Model Reference Adaptive Scheme Research Articles

A Robust Adaptive Decentralized Inverter Voltage Control Approach for Solar PV and Storage-Based Islanded Microgrid

This article proposes a new decentralized control strategy for an isolated microgrid (MG). The decentralized control strategy is applied to control the voltage source inverter output voltage by utilizing the sinusoidal pulsewidth modulation (SPWM) based control strategy. A solar photovoltaic (SPV) and storage system-based islanded MG is considered with a common inverter, which is then connected to a three-phase ac system. The maximum power point tracking based algorithm has been applied for the maximum power extraction from the SPV system. The common inverter is triggered by SPWM-based technique in a controlled way so that the output voltage can be maintained. The storage system charging and discharging has also been controlled through a control algorithm. The variations in the solar output data are captured with the help of a series of variations in the solar irradiation data. An adaptive discrete proportional integral differential (ADPID) based controller is used for the inverter voltage control for a dynamic MG model. The peak value of the ac voltage signal is being utilized as the reference signal, and a closed-loop dq control is applied. 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.

Enhanced Model Reference Adaptive Control Scheme for Tracking Control of Magnetic Levitation System

Magnetic Levitation is a process in which an object is suspended with the support of the magnetic field. Despite being an unstable system, Magnetic Levitation Systems (MAGLEV) have profound applications in various fields of engineering. MAGLEV systems are sensitive, unstable, and nonlinear and uncertainties always pose a challenge in Controller Design. As a solution, adaptive controllers came into existence with adaptation mechanisms to cover the system uncertainties. In this study, a simple, novel, and an effective approach to the Enhanced Adaptive Control scheme is proposed for the ball position control and tracking of an unstable Magnetic Levitation System. 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. The proposed scheme consists of Proportional-Integral-Velocity plus Feed Forward as the control structure and a modified version of the standard tuning rule is used as the adaptation mechanism. The control scheme is applied to a standard benchmark Magnetic Levitation System and the tracking performance of the scheme is tested by applying square and multi-sine pattern trajectories to the Magnetic Levitation System. The performance of the developed Enhanced MRAC performance is compared with that of the Proportional Integral Velocity with Feedforward Control (PIV+FF) scheme and the proposed control scheme is proven to be more suitable. The performance of the proposed scheme is also analyzed with Power Spectral Density and Root Mean Square Error to evaluate the ball position tracking control. It is inferred from the experimental results that Enhanced MRAC accommodates the changes and makes the system more reliable with good tracking ability.

Magnetizing current based improved rotor position and speed estimation of doubly-fed induction generator using model reference adaptive scheme

An improved rotor position and speed estimation technique for doubly-fed induction generator (DFIG) has been described in this paper. DFIG is widely used as electrical generator in wind energy conversion system. For this wind generator, rotor position and speed information is extremely necessary for implementation of position/speed sensor-less control logics for attaining high dynamic performance. In this respect, magnetization current based rotor position and speed estimation technique is proposed. The estimation scheme adopts model reference adaptive system (MRAS). Various highly desirable features such as integrator- and differentiator-less operation, flux computation-less logic, least machine’s parameter sensitivity, start on fly and capability of operation on both sub- and super-synchronous modes have been achieved in proposed scheme. The improvement in proposed estimation model can be stated that it is free from magnetizing parameter mismatch irrespective of power level. On the other hand, existing excitation current based scheme does not provide accurate estimation due to deviation of magnetizing inductance in low output power condition i.e. limited operating range. Also, another traditional excitation current based technique includes direct flux estimation (subjected to flux dynamics directly) in addition to magnetizing parameter dependency. Both simulation and real-time validation of proposed estimation concept have been done to present superiority over traditional schemes.

Robust model reference adaptive control for transient performance enhancement

SUMMARYTo circumvent the potentially poor transient response induced by nonlinear uncertain dynamics in the adaptive control system, this article proposes a new model reference adaptive control design scheme to improve its transient control response. We first construct a compensator to online extract the undesired dynamics in the online learning, which is incorporated into the reference model and control simultaneously. Then, an error feedback term is incorporated into the reference model to speed up the convergence of both the compensator and tracking error. Moreover, a new leakage term containing the estimation error is constructed and then added in the adaptive law to guarantee the convergence of both the estimation error and tracking error. To further reveal the mechanisms behind these proposed methods, a new methodology to analyze the transient error bounds based on L2‐norm and Cauchy‐Schwartz inequality is also developed. Based on the analysis results, we find that the proposed methods can effectively reduce the bound of the tracking error and thus achieve an improved transient control performance without violating the system stability even with high‐gain adaptation. In addition, the frequency‐domain analysis is resorted to show the comparative responses of different adaptive laws, which indicate that the proposed adaptive law can maintain the stability margin even with a high‐gain learning rate. A numerical example is given to demonstrate improved control responses of these proposed schemes.

Robust Finite-Time Tracking for Uncertain Linear Systems with Actuator Faults

In this paper, a robust finite-time fault-tolerant control (FTC) scheme is developed for uncertain linear systems in the presence of actuator faults. Since the system uncertainties and actuator faults are unknown, the controller parameters are updated online by the adaptive laws without the need for fault detection and isolation. It is proved that the proposed state-feedback model reference adaptive finite-time FTC scheme can guarantee that the tracking error converges to a small neighborhood of the origin in finite time. An application example for an aircraft lateral-directional dynamic system is presented to show the effectiveness of the proposed control scheme.

Sensorless Induction Motor Drive Based on Model Reference Adaptive System Scheme Utilising a Fictitious Resistance

Abstract This article presents a new development of an indirect stator flux-oriented controller for sensorless speed induction motor drive utilising instantaneous and steady-state values, respectively, of a fictitious resistance symbolised as R_f. The dimension of the fictitious quantity, in this context, is the ohm, which is the difference between the stator d- and q-axis fictitious resistances. However, from the measurement of the stator voltage and currents of the machine, two independent resistance estimators are built. Therefore, the first is considered as a reference model of the induction machine (IM), and the second is considered as an adjustable model. Subsequently, the error between the states of the two models is used to drive a suitable adaptation mechanism that generates the estimation of the speed, for the adjustable model. Furthermore, the structure of the proposed estimator is free from stator resistance and eliminates the requirement of any flux computation. All the detailed simulation study is carried out in MATLAB/Simulink to validate the proposed method and to highlight the robustness and the stability of the proposed model reference adaptive system estimator.

Stator Resistance Compensator Based on Model Reference Adaptive System Scheme for Sensor-Less Direct Torque Control of an Open End-Winding Induction Motor with First Coil Faults

Stator Resistance Compensator Based on Model Reference Adaptive System Scheme for Sensor-Less Direct Torque Control of an Open End-Winding Induction Motor with First Coil Faults

Measurement and control of emergent phenomena emulated by resistive-capacitive networks, using fractional-order internal model control and external adaptive control

A fractional-order internal model control technique is applied to a three-dimensional resistive-capacitive network to enforce desired closed-loop dynamics of first order. In order to handle model mismatch issues resulting from the random allocation of the components within the network, the control law is augmented with a model-reference adaptive strategy in an external loop. By imposing a control law on the system to obey first order dynamics, a calibrated transient response is ensured. The methodology enables feedback control of complex systems with emergent responses and is robust in the presence of measurement noise or under conditions of poor model identification. Furthermore, it is also applicable to systems that exhibit higher order fractional dynamics. Examples of feedback-controlled transduction include cantilever positioning in atomic force microscopy or the control of complex de-excitation lifetimes encountered in many types of spectroscopies, e.g., nuclear magnetic, electron-spin, microwave, multiphoton fluorescence, Förster resonance, etc. The proposed solution should also find important applications in more complex electronic, microwave, and photonic lock-in problems. Finally, there are further applications across the broader measurement science and instrumentation community when designing complex feedback systems at the system level, e.g., ensuring the adaptive control of distributed physiological processes through the use of biomedical implants.

Speed Sensorless Vector Controlled SMPMSM Drive Based on Modified MRAS with Single Current Sensor

New speed estimation technique is proposed in this paper. This is used to formulate a single current sensor-based vector controlled surface mounted (SM) permanent magnet synchronous motor (PMSM) drive. A combination of dc-link measurement and model reference adaptive scheme (MRAS) is adopted for the implementation of speed sensorless control. Unlike existing MRAS that make use of both dq-axes currents/flux/voltages, the proposed approach has simplified adaptive mechanism based on q-axis current only. The proposed technique is less complex, stable at low speed, less expensive. The proposed technique is simulated in MATLAB/Simulink and validated experimentally through dsPIC MCHV-2 based laboratory prototype.

Discrete Adaptive Speed Sensorless Drive of Induction Motors

The use of mechanical speed sensor in the field of control of induction motors can cause some problems. Recently, many software are developed to estimate the speed by using only electrical measurements and machine parameters. This paper presents a new speed sensorless drive of induction motors for field oriented control. The proposed speed estimator is based on the Model Reference Adaptive Scheme (MRAS) and uses based the comparison of estimated and measured stator current. The reduction error is achieved by an appropriate unity transfer function as reference model. The estimator formulation is defined and developed in the discrete case. The proposed algorithm requires neither matrix calculation nor approximation on Taylor series of matrix exponential. To establish the stability of the discrete estimator, the hyperstability concept introduced by Popov in the discrete case is used. The influence of the sampling period on the algorithm stability of is studied. Some simulation results are presented, in order to valid the theoretical development.

Sensorless State Control of Stand-Alone Doubly Fed Induction Generator Supplying Nonlinear and Unbalanced Loads

Control methods of a stand-alone doubly fed induction generator mostly use either proportional-integral regulators in multiple synchronous reference frames or proportional-integral-resonant regulators in a stationary reference frame. These control schemes work well when the plant model is not complex and system decoupling is implemented into the control algorithm. However, when the control system contains multiple resonant terms in order to compensate high harmonics of load current, tuning of many regulators in different coordinate systems becomes problematic. The paper presents the application of a linear-quadratic regulator and a rotor position observer in sensorless control of a stand-alone doubly fed induction generator supplying nonlinear and unbalanced loads. Cooperation of the observer and state controller has been validated in the laboratory experiment. An anti-windup mechanism has been implemented into the resonant terms, and it has been shown that the anti-windup structure is crucial for high performance of the state controller with resonant terms. Further, research on observers based on a model reference adaptive system scheme is done in the paper.

Parallel Stator Resistance Estimator Using Neural Networks for Rotor Flux Based Model Reference Adaptive System Speed Observer

Model reference adaptive system schemes offer simpler implementation and require less computational effort compared to other speed sensorless methods. The performance of rotor flux based model reference adaptive system schemes at low-speed operation is poor because of parameter sensitivity and presence of the integrator in the reference model. As stator resistance inevitably varies with temperature, for accurate operation at low speeds, an appropriate online identification algorithm for the stator resistance is required. In this article, a neural network based parallel stator resistance and rotor speed estimator has been proposed to simultaneously rectify the limitation of model reference adaptive system schemes, i.e., stator resistance variation and DC offset due to integrator, employing a neutral network in stator resistance estimator and modifying the reference model by adding a compensating voltage term. An indirect sensorless vector control scheme has been simulated and experimentally validated using the dSPACE DS-1104 R&D controller board (dSPACE GmbH, Paderborn, Germany) to verify the performance of drives at different operating conditions.

Improved Rotor Flux Estimation at Low Speeds for Torque MRAS-Based Sensorless Induction Motor Drives

In this paper, an improved rotor flux estimation method for the Torque model reference adaptive schemes (TMRAS) sensorless induction machine drive is proposed to enhance its performance in low and zero speed conditions. The conventional TMRAS scheme uses an open loop flux estimator and a feedforward term, with basic low pass filters replacing the pure integrators. However, the performance of this estimation technique has drawbacks at very low speeds with incorrect flux estimation significantly affecting this inherently sensorless scheme. The performance of the proposed scheme is verified by both simulated and experimental testing for an indirect vector controlled 7.5-kW induction machine. Results show the effectiveness of the proposed estimator in the low- and zero-speed regions with improved robustness against motor parameter variation compared to the conventional method.

Comparative assessment of two different model reference adaptive system schemes for speed‐sensorless control of induction motor drives

This study presents a comparison between a well-known instantaneous reactive power (Q)-based model reference adaptive system (MRAS) (Q-MRAS) and a relatively new X-MRAS used for the speed estimation of induction motor drive without speed sensor from the stability point of view. Stability aspects and sensitivity analysis of both the schemes to machine parameters variation are examined. The instantaneous and steady-state values of Q are considered to develop the reference and adaptive models respectively for the former, whereas those of a fictitious quantity X (i.e. * × ) are considered in the said models for the later. Non-computation of flux in these schemes leads to the satisfactory estimation of speed over a wide range maintaining the drive's stability in all the 4-quadrants. Both the Q- and X-MRAS are found to be dependent on the rotor resistance variation, whereas X-MRAS is also influenced by the stator resistance variation. A small signal stability study over a wide range of stator and rotor resistance variations is carried out to investigate the drive's stability in the low-speed region for both the MRAS schemes. Matlab/Simulink is used to obtain the simulation results for the proposed study. Experimental results are obtained through a dSPACE-1104-based laboratory prototype.

Investigation and analysis of high performance green energy induction motor drive with intelligent estimator

This paper attempts to enhance the performance of a green energy induction motor drive. The electronic power converters become indispensable part of the renewable energy systems (RES). The solar photovoltaic (PV) system is efficiently operated with artificial neural network (ANN) based maximum power point tracking (MPPT) algorithm. The inverter topologies for the green drive scheme are analyzed. To improve the drive performance a reduced switch multilevel inverter (RSMLI) is employed. As indirect field oriented control (IFOC) is used, the drive demands on-line estimation of rotor resistance. A neural learning model reference adaptive scheme (NL-MRAS) based rotor resistance estimator is found to exhibit good dynamic performance. This work also investigates the performance of the green drive with an intelligent estimator. The performance enhancement of the green energy drive obtained by ANN based MPPT for the PV system, a reduced switch MLI and an intelligent estimator is presented.

Performance enhanced model reference adaptive control through switching non-quadratic Lyapunov functions

Based on the model reference adaptive scheme, a robust adaptive tracking controller for systems with arbitrary relative degree and known high-frequency gain is proposed. To cope with the difficulty raised by swapping lemma for systems with relative degree beyond one, a new variable related to the tracking error is defined. It is also indicated that if the new defined variable converges to zero asymptotically with time, so does the tracking error. In the line of achieving adaptation laws, a non-quadratic Lyapunov function with one degree of freedom called α is considered, where α=1 corresponds to a quadratic Lyapunov function. Effects of choosing different values for α on the response of the system is studied analytically. It is shown that using a multi-criterion α may lead to a faster response compared to that of α=1. For enhancing the tracking performance, a switching scheme is designed in a constructive manner. Using projection technique to prevent parameter drift, it is shown analytically that the proposed scheme is robust against bounded disturbances. It is also indicated that for systems with relative degree one, knowing the high-frequency gain can be relaxed. Two examples are presented to show the effectiveness of the proposed approach: (1) a numerical example as a system with relative degree one, (2) the speed control of DC motor as a system with a relative degree larger than one.

Compliance Control and Human–Robot Interaction: Part II — Experimental Examples

Compliance control is highly relevant to human safety in human–robot interaction (HRI). This paper presents multi-dimensional compliance control of a humanoid robot arm. A dynamic model-free adaptive controller with an anti-windup compensator is implemented on four degrees of freedom (DOF) of a humanoid robot arm. The paper is aimed to compliment the associated review paper on compliance control. This is a model reference adaptive compliance scheme which employs end-effector forces (measured via joint torque sensors) as a feedback. The robot's body-own torques are separated from external torques via a simple but effective algorithm. In addition, an experiment of physical human robot interaction is conducted employing the above mentioned adaptive compliance control along with a speech interface. The experiment is focused on passing an object (a cup) between a human and a robot. Compliance is providing an immediate layer of safety for this HRI scenario by avoiding pushing, pulling or clamping and minimizing the effect of collisions with the environment.

Modified MRAS Speed Controller for Direct Torque Controlled Sensorless Induction Motor Drives

This paper presents a Modified model reference adaptive system (MMRAS) speed controller for speed estimation of induction motor using model-reference adaptive scheme (MRAS) approach in a direct torque control system. A good speed controller of induction motor can enhance the speed estimation of sensorless drives. The MMRAS controller can reduce effect of parameter variations and nonlinearities in the induction motor, and approaches to desired performance of referenced model. So, the presented controller can enhance the performance of speed estimation. The availability of the proposed structure scheme is verified by through under computation simulations with MATLAB software. Simulation results are presented in order to prove the availability of the proposed structure scheme.

Adaptive Neural Network Controller for Modeling Link Quality in WSANs

paper develops a new approach based on off-line trained artificial neural networks (ANNs) for an adaptive controller to compensate the packet loss and delay which almost occur in the channels of wireless sensor/actuator networks (WSANs). A model reference adaptive controller scheme is used to enhance the performance of the control system. The capability of training of ANNs is proposed to create a new controller which can be called model reference adaptive neural network controller (MRANNC) in WSANs to improve the link reliability and make the overall WSAN performance better. The dynamics of the plant are stored in the ANN. The output of the plant is regulated according to a prespecified reference signal. The simulation results show a promising approach that can be used to enhance the link quality in wireless control system. The packet loss of channel can be considerably adapted at different sampling times with and without time delays.

Sensorless Direct Torque Control of Hybrid Stepper Motor Based on MRAS

In classical methods only average torque of the stepper motors is controlled which causes high speed and torque ripple. In order to control the torque instantaneously and improve the performance of the hybrid stepper motor, direct torque control strategy is used in this paper. Then by taking model reference adaptive system scheme, which uses the hybrid stepper motor itself as the reference model, speed of the motor is estimated. The sensorless control of hybrid stepper motor based on MRAS is built and simulated with Matlab software. The results show that this control technology is simple and effective and accuracy of the estimation is considerably high.