• Journal of Institute of Control, Robotics and Systems
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• v.16 no.8
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• pp.735-743
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• 2010

A new Fuzzy sliding mode controller is proposed to improve the cornering performance of the four wheel hybrid vehicles. The Fuzzy sliding mode control is applied for the control of rear motor and EHB (Electro-Hydraulic Brake) to improve the cornering performance. The modeling of the automobile is simplified that each of the two wheels is modeled as two degrees of freedom object and the friction coefficient between the wheel and the ground is assumed to be constant. The output of the Fuzzy sliding mode algorithm is the direct yaw moment for the rear wheels, which compensates for the slip angle. Through the simulations using ADAMS and MATLAB Simulink, the cornering performance of the proposed algorithm is compared to the conventional PID to show the superiority of the proposed algorithm. In the simulation experiments, the J-Turn and single lane change are used for each of the Fuzzy sliding mode algorithm and PID controller with the optimal gains which are tuned empirically.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.10
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• pp.690-696
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• 2004

This paper is proposed a hybrid intelligent controller based on the vector controlled surface permanent magnet synchronous motor(SPMSM) drive system. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of SPMSM using neural network-fuzzy(NNF) control and speed estimation using artificial neural network(ANN) Controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the theoretical analysis as well as the simulation results to verify the effectiveness of the new method.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.244-249
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• 1998

In this paper, the hybrid state space self-tuning control technique Is studied within the framework of fuzzy systems and dual-rate sampling control theory. We show that fuzzy modeling techniques can be used to formulate chaotic dynamical systems. Then, we develop the hybrid state space self-tuning fuzzy control techniques with dual-rate sampling for digital control of chaotic systems. An equivalent fast-rate discrete-time state-space model of the continuous-time system is constructed by using fuzzy inference systems. To obtain the continuous-time optimal state feedback gains, the constructed discrete-time fuzzy system is converted into a continuous-time system. The developed optimal continuous-time control law is then convened into an equivalent slow-rate digital control law using the proposed digital redesign method. The proposed technique enables us to systematically and effective]y carry out framework for modeling and control of chaotic systems. The proposed method has been successfully applied for controlling the chaotic trajectories of Chua's circuit.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.10
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• pp.59-68
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• 1992

In this paper, we proposed coordinator description and ambiguity on the hybrid controller for position/force control of robot manipulator. When the hybrid controller is desiged based on the PID control conception, the parameter sharing problem must be considered. However, selection problem of coordinate system on n-DOF robot manipulator control is unsolved. Moreover, contact force on object and change of shape make another problems. And it is very difficult to figure out the accurate mathematical model of manipulator on account of ambiguity and nonlinearity of actuator. Therfore, we design a new hybrid controller, FPID(Fuzzy PID). For verifying the validity of the controller, we tried computer simulation of this system. As a result, we can get remarkable improvement of overdamping and overshooting. Also we can solve compicance problem effectively. Furthermore, ambiguity problem is solved by adding control knowledge based compensator. So robust controller can be acheived, too.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.4
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• pp.154-157
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• 2006

This paper is proposed hybrid artificial intelligent(HAI) controller for high performance of induction motor drive. The design..of this algorithm based on fuzzy-neural network(FNN) controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. 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 FNN controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.359-362
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• 1996

Our unicycle robot has simple mechanical structure. But unicycle's dynamical system is a very sensitive unstable system. Equation of motion of this simple unicycle robot was derived using Lagrange's method. In this paper a human inference control mechanism was established throughout an inquiry into hyman riding a unicycle, and we developed a hybrid controller to control our unicycle robot. Our controller is consisted with the PD and fuzzy controller containing fuzzy gain scheduling technique. Computer simulation shows good results.

• The Transactions of the Korean Institute of Power Electronics
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• v.5 no.4
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• pp.352-357
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• 2000

The vector control scheme is usually applied to the high performance induction motor drives. The PI controller is adopted traditionally to control the motor speed and currents in the vector control scheme. In this case, the dynamic performance of the induction motor is dependent on the PI gains and the gain optimization is necessary in order to get a good dynamic performance. But, it is very hard to optimize the PI gains uniquely within the speed control range because the equivalent model of the motor control system should be known exactly. In this paper, we propose the hybrid control scheme to remove the defects of PI controller. The hybrid control scheme includes the simplified fuzzy controller which operates in the transient state and the PI controller which operates in the steady state. The proposed scheme is applied to the vector control for induction motor, and the digital simulation and the experimental results are given to verify the proposed scheme.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.5
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• pp.1-9
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• 2007

This paper is proposed an efficiency optimization control algorithm for a synchronous reluctance motor which minimizes the coner and iron losses. The design of the speed controller based on adaptive fuzzy-neural networks(AFNN) controller that is implemented using fuzzy control and neural networks. There exists a variety of combinations of d and q-axis current which provide a specific motor torque. The objective of the efficiency optimization controller is to seek a combination of d and q-axis current components, which provides minimum losses at a certain operating point in steady state. The proposed algorithm allows the electromagnetic losses in variable speed and torque drives to be reduced while keeping good torque control dynamics. The control performance of the hybrid artificial intelligent controller is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm.

• Journal of Industrial Technology
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• v.20 no.B
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• pp.155-162
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• 2000

Although Fuzzy Logic Controller(FLC) adopted three terms as input gives better performance, FLC is in general composed of two-term control because of the difficulty in the construction of fuzzy rule base. In this paper, a three-term FLC which is similar to PID control but acts as a nonlinear controller is proposed. To reduce the complexity of the rule base design and to increase efficiency. a simplified fuzzy PID control is induced from a hybrid velocity/position type PID algorithm by sharing a common rule base for both fuzzy PI and fuzzy PD parts. It is simple in structure, easy in implementation, and fast in calculation. The phase plane technique is applied to obtain the rule base for fuzzy two-term control and the resultant rule base is Macvicar-Whelan type. And the membership function is a Gaussian function. The frequency response information is used in tuning of the membership functions. Also a tuning strategy for the scaling factors is proposed based on the relationship between PID gain and the scaling factors. Simulation results show better performance and the effectiveness of the proposed method.

• International Journal of Automotive Technology
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• v.6 no.5
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• pp.529-536
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• 2005

This paper presents a novel design of a fuzzy control strategy (FCS) based on torque distribution for parallel hybrid electric vehicles (HEVs). An empirical load-regulating vehicle operation strategy is developed on the basis of analysis of the components efficiency map data and the overall energy conversion efficiency. The aim of the strategy is to optimize the fuel economy and balance the battery state-of-charge (SOC), while satisfying the vehicle performance and drivability requirements. In order to accomplish this strategy, a fuzzy inference engine with a rule-base extracted from the empirical strategy is designed, which works as the kernel of a fuzzy torque distribution controller to determine the optimal distribution of the driver torque request between the engine and the motor. Simulation results reveal that compared with the conventional strategy which uses precise threshold parameters the proposed FCS improves fuel economy as well as maintains better battery SOC within its operation range.