• Journal of Institute of Control, Robotics and Systems
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• v.18 no.10
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• pp.902-911
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• 2012

This paper proposes a robust adaptive fuzzy backstepping control scheme for trajectory tracking of an electrically driven nonholonomic mobile robot with uncertainties and actuator dynamics. A complete model of an electrically driven nonholonomic mobile robot described in this work includes all models of the uncertain robot kinematics with a nonholonomic constraint, the uncertain robot body dynamics with uncertain frictions and unmodeled disturbances, and the uncertain actuator dynamics with disturbances. The proposed control scheme uses the backstepping control approach through a kinematic controller and a robust adaptive fuzzy velocity tracking controller. The presented control scheme has a voltage control input with an auxiliary current control input rather than a torque control input. It has two FBFNs(Fuzzy Basis Function Networks) to approximate two unknown nonlinear robot dynamic functions and a robust adaptive control input with the proposed adaptive laws to overcome the uncertainties such as parameter uncertainties and external disturbances. The proposed control scheme does not a priori require the accurate knowledge of all parameters in the robot kinematics, robot dynamics and actuator dynamics. It can also alleviate the chattering of the control input. Using the Lyapunov stability theory, the stability of the closed-loop robot control system is guaranteed. Simulation results show the validity and robustness of the proposed control scheme.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.19 no.1
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• pp.121-127
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• 2010

We consider the robust trajectory tracking control problem for a skid steering mobile robots. A dynamic model is derived accounting for the effects of wheel skidding. The control design utilizes the dynamic feedback linearization techniques, so as to obtain a predictable behavior for the instantaneous center of rotation thus preventing excessive skidding. The additive controller using the sliding mode type is then robustified against the unmodelled dynamics and parameter uncertainty. Simulation results show the good performances under excessively uncorrected estimations of the longitudinal forces and the lateral resistive forces caused by the skidding of the wheels in tracking trajectories.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.4
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• pp.67-75
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• 1993

The Fourier series are employed to approximate the input/output(I/O) characteristics of a dynamic system and, based on the approximation, a new learing control algorithm is proposed in order to find iteratively the control input for tracking a desired trajectory. The use of the Fourier approximation of I/O renders at least a couple of useful consequences: the frequency characteristics of the system can be used in the controller design and the reconstruction of the system states is not required. The convergence condition of the proposed algorithm is provided and the existence and uniqueness of the desired control input is discussed. The effectiveness of the proposed algorithm is illustrated by computer simulation for a robot trajectory tracking. It is shown that, by adding feedback term in learning control algorithm, robustness and convergence speed can be improved.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.386-388
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• 1996

This paper presents the learning controller for robot manipulators to track the desired trajectory exactly. The learning controller, based on the Lyapunov theory, consists of a fixed PD action and a repetitive action for the purpose of feedforward compensation which is adjusted utilizing a linear combination of the velocity and position errors. The learning controller Is often used In case of the desired trajectories are periodic tasks, and has advantage that it periodically converges to zero even if we don't know the exact dynamic parameters. In this paper, we show that the position and velocity errors of robot manipulators converge to zero sa time goes infinite for the input is periodic function and show a good trajectory tracking performance In the cartesian space.

• Proceedings of the KIEE Conference
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• 1994.11a
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• pp.305-307
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• 1994

In this paper, the desist and the implementation of a robust adaptive controller for trajectory tracking control of the robot manipulator is presented. The proposed control scheme ensures that without any prior knowledge of the robot manipulator parameters, tracking errors are converged to some boundary in the presence of a state-dependent input disturbances as well as the ideal case. The 3 DOF robot manipulator including actuator dynamics is used for the implementation of the proposed control scheme. The experimental results show that the proposed control scheme is valid for trajectory tracking control of the robot manipulator.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.4
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• pp.602-610
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• 2018

The model predictive control is an effective method to optimize the current control input that predicts the current control state and the future error using the predictive model of the control system when the reference trajectory is known. Since the control input can not have a physically infinitely large value, a predictive controller design with constraints should be considered. In addition, the reference model $A_r$ and the weight matrices Q, R that determine the control performance of the predictive controller are not optimized as arbitrarily designated should be considered in the controller design. In this study, we construct a predictive controller of a mobile robot by transforming it into a quadratic programming problem with constraints, The control performance of the mobile robot can be improved by optimizing the control parameters of the predictive controller that determines the control performance of the mobile robot using genetic algorithm. Through the computer simulation, the superiority of the proposed method is confirmed by comparing with the existing method.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.7
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• pp.599-604
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• 2013

Path planning of mobile robots has a purpose to design an optimal path from an initial position to a target point. Minimum driving time, minimum driving distance and minimum driving error might be considered in choosing the optimal path and are correlated to each other. In this paper, an efficient driving trajectory is planned in a real situation where a mobile robot follows a moving object. Position and distance of the moving object are obtained using a web camera, and the rotation angular and linear velocities are estimated using Kalman filters to predict the trajectory of the moving object. Finally, the mobile robot follows the moving object using a single curvature trajectory by estimating the trajectory of the moving object. Using the estimation by Kalman filters and the single curvature in the trajectory planning, the total tracking distance and time saved amounts to about 7%. The effectiveness of the proposed algorithm has been verified through real tracking experiments.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.6
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• pp.99-108
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• 1998

In this paper, we proposed a multi-layer neural network for direct control method of nonlinear system. The proposed control method uses neural network as the controller to learn inverse model of plant. The neural network used consists of two parts; one part is for identification of linear part, and the other is for identification of nonlinear part of inverse system. The neural network has to be learned the liner part with RLS algorithm and the nonlinear part with error of plant. From the simulation and experiment of tracking control to use one link manipulator as plant, we proved usefulness of the proposed control method to comparing to conventional direct neural network control method. By comparing the two methods, from simulation and experiment, we were convinced that the proposed control method is more simple and accuracy than the conventional method. Moreover, number of weight and bias to be controller parameter are small, and it has smaller steady state error than conventional method.

• Proceedings of the KIEE Conference
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• 2006.07a
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• pp.515-516
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• 2006

본 논문은 풍속이 변화하는 상황에서 풍력 발전 시스템의 동작을 실시간으로 모의할 수 있는 시뮬레이터의 구성과 실험 결과를 제시한다. 본 논문에서 제시하고자하는 풍력 발전 시스템 시뮬레이터는 실시간 시뮬레이터인 RTDS(Real Time Digital Simulator)와 11kW의 AC 서보 시스템을 포함하는 전동기-발전기 실험 장치(MG set, Motor-Generator Set)로 구현되었다. RTDS는 풍속 모델과 블레이드, 터빈, 발전기를 포함하는 풍력발전 시스템 모델을 실시간으로 모의하며 MG-set은 실시간으로 모의된 풍력 발전 시스템의 물리적 상태를 구현한다. 풍력 발전 시스템 모델의 동작점 궤적과 최대출력점추종(Maximum Power Point Tracking)제어에 의한 풍력 발전 시스템 시뮬레이터의 운전 결과의 제시를 통해 시뮬레이터의 유용성을 검증하였다. 본 논문에서 제시된 시뮬레이터는 상용 실시간 시뮬레이터를 이용하여 간단한 소프트웨어의 수정을 통해 다양한 모델의 풍력 반전 시스템을 모의할 수 있으며 다양한 실험 조건에서 과도상태 및 정상상태 특성 실험이 가능하므로 풍력 발전 시스템의 발전기 특성 평가, 전력 변환 장치의 성능 시험 등에 활용이 가능하다.

• The Journal of Korea Robotics Society
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• v.15 no.3
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• pp.221-232
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• 2020

In a four-wheel independent drive platform, four wheels and motors are connected directly, and the rotation of the motors generates the power of the platform. It uses a skid steering system that steers based on the difference in rotational power between wheel motors. The platform can control the speed of each wheel individually and has excellent mobility on dirt roads. However, the difficulty of the straight-running is caused due to torque distribution variation in each wheel's motor, and the direction of rotation of the wheel, and moving direction of the platform, and the difference of the platform's target direction. This paper describes an algorithm to detect the slip generated on each wheel when a four-wheel independent drive platform is traveling in a harsh environment. When the slip is detected, a compensation control algorithm is activated to compensate the torque of the motor mounted on the platform to improve the trajectory tracking performance of the platform. The four-wheel independent drive platform developed for this study verified the algorithm. The wheel slip detection and the compensation control algorithm of the platform are expected to improve the stability of trajectory tracking.