• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.6
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• pp.1-10
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• 2007

This paper presents a direct nonlinear multivariable self-tuning PID controller using neural network which adapts to the changing parameters of the nonlinear multivariable system with noises and time delays. The nonlinear multivariable system is divided linear part and nonlinear part. The linear controller are used the self-tuning PID controller that can combine the simple structure of a PID controllers with the characteristics of a self-tuning controller, which can adapt to changes in the environment. The linear controller parameters are obtained by the recursive least square. And the nonlinear controller parameters are achieved the through the Back-propagation neural network. In order to demonstrate the effectiveness of the proposed algorithm, the computer simulation results are presented to adapt the nonlinear multivariable system with noises and time delays and with changed system parameter after a constant time. The proposed PID type nonlinear multivariable self-tuning method using neural network is effective compared with the conventional direct multivariable adaptive controller using neural network.

• Advances in Computational Design
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• v.8 no.3
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• pp.237-253
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• 2023

Torque ripple content and variable switching frequency operation of conventional direct torque control (DTC) are reduced by the integration of space vector modulation (SVM) into DTC. Integration of space vector modulation to conventional direct torque control known as SVM-DTC. It had been more frequently used method in renewable energy and machine drive systems. In this paper, SVM-DTC is used to control the rotor side converter (RSC) of a wind driven doubly-fed induction generator (DFIG) because of its advantages such as reduction of torque ripples and constant switching frequency operation. However, flux and torque ripples are still dominant due to distorted current waveforms at different operations of the wind turbine. Therefore, to smoothen the torque profile a Neural Network Controller (NNC) based SVM-DTC has been proposed by replacing the PI controller in the speed control loop of the wind turbine controller. Also, stability analysis and simulation study of DFIG using process reaction curve method (RRCM) are presented. Validation of simulation study in MATLAB/SIMULINK environment of proposed wind driven DFIG system has been performed by laboratory developed prototype model. The proposed NNC based SVM-DTC yields superior torque response and ripple reduction compared to other methods.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.4
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• pp.33-41
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• 2014

This paper presents an effective memory access method for a high-speed data transfer on mobile systems using a direct memory access controller that considers the characteristics of a multi-port memory controller. The direct memory access controller has an integrated channel management function to control multiple direct memory access channels. The channels are physically separated and operate independently from each other. Experimental results show that the proposed direct memory access method improves the transfer performance by up to 72% and 69% on read and write transfer cycles, respectively. The total number of transfer cycles of the proposed method is 63% less than in a commercial method under 4-channel access.

• International Journal of Automotive Technology
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• v.5 no.2
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• pp.77-88
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• 2004

Narrow commuter vehicles can address many congestion, parking and pollution issues associated with urban transportation. In making narrow vehicles safe, comfortable and acceptable to the public, active tilt control systems are likely to playa crucial role. This paper focuses on the development of an active direct tilt control system for a narrow vehicle that utilizes an actuator in the vehicle suspension. A simple PD controller can stabilize the tilt dynamics of the vehicle to any desired tilt angle. However, the challenges in the tilt control system design arise in determining the desired lean angle in real-time and in minimizing tilt actuator torque requirements. Minimizing torque requirements requires the tilting and turning of the vehicle to be synchronized as closely as possible. This paper explores two different control design approaches to meet these challenges. A Receding Horizon Controller (RHC) is first developed so as to systematically incorporate preview on road curvature and synchronize tilting with driver initiated turning. Second, a nonlinear control system that utilizes feedback linearization is developed and found to be effective in reducing torque. A close analysis of the complex feedback linearization controller provides insight into which terms are important for reducing actuator effort. This is used to reduce controller complexity and obtain a simple nonlinear controller that provides good performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.6
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• pp.885-890
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• 2002

Turbo compressor needs high speed rotation of impeller in structure, high rated gearbox and conventional induction motor. This mechanical system increased the moment of inertia and mechanical friction loss. Resently the study of turbo compressor applied super high speed motor and drive, removing gearbox made its size small and mechanical friction loss minimum. In this study we tried to develope variable super high speed motor controller, compressor controller and MMI controller under one DSP based systems for 1500Hp, 70,000rpm direct drive Turbo compressor. It have to do unitification of each controller"s hardware and software. The result of study is applied to a 150Hp direct turbo compressor and makes it goods.oods.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.5
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• pp.76-82
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• 1999

During operating given working a robot manipulator makes some problems such as the accumulation of the error or the deviation from the command trajectory. These problems are mainly due to the disturbance noise or unmodeled system parameters. To solve these problems most of robot manipulators equip the controller. But if exact controller gains are not seleced we can't decrease the working efficiency(such as compensation about error or deviation) of the robot manipulator. So in this paper we present the controller gain tuning law by which we can find the controller gain which satisfies the per-formance specification of the robot manipulator during working of the robot. The proposed algorithm is derived from the Laypunov direct method. And by the simulation on the 4-axis SCARA type robot(SAMSUNG SM5 Robot) we guarantee the performance of this algorithm.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.3 s.174
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• pp.653-660
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• 2000

The automatization by industrial robot of today is merely rely on to the simple position repeating works, but requirements of research and development to the force control which would adapt positively to various restriction or contacting works to environment. In this paper, a learning control algorithm using, neural networks is proposed for the position and force control by a direct-drive robot. The proposed controller is the feedback controller to which the learning function of neural network is added on to and has a character of improving controller's efficiency by learning. The effectiveness of the proposed algorithm is demonstrated by the experiment on the hybrid position and force control of a parallelogram link robot with a force sensor.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.2
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• pp.107-113
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• 2004

This paper develops a adaptive fuzzy controller based fuzzy logic control for high performance of permanent magnet synchronous motor(PMSM) drives. In the proposed system, fuzzy control is used to implement the direct controller as well as the adaptation mechanism. The operation of the direct fuzzy controller and the fuzzy logic based adaptation mechanism is studied. 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 fuzzy controller is evaluated by simulation for various operating conditions. The validity of the proposed adaptive fuzzy controller is confirmed by performance results for PMSM drive system.

• Journal of Ocean Engineering and Technology
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• v.22 no.4
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• pp.14-19
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• 2008

This paper proposes a mixed $H_2/H_{\infty}$ optimal PID controller with a genetic algorithm based on the dynamic model of a brushless direct current (BLDC) motor and applies it to speed control. In the dynamic model of the BLDC motor with perturbation, the proposed controller guarantees arobust and optimal tracking performance to the desired speed of the BLDC motor. A genetic algorithm was used to obtain parameters for the PID controller that satisfy the mixed $H_2/H_{\infty}$ constraint. To implement the proposed controller, a control system based on PIC18F4431 was developed. Numerical and experimental results are shown to prove that the performance of the proposed controller was better than that of the optimal PID controller.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.5
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• pp.22-33
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• 2011

An induction motor operated with a conventional direct self controller(DSC) shows a sluggish response during startup and under changes of torque command. Fuzzy logic controller(FLC) is used in conjection with DSC to minimize these problems. A FLC chooses the switching states based on a set of fuzzy variables. Flux position, error in flux magnitude and error in torque are used as fuzzy state variables. Fuzzy rules are determinated by observing the vector diagram of flux and currents. This paper proposes hybrid fuzzy controller for direct torque control(DTC) of induction motor drives. The speed controller is based on adaptive fuzzy learning controller(AFLC), which provide high dynamics performances both in transient and steady state response. Flux position, error in flux magnitude and error in torque are used as FLC state variables. The speed is estimated with model reference adaptive system(MRAS) based on artificial neural network(ANN) trained on-line by a back-propagation algorithm. This paper is controlled speed using hybrid fuzzy controller(HFC) and estimation of speed using ANN. The performance of the proposed induction motor drive with HFC controller and ANN is verified by analysis results at various operation conditions.