Desired Terminal State Concept in Model Predictive Control: A Case Study

The paper deals with an online optimization control method for dynamical processes called Model Predictive Control (MPC). It is a popular control method in industry and frequently treated in academic areas as well. The standard predictive controllers usually do not guarantee stability especially for the case of short horizons and large control error penalization. Terminal state is one way to ensure stability or at least increase the controller robustness. In the paper, deviation of the predicted terminal state from the desired terminal state is considered as one term of the cost function. Effect of the stability and control quality is demonstrated in the simulated experiments. The application area for online optimization methods is very broad including various logistics and transport problems. If the dynamics of the controlled processes cannot be neglected, the optimization problem must be solved not only for steady state but also for transient behaviour, e.g., by MPC.

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Dual Quaternion Based Close Proximity Operation for In-Orbit Assembly via Model Predictive Control

International Journal of Aerospace Engineering ◽

10.1155/2021/1305095 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Chuqi Sun ◽

Yan Xiao ◽

Zhaowei Sun ◽

Dong Ye

Keyword(s):

Control System ◽

Model Predictive Control ◽

Predictive Control ◽

Coupling Effect ◽

Suboptimal Control ◽

Dual Quaternion ◽

Close Proximity ◽

The Stability ◽

Six Degree Of Freedom ◽

And Control

This paper studies the problem of guidance and control for autonomous in-orbit assembly. A six-degree-of-freedom (6-DOF) motion control for in-orbit assembly close proximity operation between a service satellite and a target satellite is addressed in detail. The dynamics based on dual quaternion are introduced to dispose the coupling effect between translation and rotation in a succinct frame, in which relevant perturbation and disturbance are involved. With the consideration of economical principle for fuel consume, a generic control system based on model predictive control (MPC) is then designed to generate a suboptimal control sequence for rendezvous trajectory considering actuator output saturation. The stability and robustness issues of the MPC-based control system are analyzed and proved. Numerical simulations are presented to demonstrate the effectiveness and robustness of the proposed control scheme, while additional comparisons for diverse horizons of the MPC are further conducted.

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Dynamic traffic light optimization and Control System using model-predictive control method

2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC) ◽

10.1109/itsc.2016.7795937 ◽

2016 ◽

Cited By ~ 2

Author(s):

You-Ren Chen ◽

Keng-Pin Chen ◽

Pao-Ann Hsiungy

Keyword(s):

Control System ◽

Model Predictive Control ◽

Predictive Control ◽

Control Method ◽

Dynamic Traffic ◽

Traffic Light ◽

Optimization And Control ◽

And Control

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Application of Neural Network for Nonlinear Predictive Control

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.562-564.1964 ◽

2012 ◽

Vol 562-564 ◽

pp. 1964-1967 ◽

Cited By ~ 1

Author(s):

Zhi Cheng Xu ◽

Bin Zhu ◽

Qing Bin Jiang

Keyword(s):

Neural Network ◽

Model Predictive Control ◽

Linear Model ◽

Predictive Control ◽

Control Method ◽

Nonlinear Modeling ◽

Nonlinear Process ◽

Effective Control ◽

Nonlinear Predictive Control ◽

The Stability

A novel model predictive control method was proposed for a class of dynamic processes with modest nonlinearities in this paper. In this method, a diagonal recurrent neural network (DRNN) is used to compensate nonlinear modeling error that is caused because linear model is regarded as prediction model of nonlinear process. It is aimed at offsetting the effect of model mismatch on the control performance, strengthening the robustness of predictive control and the stability of control system. Under a certain assumption condition, linear model predictive control method is extended to nonlinear process, which doesn’t need solve nonlinear optimization problem. Consequently, the computational efforts are reduced drastically. The simulation example shows that the proposed method is an effective control strategy with excellent tracing characteristics and strong robustness.

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A weighted voltage model predictive control method for a virtual synchronous generator with enhanced parameter robustness

Protection and Control of Modern Power Systems ◽

10.1186/s41601-021-00217-8 ◽

2021 ◽

Vol 6 (1) ◽

Author(s):

Leilei Guo ◽

Zhiye Xu ◽

Nan Jin ◽

Yanyan Li ◽

Wei Wang

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Output Voltage ◽

Control Method ◽

Synchronous Generator ◽

Experimental Tests ◽

Tracking Accuracy ◽

The Stability ◽

Parameter Robustness ◽

Virtual Synchronous Generator

AbstractTo address the problem of insufficient system inertia and improve the power quality of grid-connected inverters, and to enhance the stability of the power system, a method to control a virtual synchronous generator (VSG) output voltage based on model predictive control (MPC) is proposed. Parameters of the inductors, capacitors and other components of the VSG can vary as the temperature and current changes. Consequently the VSG output voltage and power control accuracy using the conventional MPC method may be reduced. In this paper, to improve the parameter robustness of the MPC method, a new weighted predictive capacitor voltage control method is proposed. Through detailed theoretical analysis, the principle of the proposed method to reduce the influence of parameter errors on voltage tracking accuracy is analyzed. Finally, the effectiveness and feasibility of the proposed method are verified by experimental tests using the Typhoon control hardware-in-the-loop experimental platform.

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