• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.181-184
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• 1996

This paper deals with the design problem of model reference adaptive controllers for MIMO plants with unknown orders. A design scheme for an adaptive control system based on CGT theorem, which has hierarchical structures derived from backstepping strategies, is proposed for MIMO plants with unknown orders but with known relative MacMillan degrees(relative degrees for SISO plants). It is also shown that all the signals in the resulting control system are bounded, and that the asymptotic tracking is achieved in the case where reference inputs are step.

• Journal of Power Electronics
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• v.18 no.5
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• pp.1398-1408
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• 2018

In order to further improve the steady-state control performance of model predictive torque control (MPTC), a double-vector-based model predictive torque control without a weighting factor is proposed in this paper. The extended voltage vectors synthesized by two basic voltage vectors are used to increase the number of feasible voltage vectors. Therefore, the control precision of the torque and the stator flux along with the steady-state performance can be improved. To avoid testing all of the feasible voltage vectors, the solution of deadbeat torque control is calculated to predict the reference voltage vector. Thus, the candidate voltage vectors, which need to be evaluated by a cost function, can be reduced based on the sector position of the predicted reference voltage vector. Furthermore, a cost function, which only includes a reference voltage tracking error, is designed to eliminate the weighting factor. Moreover, two voltage vectors are applied during one control period, and their durations are calculated based on the principle of reference voltage tracking error minimization. Finally, the proposed method is tested by simulations and experiments.

• International Journal of Control, Automation, and Systems
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• v.6 no.3
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• pp.327-338
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• 2008

A fault tolerant control system design technique has been proposed and analyzed for managing performance degradation in the presence of multiple faults in actuators. The method is based on a control structure with a model reference reconfigurable control design in an inner loop and command input adjustment in an outer loop. The reduced dynamic performance requirements in the presence of different actuator faults are accounted for through different performance reduced (degraded) reference models. The degraded steady-state performances are governed by the reduced levels of command input. The reconfigurable controller is designed on-line automatically in an explicit model reference control framework so that the dynamics of the closed-loop system follow that of the performance reduced reference model under each fault condition. The reduced command input level is determined to prevent potential actuator saturation. The proposed method has been evaluated and analyzed using an aircraft example against actuator faults subject to constraints on the magnitude and slew-rate of actuators.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.7
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• pp.499-507
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• 2019

Despite the well-known mathematical model of fixed-wing aircraft, there are various studies to meet desired performances by considering the modeling errors in the extended flight envelope. This paper proposes a new adaptation mechanism of model-reference adaptive control, which applies the Levenberg-Marquardt algorithm to the pitch attitude control of fixed-wing UAV. In addition, reference model in the adaptation law is set by referring to the dynamic properties of the plant model. The performance of the proposed adaptive control law is verified through simulations and flight tests.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.2
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• pp.399-407
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• 1996

A fuzzy learning algorithm to get the optimal fuzzy rules is presented in this paper. The algorithm introduces a reference model to generate a desired output and a performance index funtion instead of the performance index table. The performance index funtion is a cost function based on the error and error-rate between the reference and plant output. The cost function is minimized by a gradient method and the control input is also updated. In this case, the control rules which generate the desired response can be obtained by changing the portion of the error-rate in the cost funtion. In SISO(Single-Input Single- Output)plant, only by the learning delay, it is possible to experss the plant model and to get the desired control rules. In the long run, this algorithm gives us the good control rules with a minimal amount of prior informaiton about the environment.

• 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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.864-867
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• 1995

Presented in the paper is a structured approach to modeling automated manufacturing system (AMS) in the form of an event graph. The proposed two-phase procedure for formal modeling is 1) reference modeling by schematic supervisory control modeling and 2) event graph transformation from supervisory control model. Also described is a formal model for a small-sized FMS in the form of an event graph.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.2
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• pp.88-95
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• 1996

It is inevitable for local systems to have deviations which represent interactions and modeling errors originated from the decomposition process of a large scale system. This paper presents a decentralized control scheme for interconnected systems using local linear models and a neuro-coordinator. In the proposed method, the local system is composed of a linear model and unknown deviations caused by linearizing the subsystems around operating points or by estimating parameters of the subsystems. Because the local system has unmeasurable deviations we define a local reference model which consists of a local linear model and a neural network to estimate the deviations indirectly. The reference model is reformed into a linear model which has no deviations through a transformation of input variables and we obtain an optimum feedback control law which minimizes a local performance index. Finally, we derive a decentralized feedback control law which consists of local linear states and neural network outputs. In the decentralized control, the neuro-coordinator generates a corrective control signal to cancel the effect of deviations through backpropagation learning with the errors obtained from the differences of the local system outputs and reference model outputs. Also, the stability of local system is proved by the degree of learning of the neural network under an assumption on a neural network learning index. It is shown by computer simulations that the proposed control scheme can be applied successfully to the control of a biased two-link planar robot manipulator.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.40 no.8
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• pp.535-542
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• 2016

In this study, a model reference adaptive controller is developed to regulate the outlet air flow rate of centrifugal compressor for automotive supercharger. The centrifugal compressor is developed using the analytical based method to predict the transient behavior of operating and the designed model is validated with experimental data to confirm the system accuracy. The model reference adaptive control structure consists of a compressor model and a MRAC(model reference adaptive control) mechanism. The feedback control do not robust with variation of system parameter but the applied adaptive control is robust even if the system parameter is changed. As a result, the MRAC was regulated to reference air flow rate. Also MRAC was found to be more robust control compared with the feedback control even if the system parameter is changed.

• Journal of Advanced Marine Engineering and Technology
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• v.27 no.5
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• pp.609-616
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• 2003

The purpose of this study is to design the adaptive speed control system of a marine diesel engine by combining the Model Matching Method and the Nominal Model Tracking Method. The authors proposed already a new method to determine efficiently the PID control Parameters by the Model Matching Method. typically taking a marine diesel engine as a non-oscillatory second-order system. But. actually it is very difficult to find out the exact model of a diesel engine. Therefore, when diesel engine model and actual diesel engine are unmatched as an another approach to promote the speed control characteristics of a marine diesel engine, this paper Proposes a Model Reference Adaptive Speed Control system of a diesel engine, in which PID control system for the model of a diesel engine is adopted as the nominal model and Fuzzy controller and derivative operator are adopted as the adaptive controller.