• Transactions of the Korean Society of Mechanical Engineers A
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• v.23 no.6 s.165
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• pp.931-942
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• 1999

This paper develops a 3 DOF vehicle model which includes lateral, roll and yaw motion to study a 4WS vehicle. The model is used for the simulation of a 4WS vehicle behavior, and to derive a control algorithm for rear wheel steering. This paper uses a feedforward plus feedback control scheme to compute a rear wheel steering angle. The feedforward control scheme for computing the first rear wheel steering angle uses a gain which is acquired by multiplying a proper value on a gain to maintain a zero sideslip angle. The feedback control scheme for computing the second rear wheel steering angle uses fuzzy logic and model following control scheme. A linear 2 DOF model is used as a reference model for model following control, and is derived from the developed 3 DOF model by neglecting sprung mass roll motion. A reference state variable is yaw rate, and is computed using the linear 2 DOF model. J-turn and lane change maneuver simulation are performed to show the effectiveness of the developed control scheme. The simulation results show that the 4WS vehicle with the developed control scheme has much better performance in yaw rate, lateral acceleration, roll angle, and sideslip angle than the 2WS vehicle. Also, the results show that the performance of the developed control is close to the one of an optimal control which assumes all states are perfect.

• Journal of IKEEE
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• v.1 no.1 s.1
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• pp.55-63
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• 1997

The purpose of this paper is to realize robust vector control of an induction motor without speed sensor. In order to do it, the speed of an induction motor is estimated using model reference adaptive system(MRAS) and two rotor flux observers which have robustness to the parameter variation are employed as the reference model and the adjustable model in MRAS speed estimator. The MRAS-based overall control scheme has been implemented on 2.2kW induction motor control system and it is verified that the proposed speed sensorless control scheme is more stable and robust than the conventional schemes.

• Journal of Electrical Engineering and Technology
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• v.10 no.6
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• pp.2221-2227
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• 2015

To arrest a frequency nadir, a stepwise inertial control (SIC) scheme generates a constant active power reference signal of a wind turbine generator (WTG) immediately after a disturbance and maintains it for the predetermined time. From that point, however, the reference of a WTG abruptly decreases to restore the rotor speed for the predefined period. The abrupt decrease of WTG output power will inevitably cause a second frequency dip. In this paper, we propose a modified SIC scheme of a doubly-fed induction generator (DFIG) that can prevent a second frequency dip. A reference value of the modified SIC scheme consists of a reference for the maximum power point tracking control and a constant value. The former is set to be proportional to the cube of the rotor speed; the latter is determined so that the rotor speed does not reach the minimum operating limit by considering the mechanical power curve of a DFIG. The performance of the modified SIC was investigated for a 100 MW aggregated DFIG-based wind power plant under various wind conditions using an EMTP-RV simulator. The results show that the proposed SIC scheme significantly increases the frequency nadir without causing a second frequency dip.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.12
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• pp.1220-1230
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• 2006

In this paper, we propose a flew technique to the design and real-time control of an adaptive controller for robotic manipulator based on digital signal processors. The Texas Instruments DSPs(TMS320C80) chips are used in implementing real-time adaptive control algorithms to provide enhanced motion control performance for dual-arm robotic manipulators. In the proposed scheme, adaptation laws are derived from model reference adaptive control principle based on the improved Lyapunov second method. The proposed adaptive controller consists of an adaptive feed-forward and feedback controller and time-varying auxiliary controller elements. The proposed control scheme is simple in structure, fast in computation, and suitable for real-time control. Moreover, this scheme does not require any accurate dynamic modeling, nor values of manipulator parameters and payload. Performance of the proposed adaptive controller is illustrated by simulation and experimental results for a dual arm robot manipulator with eight joints. joint space and cartesian space.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.4
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• pp.29-39
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• 1997

A discrete model reference control scheme for a vehicle four wheel steering system(4WS) is proposed and evaluated for a class of discrete time nonlinar dynamics. The schmen employs a neural network to identify the plan systems, wher the neural network estimates the nonlinear dynamics of the plant. The algorithm is proven to be globally stable, with tracking errors converging to the neighborhood of zero. The merits of this scheme is that the global system stability is guaranteed. Whith thd resulting identification model which contains the neural networks, the parameters of controller are adjusted. The proposed scheme is applied to the vehicle active four wheel system and shows the validity and effectiveness through simulation. The three-degree-of freedom vehicle handling model is used to investigate vehicle handing performances. In simulation of the J-turn maneuver, the yaw rate overshoot reduction of a typical mid-size car is improved by 30% compared to a two wheel steering system(2WS) case, resulting that the proposed scheme gives faster yaw rate response andl smaller side slip angle than the 2WS case.

• 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).

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.56-59
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• 1995

In this paper, we propose a new rubust design scheme of a variable structure type model reference control (VS-MRC) which can be applied to linear time-varing plants. Our idea is started from the hypothesis that the plant consists of two parts, i.e., one has time-invariant parameters and the other has time-varying parameters. We consider the former the nominal part of the plant and the latter a kind of disturbance to the nominal one. In this design scheme, the ordinary VS-MRC is adopted to the nominal part and the signum function is introduced to eliminate the influence of the disturbance.

• Journal of Mechanical Science and Technology
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• v.18 no.3
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• pp.337-346
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• 2004

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.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.1
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• pp.68-74
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• 2008

In this paper, a novel neural fuzzy control method is proposed to control nonlinear systems. A standard PD-like fuzzy controller is designed and used as a main controller for the system. Then a neural network controller is added to the reference trajectories to form a neural-fuzzy control structure and used to compensate for nonlinear effects. Two neural-fuzzy control schemes based on two well-known neural network control schemes, the feedback error learning scheme and the reference compensation technique scheme as well as the standard PD-like fuzzy control are studied. Those schemes are tested to control the angle and the position of the inverted pendulum and their performances are compared.

• 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.