Antenna Azimuth Position Control using Model Reference Adaptive Controller and Self Tuning Controller

2020 ◽  
Author(s):  
Udai Singh ◽  
Nidhi Singh Pal
Keyword(s):  
Position Control ◽  
Adaptive Controller ◽  
Model Reference ◽  
Self Tuning ◽  
Model Reference Adaptive

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>

scholarly journals Design of L1 Adaptive Controller for Position Control of Permanent Magnet Linear Synchronous Motor (PMLSM)

2018 ◽  
pp. 39-52
Keyword(s):  
Adaptive Control ◽  
Permanent Magnet ◽  
Position Control ◽  
Adaptive Controller ◽  
Servo Motor ◽  
Model Reference ◽  
L1 Adaptive Control ◽  
Asymptotic Tracking ◽  
L1 Adaptive Controller ◽  
Model Reference Adaptive

In the present work, the design of an L1 adaptive controller for position control of a linear servo motor for X-Y table application has been developed. The AC Permanent Magnet Linear Synchronous Servo Motor (PMLSM) is considered. A comparative study between L1 adaptive control and Model Reference Adaptive Control (MRAC) has been made. The effectiveness of the L1 adaptive controller against uncertain parameters is analyzed based on simulated results. Robustness characteristics of both L1 adaptive controller and model reference adaptive controller to different input reference signals and different structures of uncertainty have been evaluated. The L1-adaptive controller could ensure uniformly bounded transient and asymptotic tracking for input and output signals. Simulations based on MATLAB of an x-y table based on PMLSM with time-varying friction and disturbance are presented to verify the theoretical findings. The simulation results within the environment of MATLAB/SIMULINK showed that L1-adaptive controller could give better tracking performance, dynamic and steady-state characteristics, than that obtained from MRAC for considered types of input and for various structures of uncertainties.

Model Reference Adaptive Control of Dual-Mode Micro/Macro Dynamics of Ball Screws for Nanometer Motion

1993 ◽  
Vol 115 (1) ◽  
pp. 103-108 ◽  
Author(s):  
P. I. Ro ◽  
P. I. Hubbel
Keyword(s):  
Adaptive Control ◽  
Sliding Friction ◽  
Position Control ◽  
Adaptive Controller ◽  
Model Reference Adaptive Control ◽  
Ball Screw ◽  
Model Reference ◽  
Frictional Contacts ◽  
Motion Characterization ◽  
Model Reference Adaptive

A desire to improve the positioning accuracy of ball screws prompted an investigation into the dynamics of nanometer motion. Characterization of the ball screw indicated that nanometer motion is possible prior to friction breakaway via elastic deformation of the frictional contacts while macroscopic motion involves slipping across the friction interfaces. The observed dynamics are nonlinear, and consequently result in inconsistent and unpredictable closed-loop response while under PI position control. The ball-screw can be modeled in two stages: The microdynamic stage includes “elastic” friction while the macrodynamic stage incorporates kinetic (sliding) friction. A two-stage model reference adaptive control (MRAC) strategy is adopted and a Lyapunov design technique is applied to derive the adaptive laws. Experimental results obtained via a DSP implementation of the adaptive controller indicate that the each stage of the adaptive control performs well within the respective dynamic regions, but performance deteriorates as either controller is operated near the boundary of the regions.

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.

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

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 A novel self-tuning fractional order PID control based on optimal model reference adaptive system

2019 ◽  
Vol 10 (1) ◽  
pp. 230
Author(s):  
Mohamed Abdelbar Shamseldin ◽  
Mohamed Sallam ◽  
Abdel Halim Bassiuny ◽  
A. M. Abdel Ghany
Keyword(s):  
Fractional Order ◽  
Load Frequency Control ◽  
The Other ◽  
Control Technique ◽  
Order System ◽  
Optimal Model ◽  
Model Reference ◽  
Self Tuning ◽  
Fractional Order Pid ◽  
Model Reference Adaptive

<span>This paper presents a novel self-tuning fractional order PID (FOPID) control based on optimal Model Reference Adaptive Control (MRAC). The proposed control technique has subjected to a third order system case study (power system load frequency control). The model reference describes the requirements of designer. It can be first or second order system. The parameters of MRAC have obtained using the harmony search (HS) optimization technique to achieve the optimal performance. Sometimes, the tuning of the five parameters of FOPID control online at same moment consumes more calculation time and more processing. So, this study proposes three methods for self-tuning FOPID control. The first method has been implemented to tune the two integral and derivative parameters only and the rest of parameters are fixed. The second method has been designed to adjust the proportional, integral derivative parameters while the other fractional parameters are constant. The last method has developed to adjust the five parameters of FOPID control simultaneously. The simulation results illustrate that the third method of self-tuning FOPID control can accommodate the sudden disturbance compared to other techniques. Also, it can absorb the system uncertainty better than the other control techniques.</span>

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