scholarly journals Study of Closed-Loop Model Reference Adaptive Control of Smart MicroGrid with QNU and Recurrent Learning

2017 ◽  
Vol 21 (4) ◽  
pp. 34-39
Author(s):  
Vladimír Malý ◽  
Martin Veselý ◽  
Peter M. Beneš ◽  
Petr Neuman ◽  
Ivo Bukovský
Keyword(s):  
Adaptive Control ◽  
Closed Loop ◽  
Model Reference Adaptive Control ◽  
Loop Model ◽  
Model Reference ◽  
Model Reference Adaptive

scholarly journals Fractional-Order Closed-Loop Model Reference Adaptive Control for Anesthesia

Algorithms ◽  
2018 ◽  
Vol 11 (7) ◽  
pp. 106 ◽  
Author(s):  
Gerardo Navarro-Guerrero ◽  
Yu Tang
Keyword(s):  
Adaptive Control ◽  
Fractional Order ◽  
Closed Loop ◽  
Adaptive Controller ◽  
Model Reference Adaptive Control ◽  
Loop Model ◽  
Unknown Parameters ◽  
Model Reference ◽  
State Measurement ◽  
Model Reference Adaptive

The design of a fractional-order closed-loop model reference adaptive control (FOCMRAC) for anesthesia based on a fractional-order model (FOM) is proposed in the paper. This proposed model gets around many difficulties, namely, unknown parameters, lack of state measurement, inter and intra-patient variability, and variable time-delay, encountered in controller designs based on the PK/PD model commonly used for control of anesthesia, and allows to design a simple adaptive controller based on the Lyapunov analysis. Simulations illustrate the effectiveness and robustness of the proposed control.

Composite Model Reference Adaptive Control for a Class of Nonlinear Fractional Order Systems

2015 ◽  
Author(s):  
Yiheng Wei ◽  
Shu Liang ◽  
Yangsheng Hu ◽  
Yong Wang
Keyword(s):  
Adaptive Control ◽  
Fractional Order ◽  
Closed Loop ◽  
Tracking Error ◽  
Model Reference Adaptive Control ◽  
Prediction Errors ◽  
Model Reference ◽  
Closed Loop Systems ◽  
The Stability ◽  
Model Reference Adaptive

This article presents a novel model reference adaptive control of fractional order nonlinear systems, which is a generalization of existing method for integer order systems. The formulating adaptive law is in terms of both tracking and prediction errors, whereas existing methods only depends on tracking error. The transient performance of the closed-loop systems with the proposed control strategy improves in the sense of generating smooth system output. The stability and tracking convergence of the resulting closed-loop system are analyzed via the indirect Lyapunov method. Meanwhile, the proposed controller is implemented by employing some fractional order tracking differentiator to generate the required fractional derivatives of a signal. Numerical examples are provided to illustrate the effectiveness of our results.

scholarly journals Stability limit of human-in-the-loop model reference adaptive control architectures

2017 ◽  
Vol 91 (10) ◽  
pp. 2314-2331 ◽  
Author(s):  
Tansel Yucelen ◽  
Yildiray Yildiz ◽  
Rifat Sipahi ◽  
Ehsan Yousefi ◽  
Nhan Nguyen
Keyword(s):  
Adaptive Control ◽  
Stability Limit ◽  
Model Reference Adaptive Control ◽  
Loop Model ◽  
Model Reference ◽  
Human In The Loop ◽  
Model Reference Adaptive

scholarly journals Effects of Linear Filter on Stability and Performance of Human-in-the-Loop Model Reference Adaptive Control Architectures

2017 ◽  
Author(s):  
Ehsan Yousefi ◽  
Didem Fatma Demir ◽  
Rifat Sipahi ◽  
Tansel Yucelen ◽  
Yildiray Yildiz
Keyword(s):  
Adaptive Control ◽  
Model Reference Adaptive Control ◽  
Human Model ◽  
Linear Filter ◽  
Loop Model ◽  
Model Reference ◽  
Human In The Loop ◽  
Error Dynamics ◽  
And Performance ◽  
Model Reference Adaptive

Model reference adaptive control (MRAC) can effectively handle various challenges of the real world control problems including exogenous disturbances, system uncertainties, and degraded modes of operations. In human-in-the-loop settings, MRAC may cause unstable system trajectories. Basing on our recent work on the stability of MRAC-human dynamics, here we follow an optimization based computations to design a linear filter and study whether or not this filter inserted between the human model and MRAC could help remove such instabilities, and potentially improve performance. To this end, we present a mathematical approach to study how the error dynamics of MRAC could favorably or detrimentally influence human operator’s error dynamics in performing a certain task. An illustrative numerical example concludes the study.

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