Experimental Studies on Model Reference Adaptive Control with Integral Action Employing a Rotary Encoder and Tachometer Sensors

Wu, Guoqiang; Wu, Shunan; Bai, Yuguang; Liu, Lei

doi:10.3390/s130404742

Cited by 15 publications

(5 citation statements)

References 11 publications

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“…Then, the PID output is used to adapt

for gathering the all-out wind power while dealing with several challenges such as the system uncertainty and the external disturbances. The configuration of the MRAC is based on three main subsystems, namely adaptive PID controller, reference model and adaptive mechanism [ 58 , 59 ], as depicted in Figure 6 .…”

Section: Mrac Strategymentioning

confidence: 99%

Robust Adaptive HCS MPPT Algorithm-Based Wind Generation System Using Model Reference Adaptive Control

Alrowaili

Ali

Youssef

et al. 2021

Sensors

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To treat the stochastic wind nature, it is required to attain all available power from the wind energy conversion system (WECS). Therefore, several maximum power point tracking (MPPT) techniques are utilized. Among them, hill-climbing search (HCS) techniques are widely implemented owing to their various features. Regarding current HCS techniques, the rotor speed is mainly perturbed using predefined constants or objective functions, which makes the selection of step sizes a multifaceted task. These limitations are directly reflected in the overall dynamic WECS performance such as tracking speed, power fluctuations, and system efficiency. To deal with the challenges of the existing HCS techniques, this paper proposes a new adaptive HCS (AD-HCS) technique with self-adjustable step size using model reference adaptive control (MRAC) based on the PID controller. Firstly, the mechanical power fluctuations are detected, then the MRAC continuously optimizes the PID gains so as to generate an appropriate dynamic step size until harvesting the maximum power point (MPP) under the optimal tracking conditions. Looking specifically at the simulation results, the proposed AD-HCS technique exhibits low oscillations around the MPP and a small settling time. Moreover, WECS efficiency is increased by 5% and 2% compared to the conventional and recent HCS techniques, respectively. Finally, the studied system is confirmed over a 1.5 MW, gird-tied, double-fed induction generator (DFIG) WECS using MATLAB/Simulink.

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“…Then, the PID output is used to adapt

Section: Mrac Strategymentioning

confidence: 99%

Robust Adaptive HCS MPPT Algorithm-Based Wind Generation System Using Model Reference Adaptive Control

Alrowaili

Ali

Youssef

et al. 2021

Sensors

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“…Based on Fig. 10, we can generate the error in (12) which is p = d/dt as the differetial operator and arrive at the sensitivity derivative in (13).…”

Section: Adaptation Mechanismmentioning

confidence: 99%

“…A nonliniear control system approach for a DC motor such as a sliding mode control design as in [5], backstepping control as in [6,7], and an adaptive control as in [8][9][10][11][12][13] have been discussed. Based on paper as in [5][6][7][8][9][10][11][12][13], the adaptive control approach is the most suitable type of control to treat conditions such as the disturbance of a DC motor. So, we used the adaptive control based on Model Reference Adaptive Control (MRAC) to control our DC motor model.…”

Section: Introductionmentioning

confidence: 99%

Model Reference Adaptive Control for DC motor based on Simulink

Munadi

Akbar

Naniwa

et al. 2016

2016 6th International Annual Engineering Seminar (InAES)

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Sometimes the conventional feedback control can not work well to cope with the changes that vary in its dynamic system. The parameters of the dynamic system that changes with time lead to a conventional feedback control system is not able to maintain control. This is caused by circumstances which are nonlinear and receive many disturbance so that the transient response of the system to be less precise and accurate to the desired steady state conditions. To overcome these problems, this paper presents an adaptive control system which can cope with the change of the dynamic DC motor system. The adaptive control system used is Model Reference Adaptive Control (MRAC) which contain four part. They are reference model, adaptation mechanism, plant, and control law. We designs DC motor plant by using Simscape in Simulink. An ideal response for reference model is designed by modeling the dynamic DC motor. Design of an adaptation mechanism with MIT rule and control law with Proportional Integral Derivative (PID) controller are present. It can affect the response of adaptive control system and matching the desired ideal response from reference model even when the system receives a disturbance.

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“…Linear control theory use tools like Root Locus, Nyquist or Bode plots to prove the stability of the controller. In modern control systems, digital controller algorithms like Linear Quadratic Regulator (LQR), Linear Quadratic Gaussian (LQG), Model Reference Adaptive Controller (MRAC) [1], Sliding Mode Control (SMC), Fuzzy Control [2] and Neural Networks [3] use more complex theory like Linear Matrix Inequalities (LMI) based on Lyapunov's stability criterion [4] [5] to prove stability. These controllers require the plant model and a set of state space equations that govern the system transfer function [6].…”

Section: Introductionmentioning

confidence: 99%