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.

scholarly journals Intrinsic Optimal Control for Mechanical Systems on Lie Group

2017 ◽  
Vol 2017 ◽  
pp. 1-8
Author(s):  
Chao Liu ◽  
Shengjing Tang ◽  
Jie Guo
Keyword(s):  
Optimal Control ◽  
Analytical Solution ◽  
Optimal Control Problem ◽  
Control Problem ◽  
Lie Group ◽  
Control Method ◽  
Geodesic Distance ◽  
Infinite Horizon ◽  
Mechanical Systems ◽  
Programming Approach

The intrinsic infinite horizon optimal control problem of mechanical systems on Lie group is investigated. The geometric optimal control problem is built on the intrinsic coordinate-free model, which is provided with Levi-Civita connection. In order to obtain an analytical solution of the optimal problem in the geometric viewpoint, a simplified nominal system on Lie group with an extra feedback loop is presented. With geodesic distance and Riemann metric on Lie group integrated into the cost function, a dynamic programming approach is employed and an analytical solution of the optimal problem on Lie group is obtained via the Hamilton-Jacobi-Bellman equation. For a special case on SO(3), the intrinsic optimal control method is used for a quadrotor rotation control problem and simulation results are provided to show the control performance.

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.

Comparative studies for the fractional optimal control in transmission dynamics of West Nile virus

2017 ◽  
Vol 10 (07) ◽  
pp. 1750095 ◽  
Author(s):  
N. H. Sweilam ◽  
O. M. Saad ◽  
D. G. Mohamed
Keyword(s):  
Optimal Control ◽  
West Nile Virus ◽  
Optimal Control Problem ◽  
Control Problem ◽  
Fractional Order ◽  
Comparative Studies ◽  
Control Method ◽  
Euler Method ◽  
West Nile ◽  
Iterative Optimal Control

In this paper, optimal control for a novel West Nile virus (WNV) model of fractional order derivative is presented. The proposed model is governed by a system of fractional differential equations (FDEs), where the fractional derivative is defined in the Caputo sense. An optimal control problem is formulated and studied theoretically using the Pontryagin maximum principle. Two numerical methods are used to study the fractional-order optimal control problem. The methods are, the iterative optimal control method (OCM) and the generalized Euler method (GEM). Positivity, boundedness and convergence of the IOCM are studied. Comparative studies between the proposed methods are implemented, it is found that the IOCM is better than the GEM.

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