• Journal of Mechanical Science and Technology
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• 제19권spc1호
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• pp.357-363
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• 2005

The non-minimum phase characteristic of a flexible manipulator makes tracking control of its tip difficult. The level of the tip tracking performance of a flexible manipulator is significantly affected by the characteristics of the tip reference trajectory as well as the characteristics of the flexible manipulator system. This paper addresses the question of how to best specify a reference trajectory for the tip of a flexible manipulator to follow in order to achieve the objectives of reducing : tip tracking error, residual tip vibration, and the required actuation effort at the manipulator joint. A novel method of tip-based learning controller for the flexible manipulator system is proposed in the paper, where a model of the flexible manipulator system with a command shaping filter is used to generate a smooth and realizable tip reference trajectory for a tip-based learning controller.

• Journal of Power Electronics
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• 제7권2호
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• pp.159-173
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• 2007

In this paper, an intelligent sliding-mode position controller (ISMC) for achieving favorable decoupling control and high precision position tracking performance of permanent-magnet synchronous motor (PMSM) servo drives is proposed. The intelligent position controller consists of a sliding-mode position controller (SMC) in the position feed-back loop in addition to an on-line trained fuzzy-neural-network model-following controller (FNNMFC) in the feedforward loop. The intelligent position controller combines the merits of the SMC with robust characteristics and the FNNMFC with on-line learning ability for periodic command tracking of a PMSM servo drive. The theoretical analyses of the sliding-mode position controller are described with a second order switching surface (PID) which is insensitive to parameter uncertainties and external load disturbances. To realize high dynamic performance in disturbance rejection and tracking characteristics, an on-line trained FNNMFC is proposed. The connective weights and membership functions of the FNNMFC are trained on-line according to the model-following error between the outputs of the reference model and the PMSM servo drive system. The FNNMFC generates an adaptive control signal which is added to the SMC output to attain robust model-following characteristics under different operating conditions regardless of parameter uncertainties and load disturbances. A computer simulation is developed to demonstrate the effectiveness of the proposed intelligent sliding mode position controller. The results confirm that the proposed ISMC grants robust performance and precise response to the reference model regardless of load disturbances and PMSM parameter uncertainties.

• 대한기계학회논문집A
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• 제29권1호
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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.

• 한국정밀공학회지
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• 제17권4호
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• pp.121-128
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• 2000

In this paper, the control algorithm fur an autonomous vehicle is studied and applied to an actual 2 wheel-driven vehicle system. In order to control a nonholonomic system, the kinematic model for an autonomous vehicle is constructed by relative velocity relationship about the virtual point at distance from the vehicle's frame. And the optimal controller that based on the kinematic model is operated on purpose to track a reference vehicle's path. The actual system is designed with named 'HYAVI' and the system controller is applied. Because all the results of simulation don't satisfy the driving conditions of HYAVI, a reformed control algorithm that satisfies an actual autonomous vehicle is applied at HYAVI. At the results of actual experiments, the path tracking works very well by the reformed control algorithm. An autonomous vehicle that applied this control algorithm can be easily used for a path generation algorithm.

• Journal of Advanced Marine Engineering and Technology
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• 제27권5호
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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.

• Transactions on Control, Automation and Systems Engineering
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• 제3권1호
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• pp.32-42
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• 2001

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.

• 제어로봇시스템학회논문지
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• 제3권2호
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• pp.124-131
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• 1997

This paper presents a modified adaptive control scheme that improves the transient performance of the overall system while maintaining the asymptotic convergence of the output error. The proposed control scheme is characterized as the added outer dynamic feedback loop on the conventional adaptive control scheme. This control scheme enables various robust control methods that were developed for standard model reference adaptive controllers to be applied to the proposed controller. In contrast with the modified adaptive controllers that use augmented errors to provide additional dynamic feedback, the proposed controller uses tracking error directly, thereby reducing the tracking error significantly in the transient state and making the error insensitive to noise.

• 한국지능시스템학회논문지
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• 제17권1호
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• pp.130-135
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• 2007

In this paper, a direct model reference adaptive fuzzy control (MRAFC) scheme is developed for the plant model whose structure is represented by the MIMO Takagi-Sugeno fuzzy model. The MRAFC scheme is proposed to provide asymptotic tracking of a reference signal lot the systems with uncertain or slowly time-varying parameters. The developed control law and adaptive law guarantee that all signals in the closed-loop system are bounded. In addition, the plant state tracks the state of the reference model asymptotically with time tot any bounded reference input signal.

• Journal of Power Electronics
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• 제18권5호
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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.

• 대한기계학회논문집A
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• 제20권1호
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• pp.115-126
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• 1996

In the time optimal control problem, bang-bang control has been used becaese it is the theoretical time minimum solution. However, to improve tracking speed performance in the time optimal control, it is important to select a switching point accurately which makes the velocity zero near the target track. But it is not easy to select the swiching point accurately because of the damping coefficient variation and uncertainties of modeling an actual system. The Adaptive model following control(AMFC) is implemented to relieve the difficulty and inconvenience of this task. The AMFC and make the controlled plant follow as closely as possible to a desired reference model whose switching point can be calculated easily and accurately, assuring the error between the states of the reference model and those of the controlled plant appoaches zero. The hybrid control method composed of AMFC and PID is applied to a tracking actuator of the magneto optical disk drive(MODD) in random access devices to improve its slow tracking performance. According to the simulaion and experimental results, the average tracking time as small as 20ms is obtained for a 3.5 magneto-optical disk drive. The AMFC also can be applied for other random access devices to improve the average tracking performance.