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

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.7
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• pp.711-719
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• 2009

This paper presents a design scheme of torque control for wheeled mobile robot(WMR) to asymptotically track the target reference trajectory. By considering the kinematic model of WMR, trajectory tracking control generates the desired tracking trajectory, which is transformed into the command velocity vector for the real WMR to track the target reference trajectory. The dynamic equation of the state error between the target reference trajectory and the desired tracking trajectory is represented by Takagi-Sugeno fuzzy model, and this model is used as the reference model for the real mobile robot error dynamics to follow. The control parameters are updated by adaptive laws that are designed for the error states of the real WMR to asymptotically follow the states of reference error model for the desired tracking trajectory. The proposed control is applied to a typical wheeled mobile robot and simulation studies are carried out to verify the validity and effectiveness of the control scheme.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.2
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• pp.97-105
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• 1996

In this paper, an adaptive model predictive controller for nodinear processes using fuzzy model is proposed. Adaptive structure is implemented by recursive fuzzy modeling. The model and control law can be obtained the same as GPC, because the consequent parts of the fuzzy model comprise linear equations of input and output variables. The proposed Adaptive fuzzy model predictive controller (AFMPC) controls nonlinear process well due to the intrinsic nonlinearity of the fuzzy model. When AFMPC's output is variation in the process control input, it maintains zero steady-state offset for a constant reference input and has superior performance. The properties and performance of the proposed control scheme were examined with nonlinear plant by simulation.

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

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

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.9 no.6
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• pp.71-77
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• 1995

This paper deals with design method of learning fuzzy controller for control of an unknown nonlinear plant using the self-tuning algorithm of fuzzy inference rules. In this method the fuzzy identification model obtained that the joined identification model of nonlinear part and linear identification model of linear part by fuzzy inference systems. This fuzzy identification model ordered self-tuning by Decent method so as to be servile to nonlinear plant. A the end, designed learning fuzzy controller of fuzzy identification model have learning structure to model reference adaptive system. The simulation results show that th suggested identification and learning control schemes are practically feasible and effective.

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

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.9
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• pp.33-43
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• 2011

Since the fuzzy neural network(FNN) is universal approximators, the development of FNN control systems have also grown rapidly to deal with non-linearities and uncertainties. However, the major drawback of the existing FNNs is that their processor is limited to static problems due to their feedforward network structure. This paper proposes the recurrent FNN(RFNN) for high performance and robust control of SynRM. RFNN is applied to speed controller for SynRM drive and model reference adaptive fuzzy controller(MFC) that combine adaptive fuzzy learning controller(AFLC) and fuzzy logic control(FLC), is applied to current controller. Also, this paper proposes speed estimation algorithm using artificial neural network(ANN). The proposed method is analyzed and compared to conventional PI and FNN controller in various operating condition such as parameter variation, steady and transient states etc.

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

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