• 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.

• Proceedings of the KIEE Conference
• /
• 2009.04a
• /
• pp.74-76
• /
• 2009

In this paper, search method and sliding mode observer are developed for efficiency optimization of induction motor. The proposed control scheme consists of efficiency controller and adaptive backstepping controller. A search controller for which information of input of fuzzy controller is included in efficiency controller that uses a direct vector controlled induction motor. The search controller is based on the "Rosenbrock" method and finds the flux level at the minimum input power of induction motor. Once this optimal flux level has been determined, this information is utilized to update the rule base of a fuzzy controller A sliding mode observer is designed to estimate rotor flux and an adaptive backstepping controller is also used to compensate for mechanical uncertainties in the speed control of induction motor. Simulation results are presented to validate the proposed controller.

• Proceedings of the KIEE Conference
• /
• 1993.07a
• /
• pp.227-230
• /
• 1993

Recently, the development of novel control methodology enables us to improve the performance of AC-machine drives by using pulse width modulation (PWM) technique. Usually, the dynamic characteristic of induction motor (IM) has been represented by the 5-th order nonlinear differential equation. This dynamics, however, can be reduced to 3-rd order dynamics by applying direct control of IM input current. This methodology concludes that it is much easier to control IM by means of the field-oriented methods employing the current controller. Therefore a precise current control is crucial to achieve a high control performance both in dynamic and steady state operations. This paper presents an adaptive fuzzy current controller with artificial neural network (ANN) for field-oriented controlled IM. This new control structure is able to adaptively minimize a current ripple while maintaining constant switching frequency. Especially the proposed controller employs neuro-computing philosophy as well as adaptive learning pattern recognizing principles with respect to variations of the system parameters. The proposed approach is applied to the IM drive system, and its performance is tested through various simulations. Simulation results show that the proposed system, compared among several known classical methods, has a superb performance.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.1 no.1
• /
• pp.29-34
• /
• 2001

This paper is devoted to investigating direct adaptive neural control of nonlinear systems with uncertain or unknown dynamic models. In the direct adaptive neural networks control area, theoretical issues of the existing backpropagation-based adaptive neural networks control schemes. The major contribution is proposing the variable index control approach, which is of great significance in the control field, and applying it to derive new stable robust adaptive neural network control schemes. This new schemes possess inherent robustness to system model uncertainty, which is not required to satisfy any matching condition. To demonstrate the feasibility of the proposed leaning algorithms and direct adaptive neural networks control schemes, intensive computer simulations were conducted based on the flexible joint robot systems and functions.

• Journal of Power Electronics
• /
• v.16 no.2
• /
• pp.564-571
• /
• 2016

This study proposes an adaptive neuro-fuzzy inference system (ANFIS)-based rotor position controller for brushless direct current (BLDC) motors to improve the control performance of the drive under transient and steady-state conditions. The dynamic response of a BLDC motor to the proposed ANFIS controller is considered as standard reference input. The effectiveness of the proposed controller is compared with that of the proportional integral derivative (PID) controller and fuzzy PID controller. The proposed controller solves the problem of nonlinearities and uncertainties caused by the reference input changes of BLDC motors and guarantees a fast and accurate dynamic response with an outstanding steady-state performance. Furthermore, the ANFIS controller provides low torque ripples and high starting torque. The detailed study includes a MATLAB-based simulation and an experimental prototype to illustrate the feasibility of the proposed topology.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.58 no.9
• /
• pp.1821-1826
• /
• 2009

An adaptive neural controller for perturbed strict-feedback nonlinear system is proposed. All the previous adaptive neural (or fuzzy) controllers are based on the backstepping scheme where the universal approximators are employed in every design steps. These schemes involve virtual controls and their time derivatives that make the stability analysis and implementation of the controller very complex. This fact is called 'explosion of complexty ' since the complexity grows exponentially as the system dynamic order increases. The proposed adaptive neural control scheme adopt the backstepping design procedure only for determining ideal control law and employ only one neural network to approximate the finally selected ideal controller, which makes the controller design procedure and stability analysis considerably simple compared to the previously proposed controllers. It is shown that all the time-varing signals containing tracking error are stable in the Lyapunov viewpoint.

• Journal of Power Electronics
• /
• v.15 no.3
• /
• pp.730-740
• /
• 2015

This paper presents model reference adaptive system speed estimators based on Type-1 and Type-2 fuzzy logic controllers for the speed sensorless direct torque and flux control of an induction motor drive (IMD) using space vector pulse width modulation. A Type-1 fuzzy logic controller (T1FLC) based adaptation mechanism scheme is initially presented to achieve high performance sensorless drive in both transient as well as in steady-state conditions. However, the Type-1 fuzzy sets are certain and cannot work effectively when a higher degree of uncertainties occurs in the system, which can be caused by sudden changes in speed or different load disturbances and, process noise. Therefore, a new Type-2 FLC (T2FLC) - based adaptation mechanism scheme is proposed to better handle the higher degree of uncertainties, improve the performance, and is also robust to different load torque and sudden changes in speed conditions. The detailed performance of different adaptation mechanism schemes are performed in a MATLAB/Simulink environment with a speed sensor and sensorless modes of operation when an IMD is operates under different operating conditions, such as no-load, load, and sudden changes in speed. To validate the different control approaches, the system is also implemented on a real-time system, and adequate results are reported for its validation.

• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.51 no.3
• /
• pp.111-119
• /
• 2002

This paper proposes a speed control system based on a fuzzy logic approach, integrated with a simple and effective adaptive algorithms. And this paper attempts to provide a thorough comparative insight into the behavior of induction motor drive with PI, direct and improved fuzzy speed controller. A indirect vector controlled induction motor is simulated under varying operating condition. The validity of the comparative results is confirmed by simulation results for induction motor drive system.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.33B no.5
• /
• pp.25-32
• /
• 1996

This paper will propose the direct adaptive learning control scheme based on adaptive control technique and intelligent control theory for a nonlinear system. Using the proposed learning control scheme, we will apply to on-line control an uncertain but for model perfect matching, it's structure condition is known. The effectiveness of the proposed control schem will be illustrated by simulations of a robot manipulator.

• Smart Structures and Systems
• /
• v.31 no.1
• /
• pp.69-78
• /
• 2023

The purpose of this paper is to develop a scalable grey predictive controller with unavoidable random delays. Grey prediction is proposed to solve problems caused by incorrect parameter selection and to eliminate the effects of dynamic coupling between degrees of freedom (DOFs) in nonlinear systems. To address the stability problem, this study develops an improved gray-predictive adaptive fuzzy controller, which can not only solve the implementation problem by determining the stability of the system, but also apply the Linear Matrix Inequality (LMI) law to calculate Fuzzy change parameters. Fuzzy logic controllers manipulate robotic systems to improve their control performance. The stability is proved using Lyapunov stability theorem. In this article, the authors compare different controllers and the proposed predictive controller can significantly reduce the vibration of offshore platforms while keeping the required control force within an ideal small range. This paper presents a robust fuzzy control design that uses a model-based approach to overcome the effects of modeling errors. To guarantee the asymptotic stability of large nonlinear systems with multiple lags, the stability criterion is derived from the direct Lyapunov method. Based on this criterion and a distributed control system, a set of model-based fuzzy controllers is synthesized to stabilize large-scale nonlinear systems with multiple delays.