• Journal of Power System Engineering
• /
• v.2 no.3
• /
• pp.74-82
• /
• 1998

This paper presents a new approach to the design of speed and azimuth control of a mobile robot with two drive wheels. The proposed control scheme uses a Gaussian function as a unit function in the neural-fuzzy network and back propagation algorithm to train the neural-fuzzy network controller in the framework of the specialized learning architecture. It is proposed to a learned controller with two neural-fuzzy networks based on an independent reasoning and a connection net with fixed weights to simplify the neural-fuzzy network. The performance of the proposed controller can be seen by the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.

• Journal of Ocean Engineering and Technology
• /
• v.11 no.1
• /
• pp.84-95
• /
• 1997

This paper presents a new approach to the design speed and azimuth control of a mobile robot with drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy-neural network, and back propagation algorithm to train the fuzzy-neural network controller in the frmework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simple the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.

• Journal of Power Electronics
• /
• v.12 no.3
• /
• pp.468-476
• /
• 2012

The interior permanent magnet synchronous motor (IPMSM) has been widely used in electric vehicle applications due to its excellent power to weigh ratio. This paper proposes the maximum torque control of an IPMSM drive using an adaptive learning (AL) fuzzy neural network (FNN) and an artificial neural network (ANN). This control method is applicable over the entire speed range while taking into consideration the limits of the inverter's rated current and voltage. This maximum torque control is an executed control through an optimal d-axis current that is calculated according to the operating conditions. This paper proposes a novel technique for the high performance speed control of an IPMSM using AL-FNN and ANN. The AL-FNN is a control algorithm that is a combination of adaptive control and a FNN. This control algorithm has a powerful numerical processing capability and a high adaptability. In addition, this paper proposes the speed control of an IPMSM using an AL-FNN, the estimation of speed using an ANN and a maximum torque control using the optimal d-axis current according to the operating conditions. The proposed control algorithm is applied to an IPMSM drive system. This paper demonstrates the validity of the proposed algorithms through result analysis based on experiments under various operating conditions.

• Journal of Electrical Engineering and Technology
• /
• v.8 no.3
• /
• pp.530-543
• /
• 2013

This paper describes a control scheme of speed sensorless fuzzy direct torque control (FDTC) of permanent magnet synchronous motor for electric vehicle (EV). Electric vehicle requires fast torque response and high efficiency of the drive. Speed sensorless FDTC In-wheel PMSM drives without mechanical speed sensors at the motor shaft have the attractions of low cost, quick response and high reliability in electric vehicle application. This paper presents a new approach to estimate the speed of in-wheel electrical vehicles based on Model Reference Adaptive System (MRAS). The direct torque control suffers in low speeds due to the effect of changes in stator resistance on the flux measurements. To improve the system performance at low speeds, a PI-fuzzy resistance estimator is proposed to eliminate the error due to changes in stator resistance. High performance sensorless drive of the in-wheel motor based on MRAS with on line stator resistance tuning is established for four motorized wheels electric vehicle and the whole system is simulated by matalb/simulink. The simulation results show the effectiveness of the new control strategy. This proposed control strategy is extensively used in electric vehicle application.

• Proceedings of the KIEE Conference
• /
• 1997.07b
• /
• pp.491-494
• /
• 1997

In this paper, we suppose a fuzzy logic controller for cruise control of vehicle. Generally, fuzzy logic controller is known as a controller which can be coped with a non-linear and a complex system. The proposed fuzzy logic controller consists of three input variables; that is, a desired speed, a current vehicle speed, and a current acceleration, and one output variable, throttle angle. The supposed fuzzy logic controller is for engine speed control system is implemented on 80586 microprocessor with DT-2801.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.8 no.4
• /
• pp.100-109
• /
• 2000

In this paper a stepping motor drive electronic ratio control system for a metal belt CVT is suggested. The electronic ratio control system developed in this study is designed to control flow rate which is required to obtain the shift speed and to control the primary actuator pressure to maintain the desired ratio. Considering these control characterstics a fuzzy logic is used for the CVT ratio control. Using the fuzzy logic dynamic models of the ratio control system is investigated and compared with the experimental results. The experimental and simulation results show that the electronic ratio control system developed in this study can be used in CVT system for the active control of the speed ratio.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.3 no.2
• /
• pp.138-147
• /
• 1998

This paper describes design and implementation results of a fuzzy logic speed controller of EV(Electric vehicle)'s induction motor for the purpose of realizing comfortable driving. The fuzzy controller is suitable for speed control of EV since that without detailed knowledge about the induction motor, it is easier to design a well-performing speed control system with good stability. PWM wave for driving the induction motor is generated by space vector modulation method and all the control algorithms are realized digitally. The results of experiment show excellence of the proposed system and that the proposed system is appropriate to control the speed of induction motor for commercial EVs.

• Journal of Biosystems Engineering
• /
• v.23 no.5
• /
• pp.457-464
• /
• 1998

A drying speed control system using fuzzy logic was developed in order to minimize the damage of rice quality for a large capacity continuous dryer. The performance of the system was tested at two object moisture content of 17% and 25% on a wet basis. For object moisture content of 17% on a wet basis, the final output moisture contents from 20.46%, 20.96%, 18.98% on a wet basis were 17.99%, 17.6% and 17.23% on a wet basis, respectively. For object moisture content of 25% on a wet basis, the final output moisture contents from 28.85%, 26.95%, 28.11%, 27.8% on a wet basis were 25.24%, 24.9%, 25.23% and 25.09% on a wet basis, respectively.

• Journal of Power Electronics
• /
• v.12 no.2
• /
• pp.294-304
• /
• 2012

This paper proposes a fuzzy speed tracking controller and a fuzzy rotor angular acceleration observer for a surface-mounted permanent magnet synchronous motor (SPMSM) based on the Takagi-Sugeno (T-S) fuzzy model. The proposed observer-based controller is robust to load torque variations since it utilizes rotor angular acceleration information instead of the load torque value. Linear matrix inequality (LMI) sufficient conditions are given to compute the gain matrices of the speed tracking controller and the observer. In addition, it is mathematically verified that the proposed observer-based control system is asymptotically stable. Simulation and experimental results are presented to confirm that the proposed control algorithm assures a better transient behavior and less sensitivity under model parameter variations than the conventional PI control method.

• Proceedings of the KIEE Conference
• /
• 2003.07b
• /
• pp.1111-1113
• /
• 2003

This paper proposes an effective self-turning algorithm based on Artificial Neural Network (ANN) for fuzzy speed control of the indirect vector controlled induction motor. Indirect vector control method divides and controls stator current by the flux and the torque producing current so that the dynamic characteristic of induction motor may be superior. However, if motor parameter changes, the flux current and the torque producing one's coupling happens and deteriorates the dynamic characteristic. The fuzzy speed controller of an induction motor has the robustness over the effect of this parameter variation than a conventional PI speed controller in some degree. This paper improves its adaptability by adding the self-tuning mechanism to the fuzzy controller. For tracking the speed command, its membership functions are adjusted using ANN adaptation mechanism. This adaptability could be embodied by moving the center positions of the membership functions. Proposed self-tuning method has wide adaptability than existent fuzzy controller or PI controller and is proved robust about parameter variation through Matlab/Simulink simulation.