• 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.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.6
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• pp.71-77
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• 2015

In this paper, we propose a scheduling method for signal measurement and control algorithm execution in a multi-motor drive controller. The multi-motor controller which is used for vehicle control receives position/velocity command and performs position/velocity control and current control. Internal resource allocation and control algorithm execution timing are very important when one microcontroller is used for multi-motor drives. The control performance of the velocity control system is verified by varying ADC(Analog to Digital Converter) conversion timing and algorithm execution timing using real experiments.

• Journal of Korean Association for Spatial Structures
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• v.21 no.2
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• pp.41-48
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• 2021

A smart tuned mass damper (TMD) is widely studied for seismic response reduction of various structures. Control algorithm is the most important factor for control performance of a smart TMD. This study used a Deep Deterministic Policy Gradient (DDPG) among reinforcement learning techniques to develop a control algorithm for a smart TMD. A magnetorheological (MR) damper was used to make the smart TMD. A single mass model with the smart TMD was employed to make a reinforcement learning environment. Time history analysis simulations of the example structure subject to artificial seismic load were performed in the reinforcement learning process. Critic of policy network and actor of value network for DDPG agent were constructed. The action of DDPG agent was selected as the command voltage sent to the MR damper. Reward for the DDPG action was calculated by using displacement and velocity responses of the main mass. Groundhook control algorithm was used as a comparative control algorithm. After 10,000 episode training of the DDPG agent model with proper hyper-parameters, the semi-active control algorithm for control of seismic responses of the example structure with the smart TMD was developed. The simulation results presented that the developed DDPG model can provide effective control algorithms for smart TMD for reduction of seismic responses.

• Journal of Korean Association for Spatial Structures
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• v.21 no.1
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• pp.79-86
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• 2021

Control performance of a smart tuned mass damper (TMD) mainly depends on control algorithms. A lot of control strategies have been proposed for semi-active control devices. Recently, machine learning begins to be applied to development of vibration control algorithm. In this study, a reinforcement learning among machine learning techniques was employed to develop a semi-active control algorithm for a smart TMD. The smart TMD was composed of magnetorheological damper in this study. For this purpose, an 11-story building structure with a smart TMD was selected to construct a reinforcement learning environment. A time history analysis of the example structure subject to earthquake excitation was conducted in the reinforcement learning procedure. Deep Q-network (DQN) among various reinforcement learning algorithms was used to make a learning agent. The command voltage sent to the MR damper is determined by the action produced by the DQN. Parametric studies on hyper-parameters of DQN were performed by numerical simulations. After appropriate training iteration of the DQN model with proper hyper-parameters, the DQN model for control of seismic responses of the example structure with smart TMD was developed. The developed DQN model can effectively control smart TMD to reduce seismic responses of the example structure.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.4
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• pp.583-588
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• 2013

In this paper we propose implement technique of vector current control in order to verify performance of an AC servo driver that is able to easy control of motion with multi-axis in the robot. In doing do, we have developed the AC servo driver to driving PMSM, and then we confirm that this driver whether operating or not normally by controlling of vector current. The vector current control was performed at the no load condition in PMSM. Then we compare command control and tracking control. As a result of verification, we recognize we get a satisfactory result.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.35 no.6
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• pp.526-532
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• 2007

This paper considers a fault tolerant control problem for a spacecraft using reaction wheels. Faults are assumed to be inherent to only actuators(reaction wheels) and a control algorithm to accommodate actuators' faults is proposed. An attitude control loop includes an angular velocity control loop. The time delay control method is used to make a spacecraft follow the command angular velocity and to accommodate actuators' faults. A stability condition for the proposed algorithm is derived and the performance is demonstrated by computer simulations.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2652-2654
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• 2005

A vacuum chamber temperature control system of Pohang Accelerator Laboratory (PAL) storage ring is a subsystem upgraded PAL control system, which is based upon Experimental Physics and Industrial Control System (EPICS) [1]. There are two control components, data acquisition system (SA120 data logger), development control system IOC (Input/Output Controller) at the storage ring of PAL. There are 240 vacuum chamber at the storage ring. It was a very important problem to solve how to monitor such a large number of vacuum chamber temperature distributed around the ring. The IOC connect MODBUS/JBUS field network to asynchronous serial ports for communication with serial device. It can simultaneously control up to 4 data acquisition systems. Upon receiving a command from a IOC running under Windows2k through the network, the IOC communicate through the slave serial interface ports to SA120. We added some software components on the top of EPICS toolkit. The design of the vacuum control system is discussed. This paper describes the development vacuum chamber temperature control system and how the design of this system.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.1
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• pp.52-57
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• 2007

This paper presents the design of intelligent moving machine control system using a real time speech recognition. The proposed moving machine control system is composed of four separated module, which are main control module, speech recognition module, servo motor driving module and sensor module. In main control module with microprocessor(80C196KC), one part of the artificial intelligences, fuzzy logic, was applied to the proposed intelligent control system. In order to improve the non-linear characteristic which depend on an user's weight and variable environment, encoder attached to the servo motors was used for feedback control. The proposed system is tested using 9 words lot control of the mobile robot, and the performance of a mobile robot using voice and joystick command is also evaluated.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.51 no.2
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• pp.68-76
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• 2002

This paper presents an implementation of high-dynamic performance control system of Reluctance Synchronous Motor (RSM) drives for an industrial servo system with direct torque control (DTC). The problems of DTC for high-dynamic performance and maximum efficiency RSM drives are the nonlinear variable flux and inductance due to a saturated stator linkage flux and nonlinear inductance curve with various load currents. The accurate estimation of the stator flux and torque are obtained using stator flux observer of which a saturated inductance Ld and Lq can be compensated by using the adapted neural network from measuring the modulus and angle of the stator current. To obtain fast torque response and maximum torque/current with varying load current, the reference command flux is ensured by imposing Ids=Iqs. This control strategy is proposed to fast response and optimal efficiency for RSM drive. In order to prove rightness of the suggested control algorithm, we have some actual experimental system using 6000 pulse/rev encoder at ${\pm}10$ and ${\pm}1500rpm$. The developed digitally high-performance control system are shown some good response characteristics of control results and high performance features using 1.0kW RSM of which has 2.57 Ld/Lq salient ratio.

• Proceedings of the KIPE Conference
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• 2010.07a
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• pp.585-586
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• 2010

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 conjunction 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 FLC for direct torque control(DTC) of induction motor drives. This controller is controlled speed using hybrid FLC. The performance of the proposed induction motor drive with hybrid FLC is verified by analysis results at various operation conditions.