A new reactive-power-based Model Reference Adaptive Controller for online rotor time constant identification of induction motors

In this paper, a hybrid controller for robotic arms is proposed and designed by combining a proportional-integral-derivative controller (PID) and a model reference adaptive controller (MRAC) in order to further improve the accuracy and joint convergence speed performance. The convergence performance of the PID controller, the model reference adaptive controller and the PID+MRAC hybrid controller for 1-DOF and 2-DOF manipulators are compared. The comparison results show that the convergence speed and its performance for the MRAC and the PID+MRAC controllers are better than that of the PID controller, and the convergence performance for the hybrid control is better than that of the MRAC control.

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The development of a model reference adaptive controller to control the knee joint of paraplegics

IEEE Transactions on Automatic Control ◽

10.1109/9.86942 ◽

1991 ◽

Vol 36 (6) ◽

pp. 683-691 ◽

Cited By ~ 50

Author(s):

M.S. Hatwell ◽

B.J. Oderkerk ◽

C.A. Sacher ◽

G.F. Inbar

Keyword(s):

Knee Joint ◽

Adaptive Controller ◽

Model Reference ◽

Model Reference Adaptive

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Evaluation of P-Q Controllers for an Injection Moulding Machine

10.1115/imece2000-1971 ◽

2000 ◽

Author(s):

Paul K. Guerrier ◽

Kevin A. Edge

Keyword(s):

Injection Moulding ◽

Adaptive Controller ◽

Pi Controller ◽

Hardware In The Loop ◽

Model Reference ◽

Industry Standard ◽

Moulding Machine ◽

Single Controller ◽

Pressure Controller ◽

Model Reference Adaptive

Abstract There are a number of problems surrounding traditional velocity and pressure controllers used on injection moulding machines. Injection moulding machines are also very expensive and full scale testing is often not appropriate at the beginning of new controller evaluation. This paper presents results for a half scale ‘hardware-in-the-loop’ load emulation of the filling and packing phases of injection moulding, suitable for controller evaluation. The problems linked to the current industry standard velocity and pressure controller are discussed along with alternative strategies. Schemes including single controller fuzzy logic and neural network solutions are discussed and ruled out in favour of ones containing separate velocity and pressure controllers. Results for a model reference adaptive pressure controller are presented and compared with those obtained using a closed loop PI controller experimentally and in simulation. Experimentally the model reference adaptive controller outperforms the PI controller but does suffer from gain drift.

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Experimental simplified rule of self tuning fuzzy logic-model reference adaptive speed controller for induction motor drive

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v20.i3.pp1653-1664 ◽

2020 ◽

Vol 20 (3) ◽

pp. 1653

Author(s):

M.Z. Ismail ◽

M.H.N. Talib ◽

Z. Ibrahim ◽

J. Mat Lazi ◽

Z. Rasin

Keyword(s):

Fuzzy Logic ◽

Induction Motor ◽

Fuzzy Logic Controller ◽

Adaptive Controller ◽

Tuning Parameter ◽

Model Reference ◽

Wide Range ◽

Speed Performance ◽

Self Tuning ◽

Model Reference Adaptive

<span>Fuzzy logic controller (FLC) has shown excellent performance in dealing with the non-linearity and complex dynamic model of the induction motor. However, a conventional constant parameter FLC (CPFL) will not be able to provide–good coverage performance for a wide speed range operation with a single tuning parameter. Therefore, this paper proposed a self tuning mechanism FLC approach by model reference adaptive controller (ST-MRAC) to continuously allow to adjust the parameters. Due to real time hardware application, the dominant rules selection method for simplified rules has been implemented as part of the reducing computational burden. Experiment results validate a good performance of the ST-MRAC compared to the CPFL for the speed performance in terms of the wide range of operations and disturbance showed remarkable performance.</span>

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Fuzzy-based Gain Adaptive Scheme for Set-Point Modulated Model Reference Adaptive Controller

International Journal of Natural Computing Research ◽

10.4018/ijncr.2018100101 ◽

2018 ◽

Vol 7 (4) ◽

pp. 1-19

Author(s):

A. K. Pal ◽

Indrajıt Naskar ◽

Sampa Paul

Keyword(s):

Adaptive Controller ◽

Process Parameter ◽

System Stability ◽

Modulation Scheme ◽

Model Reference ◽

Set Point ◽

Automatic Selection ◽

Parameter Variations ◽

Model Reference Adaptive ◽

Selection Of

In model reference adaptive controller (MRACs), the adaptive gain of the controller is varied according to the process dynamic variation as it is directly related with the system stability. In MRAC, there is no provision of an automatic selection of adaptive gain and adaptation rate. To get rid of this problem and for the automatic selection of adaptive gain, a fuzzy-based scheme is presented in this article. In the proposed fuzzy-based technique, the controller output gain is illustrated as the function of input process parameters, which is continuously amended for any process parameter variations. A set-point modulation scheme is also incorporated to tackle the undesired process parameter variations and to improve performance indices of the process under control. The performance of the proposed set-point modulated fuzzy-based model reference adaptive controller (SFMRAC) is demonstrated on different second order linear, marginally stable and nonlinear models. The scheme is also explored on a real-time twin arm overhead crane for transport of material without pendulation.

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