoon cannot obtain the solution results of other vehicles’ local optimal control problems at the current sampling moment. Under this restriction, the constraints that each vehicle needs to meet to realize the platoon’s string stability are also designed. Finally, the simulation results show the effectiveness of the algorithm.

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