Earticle

The MRAC theory has proved to be one of the most popular algorithms in the field of adaptive control, particularly for practical application to devices such as an hydraulic servosystem of which parameters are unknown or varying during operation. For small sampling period, the discrete time system becomes a nonminimal phase system. The $\delta$-MRAC was introduced to obtain the control performance of nonminimal phase system, because the z-MRAC can not control the plant for small sampling period. In this paper, $\delta$-MRAC is applied to the control of an hydraulic servosystem which is composed of servovalve, hydraulic cylinder and inertia load.

A model reference adaptive control(MRAC) theory is very useful for controlling a plant of which the parameters are unknown or vary during operation usint only input-output signal of plant. In this study, 2' nd order discreter time MRAC controller is designed for an electrohydraulic position control system which is represented with nonlinear mathematical model and the least square method is adopted for the para-meter adjustment law. This control algorthm is applied to the position control of electrohydraulic servosystem through computer simulation and the effect of the change of load, sampling time upon the performance following reference model and upon the performance of estimating plant parameters are examined.

This paper presents the analysis and simulation of the control of double star induction motor, using direct torque control (DTC) based on model reference adaptive control algorithm (MRAC). The DTC is an excellent solution for general- purpose induction drives in very wide range the short sampling time required by the TC schemes makes them suited to a very fast torque and flux controlled drives as well the simplicity of the control algorithm. DTC is inherently a motion sensorless control method. The model reference regulator (MRAC) can improve the double star induction motor performance in terms of overshoot, rapidity, cancellation of disturbance, and capacity to maintain a high level of performance. Simulation results indicate that the proposed regulator has better performance responses. The implementation of the DTC applied to a double star induction motor based on model reference regulator is validated with simulated results.

This paper focuses on a sensorless control method for small wind turbines based on permanent magnet synchronous generators. An adaptive intelligent algorithm is proposed to estimate the rotor speed for designing sensorless controllers of small wind turbines. In the proposed algorithm, a model reference adaptive system has been formed by using the instantaneous and steady-state powers to estimate the rotor speed. Then a power speed feedback (PSF) control carries out maximum power point tracking by using the optimum relationship between speed and power, which is kept in a lookup table. The efficiency of the system is improved by vanishing the observation errors and tracking errors. The validity of the proposed algorithm is verified by the simulation results.

In this paper, the model reference adaptive control (MRAC) of a flexible structure is investigated. Any mechanically flexible structure is inherently distributed parameter in nature, so that its dynamics are described by a partial, rather than ordinary, differential equation. The MRAC problem is formulated as an initial value problem of coupled partial and ordinary differential equations in weak form. The well-posedness of the initial value problem is proved. The control law is derived by using the Lyapunov redesign method on an infinite dimensional filbert space. Uniform asymptotic stability of the closed loop system is established, and asymptotic tracking, i. e., convergence of the state-error to zero, is obtained. With an additional persistence of excitation condition for the reference model, parameter-error convergence to zero is also shown. Numerical simulations are provided.

Related to the error dynamics of an adaptive system, averaging theorems are developed for coupled differential equations which consist of ordinary differential equations and a parabolic partial differential equation. The results are then applied to the convergence analysis of the parameter estimate errors in the model reference adaptive control of a nonautonomous parabolic partial differential equation with lowly time-varying parameters.

Adaptive controllers for linear system whose nominal values of coefficients only are known, that is corrupted by disturbance, are designed by signal synthesis model reference adaptive control (MRAC). This design is stemmed from the Lyapunov direct method. To reduce the model following error and to improve the conrergence rate of the design, an indirect suboptimal control law is de rived using the Hamilton Jacobi Beellman equation. Proper compensaton for the effects of time varying coefficients and plant disturbance are suggested. In the design procedure no complete identification of unknown coefficients are required.

Adaptive controllers for linear unknown coefficient system, that is corrupted by disturbance, are designed by parameter adaptation model reference adaptive control(MRAC). This design is stemmed from the Lyapunov direct method. To reduce the model following error and to improve the convergence rate of the design, an indirect-suboptimal control law is derived. Proper compensation for the effects of time-varying coefficients and plant disturbance are suggested. In the design procedure no complete identification of unknown coefficients are required.

본 논문에서는 센서리스 벡터제어를 위한 MRAC를 이용해 속도추정기를 설계한다. 이 속도추정기는 파라미터 변동에 강인하며, 그 속도값은 센서리스 벡터제어구현을 위해 필요한 속도정보로 피드백되어 사용된다. 컴퓨터시뮬레이션을 통해 그 타당성을 입증하였다.

This paper proposed a speed estimator using MRAC(Model Reference Adaptive Control) for sensorless vector control. It is robust for parameter variation and the estimated speed is used as feedback in a vector control system. Computer simulation is presented to confirm the theoretical analysis.

The problem of model reference adaptive control for the discrete time system is considered and the global stability of the overall system is shown. It extends the results of Choi's to the plants and models whose transfer functions have arbitrary gains and the method presented here makes the output error and the conftroller parameter error converge to zero. The scheme presented in this paper is simulated and gives good results.

Using Power Spectrum Analysis, conditions of parameter convergence for a Model Reference Adaptive Control (MRAC) system are described. The general Persistent Excitation (PE) condition given in time domain can be transformed to the positiveness of auto-correlation matrix which is represented in frequency domain by the spectra of reference input signal. For an MRAC system designed with relative degree one, the existence and the uniqueness of parameter nominal values due to the variation of input spectra can be analyzed by the PE condition in frequency domain. If the input signal has 2n spectra or more, it can be shown that the nominal values exist independent of adaptive gain, input amplitudes, and magnitudes or numbers of their spectra.

This paper aims to develop an adjustable gain-based model reference adaptive control (AG-MRAC) scheme for maximum power point tracking (MPPT) in photovoltaic (PV) systems. To ensure a fast convergence speed with guaranteed transient performance and overall system stability of the MPPT under rapidly changing environmental conditions, a 2-level control scheme has been proposed. The first level of control is the conventional ripple correlation control (RCC), which is used to obtain a ripple-free optimal duty cycle in the steady-state. This also serves as input for the second level of control, which is the proposed AG-MRAC controller. The conventional high-static adaptation gain MRAC provides guaranteed transient performance in MPPT. However, a high adaptation gain circumvents the adverse effects on the system stability and robustness. Moreover, in PV systems, when the environmental conditions are rapidly changing, the requirement gain depends on the magnitude of the error. Therefore, a fixed high gain controller does not provide a complete solution to the dynamic behavior of non-linear PV systems under rapidly changing environmental conditions. This paper attempts to overcome these issues using the proposed AG-MRAC architecture, where the adaptation gain is adjusted as a function of the tracking error, which is caused by variations in environmental conditions. A mathematical model of the proposed AG-MRAC has been developed and its stability has been verified using Lyapunov theory. To check the effectiveness of the proposed control scheme, simulation and experimental models have been developed for validation. In addition, a performance comparison has been done with recent similar works.

In this paper, a parameter estimator is developed for the plant model whose structure is represented by the Takagi-Sugeno model. The essential idea behind the on-line estimation is the comparison of the measured stated with the state of an estimation model whose structure is the same as that of the parameterized model. Based on the parameter estimation scheme, and indirect Model Reference Adaptive Fuzzy control(MRAFC) scheme is proposed to provide asymptotic tracking of a reference signal for the systems with uncertain for slowly time-varying parameters. The developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop systems. In addition, the plant state tracks the state of the reference model asymptotically with time for any bounded reference input signal.

In this paper a Neural Network based Model Reference Adaptive Control scheme (NN-MRAC) is proposed. In this scheme, the controller is designed by using parallel combination of the conventional Model Reference Adaptive Control (MRAC) scheme and Neural Network (NN) controller. In the conventional MRAC scheme, the controller is designed to realize plant output converging to reference model output based on the plant which is linear. This scheme is used to control linear plant effectively with unknown parameters. However, it is difficult for a nonlinear system to control the plant output in real time applications. In order to overcome the above limitations, the NN-MRAC scheme is proposed to improve the system performances. The control input of the plant is given by the sum of the MRAC output and NN controller output. The NN controller is used to compensate the nonlinearities and disturbances of the plant that are not taken into consideration in the conventional MRAC. The simulation results clearly show that the proposed NN-MRAC scheme have better steady state and transient performances than those of the current adaptive control schemes. Thus, the proposed NN-MRAC scheme named as Robust Model Reference Adaptive Intelligent Control (RMRAIC) is found to be extremely effective, efficient and useful in the field of control system.

The aim of this paper is to design an adaptive speed control system of a marine diesel engine by fusion of hard computing based proportional integral derivative (PID) control and soft computing based fuzzy control methods. The model of a marine diesel engine is considered as a typical non oscillatory second order system. When its model and the actual marine diesel engine ate not matched, it is hard to control the speed of the marine diesel engine. Therefore, this paper proposes two methods in order to obtain the speed control characteristics of a marine diesel engine. One is an efficient method to determine the PID control parameters of the nominal model of a marine diesel engine. Second is a reference adaptive speed control method that uses a fuzzy controller and derivative operator for tracking the nominal model of the marine diesel engine. It was found that the proposed PID parameters adjustment method is better than the Ziegler & Nichols' method, and that a model reference adaptive control is superior to using only PID controller. The improved control method proposed here, could be applied to other systems when a model of a system does not match the actual system.

In this paper we are interested in robust adaptive fuzzy control of nonlinear SISO systems in the presence of parametric uncertainties. The plant model structure is represented by the Takagi-Sugeno (T-S) type fuzzy system. An indirect adaptive fuzzy controller based on model reference control scheme is proposed to provide asymptotic tracking of reference signal. The controller parameters are computed at each time. The plant state tracks asymptotically the state of the reference model for any bounded reference input signal. Inverted pendulum and mass spring damper are used to check the performance of the proposed controller.

This paper investigated the speed sensorless indirect vector control of a two-phase induction motor to implement adjustable-speed drive for low-power applications. The sliding mode observer estimates rotor speed. The convergence of the nonlinear time-varying observer along with the asymptotic stability of the controller was analyzed. To define the control action which maintains the motion on the sliding manifold, an 'equivalent control' concept was used. It was simulated and implemented on a sensorless indirect vector drive for 150W two-phase induction motor. The simulation and experimental results demonstrated effectiveness of the estimation method.

The control scheme using fuzzy modeling and Parallel Distributed Compensation (PDC) concept is proposed to provide asymptotic tracking of a reference signal for the flexible joint manipulators with uncertain parameters. From Lyapunov stability analysis and simulation results, the developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop multi-input/multi-output system. In addition, the plant state tracks the state of the reference model asymptotically with time for any bounded reference input signal.

본 연구에서 구현하고자 하는 선박의 회두각 제어의 경우 파도, 바람, 조류 등의 외란의 영향을 많이 받고 있을 뿐만 아니라 그 운동 특성 역시 비선형이므로 적절한 파라미터의 선정과 제어기 구성에 어려움이 따른다. 이의 해결을 위해 K. M. Passino 등에 의해 비선형 특성을 지닌 기준 모델 적응 퍼지 알고리즘을 적용하여 제어기 구성을 시도한바 있고, 국내에서도 김종화 등에 의해 유사한 방법이 시도되어졌다. 본 연구에서는 이상의 시도에서 기준 모델에 의한 제어기 파라미터의 동정의 방법으로 사용한 M.I.T 룰 대신 일반적인 유전 알고리즘에 의해 퍼지 제어기의 파라미터를 동정하고자 한다. 유전 알고리즘에 기반한 기준 모델 적응 퍼지 제어기(MRGAFC) 알고리즘을 제안하며, 이의 검증을 위하여 화물선 회두각의 조향 문제에 이를 적용하여 종래의 방법들과 비교를 수행할 것이다.