## What is/are Reference Adaptive?

Reference Adaptive - The adaptive control system that is used for the electromechanical inverter (EMI) is model-reference adaptive control (MRAC) which has four parts i.^{[1]}In this system, GROESO-based control is used to estimate and compensate the mismatched nonlinear dynamics, model-reference adaptive control (MRAC) is used to suppress the matched parameter uncertainty, and repetitive controller undertakes the task of tracking periodic reference input signal.

^{[2]}The discrete-time adaptive posicast controller (APC) presented here is a model-reference adaptive controller for uncertain linear systems with input time delay, which are subject to unmeasurable matched disturbances.

^{[3]}Generally model-reference adaptive control (MRAC) is designed using known regression matrix.

^{[4]}

## sliding mode control

A model reference adaptive sliding mode control for the position control of the permanent magnet synchronous motor is developed in this article.^{[1]}Comparison in the effectiveness of the presented method with model reference adaptive control and integral sliding mode control under the uncertainties of the gains is also conducted.

^{[2]}This paper presents a newly designed switching linear feedback structure of sliding mode control (SLF-SMC) plugged with an model reference adaptive system (MRAS) based sensorless field-oriented control (SFOC) for induction motor (IM).

^{[3]}The sensorless control performance under the

^{[4]}This paper proposes a model reference adaptive dynamic sliding mode control (MRASMC) scheme with an adaptive uncertainty and disturbance estimator (AUDE), the scheme is then applied to two-dimensional overhead cranes control with uncertainties, to ensure the payload can be transported to the desired position rapidly while its swing angle can be eliminated simultaneously.

^{[5]}Purpose The purpose of this research paper is to design a disturbance observer-based control based on the robust model reference adaptive backstepping sliding-mode control for attitude quadrotor model subject to uncertainties and disturbances.

^{[6]}

## magnet synchronous motor

This paper introduces a Fractional Order Field Oriented Control (FO-FOC) with sensorless Model Reference Adaptive System (MRAS) for the Permanent Magnet Synchronous Motor (PMSM).^{[1]}Different advanced sensorless techniques have been proposed based on the model reference adaptive system (MRAS) principle to make the interior permanent magnet synchronous motor (IPMSM) drives mechanically more robust.

^{[2]}This paper presents an efficient sensorless permanent magnet synchronous motor (PMSM) drive based on model reference adaptive system (MRAS) speed and position estimation for solar photovoltaic (PV)- battery power driven electric vehicle (EV).

^{[3]}In this paper, an improved adaptive law based model reference adaptive system (IAL-MRAS) algorithm is proposed to enhance the dynamic performance of a surface-mounted permanent magnet synchronous motor (SPMSM) sensorless drives, in which both a speed/position estimator and composite speed controller are designed by a systematic way.

^{[4]}This article presents a sensorless MPC (Model Predictive Control) for a PMSM (Permanent Magnet Synchronous Motor) in which the rotor speed is provided by a MRAS (Model Reference Adaptive System) observer, and general control strategy is FOC (Field Oriented Control) type.

^{[5]}

## proportional integral derivative

The design dual controller consists of model reference adaptive control (MRAC) along with proportional integral derivative (PID) controller and an integral use for the feedback of the design scheme.^{[1]}Purpose This paper aims to present a new approach, called hybrid model reference adaptive controller or H-MRAC, for the hybrid controller (proportional-integral-derivative [PID + MRAC]) that will be used to control the position of a pneumatic manipulator.

^{[2]}The present research aims to model, simulate and implement a new hybrid control approach based on a combination of proportional integral derivative (PID) Controller and Model Reference Adaptive Controller (MRAC), in which Lyapunov’s theory is used to ensure asymptotic stability to control a two degrees of freedom (DoF) manipulator driven by McKibben’s artificial pneumatic muscles.

^{[3]}In this paper, a Proportional-Integral-Derivative (PID) controller tuning scheme by Initialized Model Reference Adaptive Control (IMRAC) for a Lower Limb Exoskeleton (LLE) is presented.

^{[4]}

## speed sensorless control

This paper represents modified model reference adaptive system (MRAS) method for speed sensorless control of interior permanent magnet synchronous motor (IPMSM), which can estimate rotor speed and position information.^{[1]}The speed sensorless control algorithm is designed utilizing the model reference adaptive system (MRAS) based on a Luenberger observer (LO).

^{[2]}For high-performance speed sensorless control, a finite control set model predictive current control (FCS-MPCC) algorithm based on a model reference adaptive system (MRAS) is proposed.

^{[3]}In this study, two novel complex-valued model reference adaptive systems (MRASs) based on rotor flux and stator current are performed for speed-sensorless control of squirrel cage induction motor (SCIM) and tested under different operating conditions for a wide speed range.

^{[4]}

## proportional integral controller

In this article, a novel sliding mode adaptation mechanism of a model reference adaptive system speed estimator and a modified sliding mode speed controller are proposed to replace the proportional-integral controller used in the adaptation mechanism of the model reference adaptive system and the proportional-integral speed controller, respectively.^{[1]}Further, the performance of the proposed scheme is compared with a constrained static output feedback controller and a model reference adaptive proportional integral controller to confirm its superiority.

^{[2]}In particular, the inner control loop is designed as a model reference adaptive controller (MRAC) to deal with uncertainties in the system parameters, while the outer control loop utilises a fuzzy proportional-integral controller to reduce the effect of external disturbances on the load.

^{[3]}First, a conventional proportional-integral controller-based stator resistance estimation technique is used for a speed sensorless control scheme with two different model reference adaptive system (MRAS) concepts.

^{[4]}

## adaptive control structure

This article presents a discrete robust adaptive control structure, gathering a Robust Model Reference Adaptive Controller (RMRAC) with an adaptive Super-Twisting Sliding Mode (STSM) controller.^{[1]}In this paper, a new adaptive control structure for time-delayed systems has been proposed, combining the model reference adaptive control with the modified Smith predictor (SP).

^{[2]}This paper proposes an adaptive control structure involving the integration of a Luenberger observer with a model reference adaptive system.

^{[3]}

## linear time invariant

The paper addresses the problem of transient performance improvement of direct model reference adaptive control (MRAC) of discrete linear time-invariant (LTI) plants.^{[1]}In this paper it is shown that, using the recently introduced dynamic regressor extension and mixing parameter estimation technique, it is possible to remove the key assumption of prior knowledge on the high frequency gain imposed in model reference adaptive control of linear time-invariant multivariable systems.

^{[2]}For a general multi-input multi-output linear time-invariant system with unknown parameters, a multivariable model reference adaptive control (MRAC) scheme guarantees asymptotic output tracking, under some design conditions.

^{[3]}

## brushless doubly fed

This article proposes a rotor position observer based on a reactive power model reference adaptive system (MRAS) for sensorless direct voltage control (DVC) strategy of the stand-alone brushless doubly fed induction generators (BDFIGs).^{[1]}A novel model reference adaptive system for the rotor position/speed estimation and sensorless operation of a brushless doubly fed reluctance generator with maximum power point tracking is presented.

^{[2]}This paper proposes an active power based model reference adaptive system for sensorless speed estimation of primary field oriented brushless doubly-fed reluctance generator (BDFRG).

^{[3]}

## direct torque control

The direct torque control with space vector modulation method with model reference adaptive system with current model and current estimator and model reference adaptive system—flux based estimator applied in the control loop of the motor speed have been analyzed.^{[1]}This algorithm integrates the super twisting control approach with direct torque control and model reference adaptive system.

^{[2]}This study emphasis on a direct torque control (DTC) strategy with a speed estimator utilizing the Model Reference Adaptive System (MRAS) for a doubly fed induction machine (DFIM).

^{[3]}

## Model Reference Adaptive

Therefore, in this paper, while modeling the boiler and its pressure relations more precisely, we will introduce a recurrent type-2 fuzzy RBFN-based model reference adaptive control system with various uncertainties so that the uncertainty and inaccuracy of the model can be compensated.^{[1]}This paper describes the design of an adaptive controller based on model reference adaptive PID control (MRAPIDC) to stabilize a two-tank process when large variations of parameters and external disturbances affect the closed-loop system.

^{[2]}Finally, the availability of the proposed control method have been demonstrated by a series of experiments compared with a hysteresis inverse compensation-based model reference adaptive control method and a classical model free adaptive control method.

^{[3]}In order to solve the problem that the parameters of the regulator and the actual parameters do not match due to the time-varying motor parameters in the operation of PMSM, this paper adopts a robust adaptive control strategy based on linear matrix inequality (LMI) based on the model reference adaptive method (MRAS) to consider the uncertainties of the motor parameters in the state equation of the motor control system, the controller parameters are obtained by solving the LMI.

^{[4]}In this work, two Robust Model Reference Adaptive Control (RMRAC) for grid-side current control of a static 3-wire converter with LCL filter are implemented and compared.

^{[5]}Based on the Model Reference Adaptive Control (MRAC) configuration, the objective of the proposed controller is to ensure the output of the controlled plant to track the output of a given reference model system, while maintaining the overall closed-loop stability despite external disturbances and model uncertainties.

^{[6]}As for the inverse decoupling control system of a bearingless induction motor (BL-IM), in order to eliminate the influence of rotor resistance variation on its control performance, on the basis of the reactive power calculation of torque system, a novel fuzzy model reference adaptive (MRAS) identification method of rotor resistance is proposed.

^{[7]}Then, a model reference adaptive fault observer was designed to observe the faults in real-time.

^{[8]}Furthermore, a modified current model reference adaptive system (MRAS) is also proposed to increase the dynamic response and estimation accuracy.

^{[9]}The performance of the proposed scheme provides better voltage stability and ease of implementation as compared to the conventional proportional integral differential (PID) and the model reference adaptive schemes for an isolated MG under various test conditions.

^{[10]}In this paper, the design of a new robust model reference adaptive PI (MRAC-PI) current controller is proposed for a two-stage grid connected photovoltaic (PV) inverter.

^{[11]}This study presents a control design of roll motion for a vertical take-off and landing unmanned air vehicle (VTOL-UAV) design based on the Model Reference Adaptive Control (MRAC) scheme in the hovering flight phase.

^{[12]}An adaptive control scheme equipped with an input compensator is constructed to make the system to have a uniform interactor matrix and a consistent pattern of the gain matrix signs over different operating conditions, which are key prior design conditions for model reference adaptive control applied to quadrotor systems.

^{[13]}A proposed sliding mode (SM) and fuzzy logic (FL)-based model reference adaptive load frequency control (MRALFC) scheme is designed and implemented in this work to regulate power fluctuations and load frequency deviation utilizing the feature of droop characteristic of a wind energy conversion system (WECS).

^{[14]}Based on the idea of model reference adaptive control, two adaptive laws are presented to reduce the conservativeness that is usually introduced when the time-delays are unknown.

^{[15]}In this article, a novel sliding mode adaptation mechanism of a model reference adaptive system speed estimator and a modified sliding mode speed controller are proposed to replace the proportional-integral controller used in the adaptation mechanism of the model reference adaptive system and the proportional-integral speed controller, respectively.

^{[16]}The algorithm is based on a model reference adaptive system architecture consisting of a reference system and an adaptive model.

^{[17]}The ATLC controller is developed based on trajectory linearization control (TLC) method and model reference adaptive control (MRAC) method.

^{[18]}This article presents a discrete robust adaptive control structure, gathering a Robust Model Reference Adaptive Controller (RMRAC) with an adaptive Super-Twisting Sliding Mode (STSM) controller.

^{[19]}This paper proposes a sliding mode controller based on robust model reference adaptive proportional-integral (RMRA-PI) control for a stand-alone voltage source inverter (SA-VSI).

^{[20]}This paper represents modified model reference adaptive system (MRAS) method for speed sensorless control of interior permanent magnet synchronous motor (IPMSM), which can estimate rotor speed and position information.

^{[21]}This paper introduces a Fractional Order Field Oriented Control (FO-FOC) with sensorless Model Reference Adaptive System (MRAS) for the Permanent Magnet Synchronous Motor (PMSM).

^{[22]}A Model Reference Adaptive System is developed using stator current and stator voltages, which are further developed with BEIC to approximate the rotor rpm.

^{[23]}Furthermore, the proposed control architecture employs barrier Lyapunov functions and a novel robust model reference adaptive control law to guarantee a priori user-defined constraints on both the trajectory tracking error and the control input, despite poor information on the aircraft's inertial properties and the presence of unknown, unsteady payloads.

^{[24]}To tackle these issues, this paper designs a control method based on the Model Reference Adaptive Control with an integrator (MRACI) with reference dynamics like an observer.

^{[25]}The design dual controller consists of model reference adaptive control (MRAC) along with proportional integral derivative (PID) controller and an integral use for the feedback of the design scheme.

^{[26]}The topological character of the proposed MRAC (Model Reference Adaptive Controller) consists of three parts: a feedforward controller, a derivative portion and an ordinary feedback.

^{[27]}This paper deals with model reference adaptive control for an uncertain switched linear system in the presence of disturbances in the state and control input based on time-dependent switching methods.

^{[28]}This paper develops a new model reference adaptive control (MRAC) framework using partial-state feedback for solving a multivariable adaptive output tracking problem.

^{[29]}This brief proposes the first model reference adaptive control (MRAC) method for aortic pressure (AoP) regulation and maintaining the heart’s physiological aerobic metabolism in ex vivo heart perfusion (EVHP).

^{[30]}To achieve stability and minimize trajectory tracking error, a novel robust augmented adaptive torque control law is developed for the system, which combines a feedback linearization controller with a model reference adaptive controller.

^{[31]}The synchronization problem is solved by introducing the distributed model reference adaptive control.

^{[32]}This paper proposes a Reactive power-based Model Reference Adaptive Controller (Q-MRAC) for the real-time estimation of rotor resistance considering the magnetizing inductance saturation, for an IFOC based induction motor drive used in electric vehicle (EV).

^{[33]}This paper presents an application of Model reference adaptive system (MRAS) in the control of wind turbine power.

^{[34]}Results for Proportional Integral and Derivative (PID) and Model Reference Adaptive Control (MRAC) of model generated using force-moment mathematical model are analyzed and compared using MATLAB Simulink.

^{[35]}This article proposes a rotor temperature estimation method for in-service induction machine (IM) based on parameter identification, which combines the advantage of recursive least squares (RLS) and model reference adaptive system (MRAS).

^{[36]}The proposed control designs rely on modifications to the classical model reference adaptive control framework and the more recent $${\mathscr {L}}_1$$ adaptive control architecture, in which an additional low-pass filter is used to ensure guaranteed transient performance and robustness to time delays in the control input even in the limit of arbitrarily large adaptive gains.

^{[37]}An improved model reference adaptive system (MRAS) is proposed.

^{[38]}The speed sensorless control algorithm is designed utilizing the model reference adaptive system (MRAS) based on a Luenberger observer (LO).

^{[39]}Purpose This paper aims to present a new approach, called hybrid model reference adaptive controller or H-MRAC, for the hybrid controller (proportional-integral-derivative [PID + MRAC]) that will be used to control the position of a pneumatic manipulator.

^{[40]}Two self-adjusting observers derived from a modified current-based model reference adaptive system (CB-MRAS) are presented.

^{[41]}Sliding mode observer (SMO) and Model reference adaptive system (MRAS) are compared.

^{[42]}Design/methodology/approach The concept discussed here relies on model reference adaptive control.

^{[43]}A numerical example is given to illustrate the effectiveness of the decentralized filtering adaptive neural network control architecture by comparing against the model reference adaptive control (MRAC).

^{[44]}An adaptive controller is designed based on the model reference adaptive control when the aircraft encounters the wind turbulence in different dominant directions.

^{[45]}The speed sensorless scheme considered here is a model reference adaptive system whose observing parameter is rotot-flux.

^{[46]}, 2020), which focus on deep model reference adaptive control for linear systems with known drift dynamics and control effectiveness matrices, this letter considers general control-affine uncertain nonlinear systems.

^{[47]}The article presents the results of research, analysis and how to build learning feed-forward controller based on model reference adaptive system in the remote control loop for missile stabilization.

^{[48]}Different advanced sensorless techniques have been proposed based on the model reference adaptive system (MRAS) principle to make the interior permanent magnet synchronous motor (IPMSM) drives mechanically more robust.

^{[49]}This paper proposes an innovative Supervised model Reference Adaptive System (SuMRAS) for the rotor flux linkage identification in Surface Permanent Magnet Synchronous Machines (SPMSM).

^{[50]}

## reference adaptive control

Therefore, in this paper, while modeling the boiler and its pressure relations more precisely, we will introduce a recurrent type-2 fuzzy RBFN-based model reference adaptive control system with various uncertainties so that the uncertainty and inaccuracy of the model can be compensated.^{[1]}Finally, the availability of the proposed control method have been demonstrated by a series of experiments compared with a hysteresis inverse compensation-based model reference adaptive control method and a classical model free adaptive control method.

^{[2]}In this work, two Robust Model Reference Adaptive Control (RMRAC) for grid-side current control of a static 3-wire converter with LCL filter are implemented and compared.

^{[3]}Based on the Model Reference Adaptive Control (MRAC) configuration, the objective of the proposed controller is to ensure the output of the controlled plant to track the output of a given reference model system, while maintaining the overall closed-loop stability despite external disturbances and model uncertainties.

^{[4]}This study presents a control design of roll motion for a vertical take-off and landing unmanned air vehicle (VTOL-UAV) design based on the Model Reference Adaptive Control (MRAC) scheme in the hovering flight phase.

^{[5]}An adaptive control scheme equipped with an input compensator is constructed to make the system to have a uniform interactor matrix and a consistent pattern of the gain matrix signs over different operating conditions, which are key prior design conditions for model reference adaptive control applied to quadrotor systems.

^{[6]}Based on the idea of model reference adaptive control, two adaptive laws are presented to reduce the conservativeness that is usually introduced when the time-delays are unknown.

^{[7]}The ATLC controller is developed based on trajectory linearization control (TLC) method and model reference adaptive control (MRAC) method.

^{[8]}Furthermore, the proposed control architecture employs barrier Lyapunov functions and a novel robust model reference adaptive control law to guarantee a priori user-defined constraints on both the trajectory tracking error and the control input, despite poor information on the aircraft's inertial properties and the presence of unknown, unsteady payloads.

^{[9]}To tackle these issues, this paper designs a control method based on the Model Reference Adaptive Control with an integrator (MRACI) with reference dynamics like an observer.

^{[10]}The design dual controller consists of model reference adaptive control (MRAC) along with proportional integral derivative (PID) controller and an integral use for the feedback of the design scheme.

^{[11]}This paper deals with model reference adaptive control for an uncertain switched linear system in the presence of disturbances in the state and control input based on time-dependent switching methods.

^{[12]}This paper develops a new model reference adaptive control (MRAC) framework using partial-state feedback for solving a multivariable adaptive output tracking problem.

^{[13]}This brief proposes the first model reference adaptive control (MRAC) method for aortic pressure (AoP) regulation and maintaining the heart’s physiological aerobic metabolism in ex vivo heart perfusion (EVHP).

^{[14]}The synchronization problem is solved by introducing the distributed model reference adaptive control.

^{[15]}Results for Proportional Integral and Derivative (PID) and Model Reference Adaptive Control (MRAC) of model generated using force-moment mathematical model are analyzed and compared using MATLAB Simulink.

^{[16]}The proposed control designs rely on modifications to the classical model reference adaptive control framework and the more recent $${\mathscr {L}}_1$$ adaptive control architecture, in which an additional low-pass filter is used to ensure guaranteed transient performance and robustness to time delays in the control input even in the limit of arbitrarily large adaptive gains.

^{[17]}Design/methodology/approach The concept discussed here relies on model reference adaptive control.

^{[18]}A numerical example is given to illustrate the effectiveness of the decentralized filtering adaptive neural network control architecture by comparing against the model reference adaptive control (MRAC).

^{[19]}An adaptive controller is designed based on the model reference adaptive control when the aircraft encounters the wind turbulence in different dominant directions.

^{[20]}, 2020), which focus on deep model reference adaptive control for linear systems with known drift dynamics and control effectiveness matrices, this letter considers general control-affine uncertain nonlinear systems.

^{[21]}A design methodology of advance PID controller using Model Reference Adaptive Control (MRAC) based PID technique is designed in this study to compare its performance against a conventional PID controller based on different road surface and various vehicle set speed.

^{[22]}It is based on Nonlinear Model Reference Adaptive Control where the reference model corresponds to a chaotic Duffing oscillator.

^{[23]}Model reference adaptive control (MRAC) was utilized to develop a synchronous speed-identification scheme based on the reactive power of the motor, and the rotor speed was estimated by subtracting the slip speed from the estimated synchronous speed.

^{[24]}Based on the identified model, model reference adaptive control is designed as angular rate controller, and PID controller is used to stabilize attitude loop.

^{[25]}As it is well-known, system uncertainties and unmodeled dynamics can deteriorate stability properties of model reference adaptive control systems.

^{[26]}At first, model reference adaptive control.

^{[27]}This paper presents the design of the Two-Degree-of-Freedom (2DOF) based Model Reference Adaptive Control (MRAC) as a tracking control system for the Non-minimum Phase (NMP) system.

^{[28]}The adaptive control system that is used for the electromechanical inverter (EMI) is model-reference adaptive control (MRAC) which has four parts i.

^{[29]}The paper addresses the problem of transient performance improvement of direct model reference adaptive control (MRAC) of discrete linear time-invariant (LTI) plants.

^{[30]}Eventually, in order to evaluate the efficiency of the proposed method, this method is simulated on SLFJM and the results are compared with conventional ${\mathscr{L}_{1}}\_{\text{AC}}$ and “Model Reference Adaptive Control (MRAC)” methods.

^{[31]}To overcome these difficulties, the Lyapunov-based Model Reference Adaptive Control (MRAC) has been suggested here and the novelty of this technique is that it not only nullifies undershoots and overshoots, but it also tracks the reference input trajectory simultaneously.

^{[32]}This letter confluences ideas from distributed model reference adaptive control (MRAC) architecture for multi-agent systems and closed-loop reference model (CRM) based MRAC algorithm.

^{[33]}To solve the above problems, this study proposes an HMC steering torque control method based on the model reference adaptive control (MRAC).

^{[34]}The proposed FRADC contains three intuitional terms: (1) a decoupling term that alleviates the coupled motions among different axes, making the controller implementation more convenient and efficient; (2) a feedforward compensation term based on a fractional normalized Bouc-Wen (FONBW) model, compensating for the rate-dependent hysteresis effect induced by the piezoelectric actuators; (3) a feedback model reference adaptive control (MRAC) term with fractional proportional-plus-integral-type updating rules that suppresses the creep effect, parameters uncertainty and external disturbances, further enhancing the robustness and positioning accuracy.

^{[35]}A novel methodology, based on Model Reference Adaptive Control, is presented to robustly estimate the time-varying set-points that maximise the bottleneck throughput.

^{[36]}Model reference adaptive control is used to recover the nominal model used by MPC.

^{[37]}This approach, named adaptive control, involves the insertion of a low-pass filter at the input of the Model Reference Adaptive Control (MRAC).

^{[38]}Meanwhile, Model reference adaptive control (MRAC) is adopted in the speed loop to eliminate the disturbance caused by the ripple of real-time update parameters, through which the disturbance caused by parameter mismatch is suppressed effectively.

^{[39]}In this paper, the containment control problem of heterogeneous uncertain high-order linear Multi-Agent Systems (MASs) is addressed and solved via a novel fully-Distributed Model Reference Adaptive Control (DMRAC) approach, where each follower computes its adaptive control action on the basis of local measurements, information shared with neighbors (within the communication range) and the matching errors w.

^{[40]}This paper develops a neural-network-based model reference adaptive control (MRAC) scheme for a rotorcraft in the presence of input saturation.

^{[41]}In this chapter, we design a decentralized model reference adaptive control for this class of large-scale systems with long input and state delays and time-varying delays in the uncertain nonlinear interconnection terms.

^{[42]}Comparison in the effectiveness of the presented method with model reference adaptive control and integral sliding mode control under the uncertainties of the gains is also conducted.

^{[43]}The adaptation scheme is the binary model reference adaptive control (BMRAC) which utilizes parameter projection and sufficiently high adaptation gains.

^{[44]}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.

^{[45]}This paper confluences ideas from distributed model reference adaptive control (MRAC) architecture for multi-agent systems and closed-loop reference model (CRM) based MRAC algorithm.

^{[46]}In this study, model reference adaptive control approaches based on the Massachusetts Institute of Technology (MIT) rule and Lyapunov method were exploited in order to improve the performance of the speed governor for frequency containment control.

^{[47]}Leveraging model reference adaptive control framework and networked control architectures, we develop a coordinated leader-follower consensus controller capable of overcoming communication losses within the team, handling non-communicative robots, and compensating for environmental noise.

^{[48]}Adaptive controllers may offer good reliability and robustness against these issues, but the traditional model reference adaptive control has a slow response in the transient, for these reasons, in this work a variable structure model reference adaptive controller is applied, since it has a faster transient response.

^{[49]}In this system, GROESO-based control is used to estimate and compensate the mismatched nonlinear dynamics, model-reference adaptive control (MRAC) is used to suppress the matched parameter uncertainty, and repetitive controller undertakes the task of tracking periodic reference input signal.

^{[50]}

## reference adaptive system

Furthermore, a modified current model reference adaptive system (MRAS) is also proposed to increase the dynamic response and estimation accuracy.^{[1]}In this article, a novel sliding mode adaptation mechanism of a model reference adaptive system speed estimator and a modified sliding mode speed controller are proposed to replace the proportional-integral controller used in the adaptation mechanism of the model reference adaptive system and the proportional-integral speed controller, respectively.

^{[2]}The algorithm is based on a model reference adaptive system architecture consisting of a reference system and an adaptive model.

^{[3]}This paper represents modified model reference adaptive system (MRAS) method for speed sensorless control of interior permanent magnet synchronous motor (IPMSM), which can estimate rotor speed and position information.

^{[4]}This paper introduces a Fractional Order Field Oriented Control (FO-FOC) with sensorless Model Reference Adaptive System (MRAS) for the Permanent Magnet Synchronous Motor (PMSM).

^{[5]}A Model Reference Adaptive System is developed using stator current and stator voltages, which are further developed with BEIC to approximate the rotor rpm.

^{[6]}This paper presents an application of Model reference adaptive system (MRAS) in the control of wind turbine power.

^{[7]}This article proposes a rotor temperature estimation method for in-service induction machine (IM) based on parameter identification, which combines the advantage of recursive least squares (RLS) and model reference adaptive system (MRAS).

^{[8]}An improved model reference adaptive system (MRAS) is proposed.

^{[9]}The speed sensorless control algorithm is designed utilizing the model reference adaptive system (MRAS) based on a Luenberger observer (LO).

^{[10]}Two self-adjusting observers derived from a modified current-based model reference adaptive system (CB-MRAS) are presented.

^{[11]}Sliding mode observer (SMO) and Model reference adaptive system (MRAS) are compared.

^{[12]}The speed sensorless scheme considered here is a model reference adaptive system whose observing parameter is rotot-flux.

^{[13]}The article presents the results of research, analysis and how to build learning feed-forward controller based on model reference adaptive system in the remote control loop for missile stabilization.

^{[14]}Different advanced sensorless techniques have been proposed based on the model reference adaptive system (MRAS) principle to make the interior permanent magnet synchronous motor (IPMSM) drives mechanically more robust.

^{[15]}This paper proposes an innovative Supervised model Reference Adaptive System (SuMRAS) for the rotor flux linkage identification in Surface Permanent Magnet Synchronous Machines (SPMSM).

^{[16]}It is based on the back electromotive force (EMF) model reference adaptive system (MRAS) and second-order sliding-mode supertwisting algorithm (STA).

^{[17]}Furthermore, a simple hybrid observer is proposed which combines a model reference adaptive system (MRAS) and a sliding mode (SM) observer.

^{[18]}To estimate the rotor speed and stator flux the model reference adaptive system (MRAS) is used that is designed from identified voltages and currents.

^{[19]}Since the stator magnetizing current cannot be obtained directly, a novel iron loss resistance observer based on the model reference adaptive system (MRAS) is designed to estimate iron loss resistance and stator magnetizing current simultaneously.

^{[20]}This paper presents an efficient sensorless permanent magnet synchronous motor (PMSM) drive based on model reference adaptive system (MRAS) speed and position estimation for solar photovoltaic (PV)- battery power driven electric vehicle (EV).

^{[21]}A model reference adaptive system (MRAS) with sensorless stator and rotor flux observer is used to estimate the stator and rotor fluxes, stator currents and rotor speed of sensorless IM.

^{[22]}This paper presents a model reference adaptive system (MRAS) based sensorless control strategy for TPMLSM.

^{[23]}The proposed sensorless technique is based on a model reference adaptive system (MRAS) and tested under distorted grid conditions.

^{[24]}Therefore, to solve the problem of parameter dependence in PCC and achieve high performance control of the PMLSM control system, a model reference adaptive system (MRAS) which can identify the inductance and permanent magnet (PM) flux linkage simultaneously is proposed and applied.

^{[25]}This paper presents a predictive strategy and Model Reference Adaptive System (MRAS) technique for controlling the synchronous reluctance motor (SynRM).

^{[26]}The direct torque control with space vector modulation method with model reference adaptive system with current model and current estimator and model reference adaptive system—flux based estimator applied in the control loop of the motor speed have been analyzed.

^{[27]}This article proposes a rotor position observer based on a reactive power model reference adaptive system (MRAS) for sensorless direct voltage control (DVC) strategy of the stand-alone brushless doubly fed induction generators (BDFIGs).

^{[28]}The proposed dc voltage estimation technique is based on model reference adaptive system (MRAS).

^{[29]}In this paper, a parameter estimation strategy of individual parallel connected inverter is presented based on a novel model reference adaptive system architecture has been proposed.

^{[30]}A novel model reference adaptive system for the rotor position/speed estimation and sensorless operation of a brushless doubly fed reluctance generator with maximum power point tracking is presented.

^{[31]}Aiming at the problems of poor stability and low system accuracy in the vector control of permanent magnet synchronous linear motor (PMSLM), a vector control strategy for PMSLM based on model reference adaptive system (MRAS) is proposed.

^{[32]}Based on a model reference adaptive system approach, we present an online parameter identification method for nonlinear infinite-dimensional evolutionary systems.

^{[33]}In this paper, an improved adaptive law based model reference adaptive system (IAL-MRAS) algorithm is proposed to enhance the dynamic performance of a surface-mounted permanent magnet synchronous motor (SPMSM) sensorless drives, in which both a speed/position estimator and composite speed controller are designed by a systematic way.

^{[34]}This work proposes an alternative strategy to the use of a speed sensor in the implementation of active and reactive power based model reference adaptive system (PQ-MRAS) estimator in order to calculate the rotor and stator resistances of an induction motor (IM) and the use of these parameters for the detection of inter-turn short circuits (ITSC) faults in the stator of this motor.

^{[35]}To solve this problem, an estimation technique proposed to identify the value of harmonic filter parameter based on Model reference adaptive system (MRAS).

^{[36]}This letter introduces a new method to estimate the motor speed based on the Model Reference Adaptive System (MRAS) technique.

^{[37]}This paper presents a newly designed switching linear feedback structure of sliding mode control (SLF-SMC) plugged with an model reference adaptive system (MRAS) based sensorless field-oriented control (SFOC) for induction motor (IM).

^{[38]}The sensorless control performance under the

^{[39]}Furthermore, a comparative study with and Luenberger observer(LO) and model reference adaptive system(MRAS) is presented.

^{[40]}Then, a novel model reference adaptive system (MRAS)-based

^{[41]}Afterward, the real-time d-q-axis inductance identified by a fault-tolerance model reference adaptive system is used to replace the original parameters in MPC.

^{[42]}First, a conventional proportional-integral controller-based stator resistance estimation technique is used for a speed sensorless control scheme with two different model reference adaptive system (MRAS) concepts.

^{[43]}In this paper, F-MRAS (flux-based model reference adaptive system) speed estimator is employed for estimating the speed/position.

^{[44]}The proposed speed observer combines artificial intelligence and model reference adaptive system (MRAS).

^{[45]}The estimation of the speed is based on Model Reference Adaptive System (MRAS) method.

^{[46]}This algorithm integrates the super twisting control approach with direct torque control and model reference adaptive system.

^{[47]}The model reference adaptive system (MRAS) was employed for the speed self-sensing mechanism.

^{[48]}This study emphasis on a direct torque control (DTC) strategy with a speed estimator utilizing the Model Reference Adaptive System (MRAS) for a doubly fed induction machine (DFIM).

^{[49]}For high-performance speed sensorless control, a finite control set model predictive current control (FCS-MPCC) algorithm based on a model reference adaptive system (MRAS) is proposed.

^{[50]}

## reference adaptive controller

This article presents a discrete robust adaptive control structure, gathering a Robust Model Reference Adaptive Controller (RMRAC) with an adaptive Super-Twisting Sliding Mode (STSM) controller.^{[1]}The topological character of the proposed MRAC (Model Reference Adaptive Controller) consists of three parts: a feedforward controller, a derivative portion and an ordinary feedback.

^{[2]}To achieve stability and minimize trajectory tracking error, a novel robust augmented adaptive torque control law is developed for the system, which combines a feedback linearization controller with a model reference adaptive controller.

^{[3]}This paper proposes a Reactive power-based Model Reference Adaptive Controller (Q-MRAC) for the real-time estimation of rotor resistance considering the magnetizing inductance saturation, for an IFOC based induction motor drive used in electric vehicle (EV).

^{[4]}Purpose This paper aims to present a new approach, called hybrid model reference adaptive controller or H-MRAC, for the hybrid controller (proportional-integral-derivative [PID + MRAC]) that will be used to control the position of a pneumatic manipulator.

^{[5]}This paper shows an application of an MRAC (Model Reference Adaptive Controller) using the MIT rule, through a damped pendulum, that has a CC motor with an attached propeller, as input parameter and the angle captured by an accelerometer as an output parameter in the SISO (Single Input Single Output) system.

^{[6]}This article proposes the development of a model reference adaptive controller (MRAC) for grid integration of a single-stage three-phase grid-connected photovoltaic system (GCPVS).

^{[7]}Next, the recurrent neural network combined with a Model Reference Adaptive Controller (MRAC) is used to calculate the reference trajectory for the system.

^{[8]}The performance of the L1 adaptive controller is obtained by comparison with the traditional model reference adaptive controller (MRAC).

^{[9]}This paper proposes the design of a Robust Model Reference Adaptive Controller (RMRAC) with an adaptive Super-Twisting Sliding Mode (STSM) controller using a first order model reference, neglecting a pair of conjugated complex poles of the system plant (LCL filter).

^{[10]}Finally, the gain-prediction compensator is combined with a linearized β model reference adaptive controller to compensate the adverse effects of very large time delay, which exhibits excellent performance when addressing the extreme conditions at high angles of attack.

^{[11]}In particular, the inner control loop is designed as a model reference adaptive controller (MRAC) to deal with uncertainties in the system parameters, while the outer control loop utilises a fuzzy proportional-integral controller to reduce the effect of external disturbances on the load.

^{[12]}The discrete-time adaptive posicast controller (APC) presented here is a model-reference adaptive controller for uncertain linear systems with input time delay, which are subject to unmeasurable matched disturbances.

^{[13]}Furthermore, a model reference adaptive controller (MRAC) based on the theory of Lyapunov is constructed to enhance the fault-tolerant control performance of SCR Simulation, on the basis of the diagnosis information of UKF observer.

^{[14]}The present research aims to model, simulate and implement a new hybrid control approach based on a combination of proportional integral derivative (PID) Controller and Model Reference Adaptive Controller (MRAC), in which Lyapunov’s theory is used to ensure asymptotic stability to control a two degrees of freedom (DoF) manipulator driven by McKibben’s artificial pneumatic muscles.

^{[15]}The paper deals with description of design of a model reference adaptive controller for a pneumatics artificial muscle.

^{[16]}In accordance with the proposed model, an observer-based model reference adaptive controller is developed, leading to a convex optimization problem subject to matrix inequalities.

^{[17]}The Model Reference Adaptive Controller is a high order of adaptive control.

^{[18]}This paper presents a new fractional-order robust model reference adaptive controller for the piezo-actuated active vibration isolation systems with a relative-degree-one model.

^{[19]}The performance of $\mathcal{L}_{l}$ adaptive controller is obtained by comparing with traditional model reference adaptive controller (MRAC).

^{[20]}In this paper, we present a hybrid direct-indirect model reference adaptive controller (MRAC), to address a class of problems with matched and unmatched uncertainties.

^{[21]}Due to the fact that it is not possible to obtain full state of DEAP actuator, the authors applied Model Reference Adaptive Controller with output feedback were voltage is an input signal and distance is an output.

^{[22]}Analysis of two adaptive controller parameter adjustment laws for a model reference adaptive controller has been discussed in this paper.

^{[23]}This paper presents a new algorithm of a neural network model reference adaptive controller that uses a variable learning rate.

^{[24]}The model reference adaptive controller is able to adapt to unknown friction characteristics and parameter uncertainties and to reduce the following error.

^{[25]}

## reference adaptive method

In order to solve the problem that the parameters of the regulator and the actual parameters do not match due to the time-varying motor parameters in the operation of PMSM, this paper adopts a robust adaptive control strategy based on linear matrix inequality (LMI) based on the model reference adaptive method (MRAS) to consider the uncertainties of the motor parameters in the state equation of the motor control system, the controller parameters are obtained by solving the LMI.^{[1]}So model reference adaptive method is raised for online multi-parameter identification of PMSM.

^{[2]}A basic optimal LQR control law is compensated by the augmented model reference adaptive method under the framework of feedback control structure, to improve the dynamic and anti-interference performance of the system which affected by uncertainty.

^{[3]}

## reference adaptive pid

This paper describes the design of an adaptive controller based on model reference adaptive PID control (MRAPIDC) to stabilize a two-tank process when large variations of parameters and external disturbances affect the closed-loop system.^{[1]}Received Mar 31, 2020 Revised Jun 20, 2020 Accepted Jul 6, 2020 This paper describes the design of an adaptive controller based on model reference adaptive PID control (MRAPIDC) to stabilize a two-tank process when large variations of parameters and external disturbances affect the closed-loop system.

^{[2]}

## reference adaptive scheme

The performance of the proposed scheme provides better voltage stability and ease of implementation as compared to the conventional proportional integral differential (PID) and the model reference adaptive schemes for an isolated MG under various test conditions.^{[1]}The proposed Enhanced Model Reference Adaptive Scheme (EMRAC) follows the same phenomenon of the Model Reference Adaptive Scheme (MRAC) with a slight difference in its control strategy.

^{[2]}

## reference adaptive proportional

This paper proposes a sliding mode controller based on robust model reference adaptive proportional-integral (RMRA-PI) control for a stand-alone voltage source inverter (SA-VSI).^{[1]}Further, the performance of the proposed scheme is compared with a constrained static output feedback controller and a model reference adaptive proportional integral controller to confirm its superiority.

^{[2]}