Multi-Rate Generalized Predictive Control for Multi-Variable Systems

1993 ◽  
Vol 207 (4) ◽  
pp. 253-260
Author(s):  
I O Park ◽  
J H Oh
Keyword(s):  
Process Control ◽  
Predictive Control ◽  
Control Algorithm ◽  
Model Identification ◽  
Measurement Noise ◽  
Noise Model ◽  
Generalized Predictive Control ◽  
Identification Algorithm ◽  
Stochastic Framework ◽  
And Control

The purpose of this paper is to drive the adaptive multi-rate generalized predictive control for multi-variable systems in a stochastic framework. Modelling disturbances as white noise is inadequate for process control because most disturbances encountered in process control are coloured or non-stationary in nature. For that reason a stochastic parallel model identification algorithm for a multi-rate-sampled system is proposed. No attempt is made to identify the noise model. Hence the algorithm is applicable to any measurement noise case. The measurement noise can be arbitrary (for example coloured or non-stationary noise), except for the assumption that it and control inputs are stochastically uncorrelated. Then the control algorithm based on the generalized predictive control is proposed. In order to demonstrate the effectiveness of the proposed control algorithm a simulation study is carried out. The closed-loop performances are excellent.

The Research of Air Precooling System Based on Generalize Predictive Control Strategy

2013 ◽  
Vol 433-435 ◽  
pp. 1091-1098
Author(s):  
Wei Bo Yu ◽  
Cui Yuan Feng ◽  
Ting Ting Yang ◽  
Hong Jun Li
Keyword(s):  
Predictive Control ◽  
Control Strategy ◽  
Control Algorithm ◽  
Exchange Process ◽  
Generalized Predictive Control ◽  
Matlab Simulation ◽  
Control Effect ◽  
Heat Exchange Process ◽  
Complex Control System ◽  
And Control

The air precooling system heat exchange process is a complex control system with features such as: nonlinear, lag and random interference. So choose Generalized Predictive Control Algorithm that has low model dependence, good robustness and control effect, as well as easy to implement. But due to the large amount of calculation of traditional generalized predictive control and can't juggle quickness and overshoot problem, an improved generalized predictive control algorithm is proposed, then carry out the MATLAB simulation, the experimental results show that the algorithm can not only greatly reduce the amount of computation, but also can restrain the overshoot and its rapidity.

The Theory and Experiments of Recursive Generalized Predictive Control

2002 ◽  
Author(s):  
Suk-Min Moon ◽  
Robert L. Clark ◽  
Daniel G. Cole
Keyword(s):  
System Identification ◽  
Predictive Control ◽  
Control Algorithm ◽  
Controller Design ◽  
Sampling Rate ◽  
Control Design ◽  
Weighting Factor ◽  
Generalized Predictive Control ◽  
Prediction Horizon ◽  
And Control

The recursive generalized predictive control algorithm, combining the process of system identification and the process of the controller design, is presented. In the control design process, there are three parameters to be chosen: the prediction horizon, the control horizon, and the input weighting factor. Two new parameters are defined for the practical choice of the prediction horizon and the control horizon. A time varying algorithm for the input weighting factor and a dual-sampling-rate algorithm between system identification and control design are presented. The recursive generalized predictive control algorithm is applied to two different systems: a sound enclosure and an optical jitter suppression testbed.

On a generalized predictive control algorithm

1987 ◽  
Vol 9 (5) ◽  
pp. 369-377 ◽  
Author(s):  
R Gorez ◽  
V Wertz ◽  
Zhu Kuan-Yi
Keyword(s):  
Predictive Control ◽  
Control Algorithm ◽  
Generalized Predictive Control

scholarly journals Research on Multi-Objective Optimization and Control Algorithms for Automatic Train Operation

Energies ◽  
2019 ◽  
Vol 12 (20) ◽  
pp. 3842 ◽  
Author(s):  
Kai-wei Liu ◽  
Xing-Cheng Wang ◽  
Zhi-hui Qu
Keyword(s):  
Predictive Control ◽  
Control Algorithm ◽  
Lower Layer ◽  
Target Speed ◽  
Speed Controller ◽  
Urban Rail ◽  
Train Operation ◽  
Automatic Train Operation ◽  
And Control ◽  
Layer Control

The automatic train operation (ATO) system of urban rail trains includes a two-layer control structure: upper-layer control and lower-layer control. The upper-layer control is to optimize the target speed curve of ATO, and the lower-layer control is the tracking by the urban rail train of the optimal target speed curve generated by the upper-layer control according to the tracking control strategy of ATO. For upper-layer control, the multi-objective model of urban rail train operation is firstly built with energy consumption, comfort, stopping accuracy, and punctuality as optimization indexes, and the entropy weight method is adopted to solve the weight coefficient of each index. Then, genetic algorithm (GA) is used to optimize the model to obtain an optimal target speed curve. In addition, an improved genetic algorithm (IGA) based on directional mutation and gene modification is proposed to improve the convergence speed and optimization effect of the algorithm. The stopping and speed constraints are added into the fitness function in the form of penalty function. For the lower-layer control, the predictive speed controller is designed according to the predictive control principle to track the target speed curve accurately. Since the inflection point area of the target speed curve is difficult to track, the softness factor in the predictive model needs to be adjusted online to improve the control accuracy of the speed. For this paper, we mainly improve the optimization and control algorithms in the upper and lower level controls of ATO. The results show that the speed controller based on predictive control algorithm has better control effect than that based on the PID control algorithm, which can meet the requirements of various performance indexes. Thus, the feasibility of predictive control algorithm in an ATO system is also verified.

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