• International Journal of Control, Automation, and Systems
• /
• v.3 no.4
• /
• pp.552-563
• /
• 2005

In this paper, we propose a wavelet based fuzzy neural network (WFNN) based direct adaptive control scheme for the solution of the tracking problem of mobile robots. To design a controller, we present a WFNN structure that merges the advantages of the neural network, fuzzy model and wavelet transform. The basic idea of our WFNN structure is to realize the process of fuzzy reasoning of the wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. In our control system, the control signals are directly obtained to minimize the difference between the reference track and the pose of a mobile robot via the gradient descent (GD) method. In addition, an approach that uses adaptive learning rates for training of the WFNN controller is driven via a Lyapunov stability analysis to guarantee fast convergence, that is, learning rates are adaptively determined to rapidly minimize the state errors of a mobile robot. Finally, to evaluate the performance of the proposed direct adaptive control system using the WFNN controller, we compare the control results of the WFNN controller with those of the FNN, the WNN and the WFM controllers.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.23 no.10
• /
• pp.59-68
• /
• 2009

The field oriented control of induction motors is widely used in high performance applications. However, detuning caused by parameter disturbance still limits the performance of these drives. In order to accomplish variable speed operation, conventional PI-like controllers are commonly used. These controllers provide limited good performance over a wide range of operation, even under ideal field oriented conditions. This paper is proposed high performance control of induction motor drive using multi adaptive fuzzy controller. This controller has been performed for speed control with fuzzy adaptation mechanism (FAM)-PI, current control with model reference adaptive fuzzy control(MFC) and estimation of speed using ANN. The proposed control algorithm is applied to induction motor drive system using FAM-PI, MFC and ANN controller. The performance of proposed controller is evaluated by analysis for various operating conditions using parameters of induction motor drive. Also, this paper is proposed the analysis results to verify the effectiveness of this controller.

• Journal of the Korean Institute of Telematics and Electronics T
• /
• v.36T no.4
• /
• pp.60-70
• /
• 1999

In this paper, model reference adaptive fuzzy controller (MRAFC) was proposed in order to overcome the difficulty of extracting rules and defects of the adaptation performance in the FLC. MRAFC comprised inner feedback loop consisting of the FLC and plant, and outer loop consisting of an adaptation mechanism which was designed for tuning a control rule of the FLC. A reference-model was used for design criteria of a fuzzy controller which characterizes and quantizes the control performance required in the overall control system. Tuning control rules of FLC is performed by the adaptation mechanism. The performance of proposed algorithm was verified through experiment for the DC servo motor.

• Proceedings of the KIEE Conference
• /
• 2004.11c
• /
• pp.660-662
• /
• 2004

This paper proposes an Intelligent Speed Estimator in order to realize the speed-sensorless vector control of an induction motor. Intelligent Speed Estimator used Model Reference Adaptive System which has Fuzzy-Neural adaptive mechanism as Speed Estimation method. The Intelligent Speed Estimator estimates the speed of an induction motor with a rotor flux of a reference model and adjustable model in MRAS. The Intelligent Speed Estimator reduces the error of the rotor flux between the voltage flux model and the current flux model using the error and the change of error as input of the Estimator. The computer simulation is executed to verify the propriety and the effectiveness of the proposed speed estimator.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.53 no.6
• /
• pp.389-394
• /
• 2004

This paper proposes a speed estimation method using FLC(Fuzzy Logic Controller) in order to realize the speed control of PMAM(Permanent Magnet AC Motor) with no speed sensor. This method uses FLC as a adaptive laws of MRAS(Model Reference Adaptive System) and estimates the rotor speed of PMAM with a difference between the reference model and the adjustable model. Speed control is performed by PI controller with the estimated speed. The experiment is executed to verify the propriety and the effectiveness of the proposed system.

• Proceedings of the KIEE Conference
• /
• 2009.07a
• /
• pp.780_781
• /
• 2009

This paper proposes efficiency optimization control of IPMSM drive using hybrid intelligent controller(HIC). The design of the speed controller based on fuzzy-neural network that is implemented using fuzzy control and neural network. The design of the current based on adaptive fuzzy control using model reference and the estimation of the speed based on neural network using ANN controller. In order to maximize the efficiency in such applications, this paper proposes the optimal control method of the armature current. The optimal current can be decided according to the operating speed and the load conditions. This paper proposes speed control of IPMSM using ALM-FNN, current control of model reference adaptive fuzzy control(MTC) and estimation of speed using ANN controller. The proposed control algorithm is applied to IPMSM drive system controlled HIC, the operating characteristics controlled by efficiency optimization control are examined in detail.

• 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.

• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.54 no.1
• /
• pp.8-14
• /
• 2005

This paper is proposed hybrid artificial intelligent(HAI) controller for speed control of surface permanent magnet synchronous motor(SPMSM) drive. The design of this algorithm based on HAI 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 HAI controller is evaluated by analysis for various operating conditions. The results of analysis prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.2254-2259
• /
• 2005

In this paper, we propose a wavelet based fuzzy neural network(WFNN) based direct adaptive control scheme for the solution of the tracking problem of mobile robots. To design a controller, we present a WFNN structure that merges advantages of neural network, fuzzy model and wavelet transform. The basic idea of our WFNN structure is to realize the process of fuzzy reasoning of wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. In our control system, the control signals are directly obtained to minimize the difference between the reference track and the pose of mobile robot using the gradient descent(GD) method. In addition, an approach that uses adaptive learning rates for the training of WFNN controller is driven via a Lyapunov stability analysis to guarantee the fast convergence, that is, learning rates are adaptively determined to rapidly minimize the state errors of a mobile robot. Finally, to evaluate the performance of the proposed direct adaptive control system using the WFNN controller, we compare the control performance of the WFNN controller with those of the FNN, the WNN and the WFM controllers.

• The Transactions of the Korean Institute of Electrical Engineers B
• /
• v.55 no.2
• /
• pp.98-106
• /
• 2006

This paper proposes to position and speed control of interior Permanent magnet synchronous motor(IPMSM) drive without mechanical sensor. Also, this paper develops a adaptive fuzzy controller based fuzzy logic control for high performance of PMSM drives. In the proposed system, fuzzy control is used to implement the direct controller as well as the adaptation mechanism. A Gopinath observer is used for the mechanical state estimation of the motor. The observer was developed based on nonlinear model of IPMSM, that employs a d-q rotating reference frame attached to the rotor. A Gopinath observer is implemented to compute the speed and position feedback signal. The validity of the proposed scheme is confirmed by various response characteristics.