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

1991 ◽  
Vol 36 (6) ◽  
pp. 683-691 ◽  
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

Synthesis Design and Analysis of a Hybrid Controller for Robotic Arms

2016 ◽  
Author(s):  
Dan Zhang ◽  
Bin Wei
Keyword(s):  
Pid Controller ◽  
Adaptive Controller ◽  
Convergence Speed ◽  
Model Reference ◽  
Robotic Arms ◽  
Hybrid Controller ◽  
Comparison Results ◽  
Convergence Performance ◽  
Model Reference Adaptive ◽  
Better Than

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.

Evaluation of P-Q Controllers for an Injection Moulding Machine

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.

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

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>

Sign in / Sign up

Export Citation Format

Share Document