• Transactions on Control, Automation and Systems Engineering
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• v.4 no.4
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• pp.330-340
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• 2002

In this Paper. a non-linear approach to a design of model reference adaptive control is presented. The approach is demonstrated by a case study of a simple single-pole and no zero, linear, discrete-time plant. The essence of the idea is to generate a full non-linear model of the plant dynamics and the parameter adaptation dynamics as a gradient descent algorithm with respect to a Riemannian metric. It is shown how a Riemannian metric can be chosen so that the modelled plant dynamics do in fact match the true plant dynamics. The performance of the proposed scheme is compared to a traditional model reference adaptive control scheme using the classical sensitivity derivatives (Euclidean gradients) for the descent algorithm.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.1 s.232
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• pp.88-95
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• 2005

This paper presents a design scheme of model reference adaptive control incorporating a Neural Network for a pneumatic servo system. The parameters of discrete-time model of plant are estimated by using the recursive least square method. Neural Network is utilized in order to compensate the nonlinear nature of plant such as compressibility of air and frictions present in cylinder. The experiment of a trajectory tracking control using the proposed control scheme has been performed and its effectiveness has been proved by comparing with the results of a model reference adaptive control.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.2
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• pp.107-113
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• 2004

This paper develops a adaptive fuzzy controller based fuzzy logic control for high performance of permanent magnet synchronous motor(PMSM) drives. In the proposed system, fuzzy control is used to implement the direct controller as well as the adaptation mechanism. The operation of the direct fuzzy controller and the fuzzy logic based adaptation mechanism is studied. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive fuzzy controller is evaluated by simulation for various operating conditions. The validity of the proposed adaptive fuzzy controller is confirmed by performance results for PMSM drive system.

• Journal of Power Electronics
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• v.13 no.1
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• pp.122-138
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• 2013

Adaptive control of a peak current mode controlled (PCM) boost converter supplied by a PEM fuel cell is described in this paper. The adaptive controller with reference model and signal adaptation is developed in order to compensate the deviation of the response during the change of the operating point. The procedure for determining the adaptive algorithm's weighting coefficients, based on a combination of the pole-zero placement method and an optimization method is proposed. After applying the proposed procedure, the optimal adaptive algorithm's weighting coefficients can be determined in just a few iterations, without the use of a computer, thus greatly facilitating the application of the algorithm in real systems. Simulation and experimental results show that the dynamic behavior of a highly nonlinear control system with a fuel cell and a PCM boost converter, can fairly accurately be described by the dynamic behavior of the reference model, i.e., a linear system with constant parameters.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.12
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• pp.2708-2715
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• 2012

This paper proposes a new sensorless speed control scheme of induction motor using Model Reference Adaptive Control and Direct Torque Control System. The Model Reference Adaptive Control System is based on the comparison between the outputs of Reference Model and Adjustable Model. The error between the estimated quantities obtained by the two models is used to drive a suitable adaptation mechanism which generates the estimated rotor speed for the Adjustable Model. And the Direct Torque Control scheme controls torque and flux by restricting the flux and torque errors within respective hysteresis bands, and motor torque and flux are controlled by the stator voltage space vector using optimum inverter switching table. The simulation results of proposed method indicate good speed responses from the low speed range to the high, and also show favorable characteristics of load operation.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.57 no.3
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• pp.250-254
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• 2008

Robot actuators are significantly important with respect to whole control system performance. This paper presents an adaptive control approach for flexible robot actuators with time-varying spring and damping nature. We first represent a perturbed system model with assumption that its information are partially known. Nominal model reference control method is employed for deriving our adaptive control law. We carry out numerical simulation to evaluate the proposed control system and compare simulation results to a well-known control method for demonstrating its effectiveness.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.12 no.6
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• pp.77-83
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• 2003

This paper deals with a position control problem of a hydraulic proportional position control system using a nonlinear friction compensation control. As nonlinear friction, stiction and coulomb friction forces are considered and modeled as deadzone and external disturbance respectively. In order to compensate this nonlinearities, we designed the controller which is the adaptive friction compensator using discrete time Model Reference Adaptive Control method in this paper. Digital Signal Processing board is employed for data acquisition and manipulation. The experimental results show that response is slow and steady-state error cannot be compensated properly without friction compensation but this compensator is effective to obtain fast response and good steady-state response.

• Journal of Power Electronics
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• v.14 no.3
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• pp.549-563
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• 2014

In this paper, the speed regulation problem of permanent synchronous motor (PMSM) systems under the vector control framework is studied. A model reference adaptive controller (MRAC) based on the Lyapunov stability theory is first designed. Since the standard MRAC method provides poor disturbance rejection performance in the case of strong disturbances, a composite control method which combines the MRAC method and the disturbance estimation method, called the MRAC+ESO method, is proposed. An extended state observer (ESO) is introduced to estimate the lumped disturbances. The obtained estimated value acts as a feedforward compensation term to the MRAC controller. A stability analysis of the composite control method is given. Simulation and experimental results are presented and compared to show the effectiveness of the proposed control method.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.2
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• pp.95-105
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• 2001

In this paper, in order to control uncertain chaotic system, an adaptive fuzzy control(AFC) scheme is developed for the multi-input/multi-output plants represented by the Takagi-Sugeno(T-S) fuzzy models. The proposed AFC scheme provides robust tracking of a desired signal for the T-S fuzzy systems with uncertain parameters. The developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop system. In addition, the chaotic state tracks the state of the stable reference model(SRM) asymptotically with time for any bounded reference input signal. The suggested AFC design technique is applied for the control of an uncertain Lorenz system based on T-S fuzzy model such as stabilization, synchronization and chaotic model following control(CMFC).

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.8
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• pp.1167-1177
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• 1989

In this paper, a generalized adaptive law is proposed which uses a rational function type operator for parameter adjustment. To satisfy the passivity condition of the adaptation block, we introduce a constant feedback gain into the adaptation block. This adaptation scheme is applied to the model reference adaptive control of a continuous-time, linear time-invariant, minimum-phase system whose relative degree is 1. We prove the asymptotic stability of the output error of this adaptive system by hyperstability method. It is shown that by digital computer simulations this law can give a better output error transient response in some cases than the conventional gradient adaptive law. And the output error responses for the several types of the proposed adaptation law are examined in the presence of a kind of unmodeled dynamics.