Design of Refined Grey Prediction Controller

Tong, Chia-Chang; Barbieri, Enrique

doi:10.1109/icsmc.2006.384478

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…where n is the size of the data. One can create a sequence to describe the variation of the information by using fewer (at least four) data points [8,9]. Reference [10] choose n to be 5, where more computations will be required if n is greater than that.…”

Section: Theory Of Grey-predictormentioning

confidence: 99%

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Design of Speed-Controller for Brushless DC-Motor Based on Grey Predictor-PID Controller

Ali¹

2018

ETJ

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Brushless direct current (BLDC) motor is a permanent-magnet synchronous motor but the commutator and the brushes are replaced by electronic commutation system. This motor is characterized by many advantages compared with other motors and it being used in a wide range of applications. In BLDC motor, speed control plays an essential role; the speed-controller has to respond rapidly to command changes and to offer enough robustness against the variations of the mechanical load. In this paper, the design of Grey-PID controller is proposed as speed controller of BLDC motor where a new adaptation algorithm is suggested to update the parameters of the adaptive PID controller. The proposed algorithm takes advantages of both of Grey model GM(1,1) and gradient-descent method. The Grey-PID speed control system is simulated by using Matlab\Simulink environment. The simulation results confirm that the proposed controller performs better than the traditional PID controller. By using Grey-PID controller, starting the motor or sudden change of speed can be performed with short rise and settling times, no overshoot and no steady-state error. The proposed controller, also, has a great ability to restrain the fluctuation of speed caused by load disturbance.

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Section: Theory Of Grey-predictormentioning

confidence: 99%

“…At the next sampling time, Ω (0) will be: Ω (0) = (Ω (0) (2), Ω (0) (3), , , , Ω (0) ( + 1)) and so on [5,8]. B-Obtain the accumulated-generating-operation (AGO) of information sequence [8,9]:…”

Section: Theory Of Grey-predictormentioning

confidence: 99%

Design of Speed-Controller for Brushless DC-Motor Based on Grey Predictor-PID Controller

Ali¹

2018

ETJ

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“…In this paper, we build a dynamic model called the grey model GM(n, h) [8], [9] to approximate the system. GM (1, 1) can be described as follows:…”

Section: Grey Predictionmentioning

confidence: 99%

Output-regulated optimal grey controller

Huang

Liu

Chang

et al. 2010

Journal of Information and Optimization Sciences

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In this paper, we propose a new method to design a grey prediction system controller using output feedback. The optimal reduced order model will be used to retain the physical meaning of the output states. By using only the output states feedback, the control strategy can be implemented easily. The grey prediction method adopts the forecasting information from the output state variables to control system behavior. As a result, it reduces the oscillation and enhances the dynamic stability of the system. The advantages of the proposed method are verified through a detailed simulation of a multi-machine power system. * Responses of the system with the proposed method and full state feedback optimal control are included for comparative analyses. In conclusion, the effectiveness of the grey prediction system controller in enhancing the dynamic performance stability is much better than the traditional optimal control methods.

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“…In order to obtain a fast system response with little overshoot, the step size of the grey predictor should be changed adaptively. In the literature on grey system theory, there are some methods that tune the step size of the grey predictor according to the input state of the system (Wong and Liang, 1997). In order to determine the appropriate forecasting step size, some online rule-tuning algorithms using a fuzzy inference system have been proposed for the control of an inverted pendulum, a fuzzy tracking method for a mobile robot and non-minimum phase systems (Feng and Wong, 2002;Wong and Liang, 1997;Wong et al, 2001).…”

Section: Introductionmentioning

confidence: 99%

An adaptive grey PID-type fuzzy controller design for a non-linear liquid level system

Kayacan

Kaynak

2009

Transactions of the Institute of Measurement and Control

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Product-sum-type fuzzy controllers are known to have similar characteristics to PD-type controllers. In the case of type-0 control systems, PID-type fuzzy controllers have been proposed in the literature in order to eliminate the steady-state error. However, these control methods, essentially based on conventional PID theory, have no predictive capabilities. The concept of grey system theory, which has a certain prediction capability, offers an alternative approach for various kinds of conventional control methods, such as PID control and fuzzy control. This paper proposes a grey prediction-based fuzzy PID controller that can overcome the stated shortcomings. In order to obtain a better controller performance, another fuzzy controller is designed to change the step size of the grey predictor. A non-linear liquid level system is taken as a test bed. The grey model developed is examined under several different conditions and it is shown that the proposed grey fuzzy PID controller can predict the future output value of the system. It is clear that the proposed adaptive PID-type fuzzy controller is effective in controlling such a non-linear system accurately by changing the step size adaptively for real-time working.

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Design of Refined Grey Prediction Controller

Cited by 5 publications

References 10 publications

Design of Speed-Controller for Brushless DC-Motor Based on Grey Predictor-PID Controller

Design of Speed-Controller for Brushless DC-Motor Based on Grey Predictor-PID Controller

Output-regulated optimal grey controller

An adaptive grey PID-type fuzzy controller design for a non-linear liquid level system

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