• 드라이브 ㆍ 컨트롤
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• 제16권2호
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• pp.22-29
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• 2019

This study presented a model predictive tracking control algorithm of autonomous truck based on object state estimation using extended Kalman filter. To design the model, the 1-layer laser scanner was used to estimate position and velocity of the object using extended Kalman filter. Based on these estimations, the desired linear path for object tracking was computed. The lateral and yaw angle errors were computed using the computed linear path and relative positions of the truck. The computed errors were used in the model predictive control algorithm to compute the optimal steering angle for object tracking. The performance evaluation was conducted on Matlab/Simulink environments using planar truck model and actual point data obtained from laser scanner. The evaluation results showed that the tracking control algorithm developed in this study can track the object reasonably based on the model predictive control algorithm based on the estimated states.

• Journal of Power Electronics
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• 제16권1호
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• pp.277-286
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• 2016

This paper offers two Maximum Power Point Tracking (MPPT) systems for Photovoltaic (PV) applications. The first MPPT method is based on a fixed frequency Model Predictive Control (MPC). The second MPPT technique is based on the Predictive Hysteresis Control (PHC). An experimental demonstration shows that the proposed techniques are fast, accurate and robust in tracking the maximum power under different environmental conditions. A DC/DC converter with a high voltage gain is obligatory to track PV applications at the maximum power and to boost a low voltage to a higher voltage level. For this purpose, a high gain Switched Inductor Quadratic Boost Converter (SIQBC) for PV applications is presented in this paper. The proposed converter has a higher gain than the other transformerless topologies in the literature. It is shown that at a high gain the proposed SIQBC has moderate efficiency.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.292-295
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• 2003

In this paper, we present static output feedback model predictive tracking control for linear system with uncertainty. The proposed control law is based on integral action form to provide zero o��set for constant command signals and the closed loop stability is guaranteed under linear matrix inequality conditions on the terminal weighting matrix using the decreasing monotonicity property of the performance index. Through simulation examples, we illustrate that the proposed schemes can be appropriate tracking controllers for uncertain system.

• 제어로봇시스템학회논문지
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• 제21권3호
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• pp.237-242
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• 2015

This paper presents a method to enhance tracking performance of an input-constrained bilinear system using MPC (Model Predictive Control) when a feasible tracking control is known. Since the error dynamics induced by the known tracking control is asymptotically stable, there exists a Lyapunov function for the stable error dynamics. By defining a cost function including the Lyapunov function and describing tracking performance, an MPC law is derived. In simulation, the performance of the proposed MPC law is demonstrated by applying it to a converter model.

• 한국기계기술학회지
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• 제20권6호
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• pp.809-817
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• 2018

The model predictive controller performance of the mobile robot is set to an arbitrary value because it is difficult to select an accurate value with respect to the controller parameter. The general model predictive control uses a quadratic cost function to minimize the difference between the reference tracking error and the predicted trajectory error of the actual robot. In this study, we construct a predictive controller by transforming it into a quadratic programming problem considering velocity and acceleration constraints. The control parameters of the predictive controller, which determines the control performance of the mobile robot, are used a simple weighting matrix Q, R without the reference model matrix $A_r$ by applying a quadratic cost function from which the reference tracking error vector is removed. Therefore, we designed the predictive controller 1 and 2 of the mobile robot considering the constraints, and optimized the controller parameters of the predictive controller using a genetic algorithm with excellent optimization capability.

• 로봇학회논문지
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• 제15권3호
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• pp.277-285
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• 2020

A differential drive wheeled robot is a kind of mobile robot suitable for indoor navigation. Model predictive control is an optimal control technique with various advantages and can achieve excellent performance. One of the main advantages of model predictive control is that it can easily handle constraints. Therefore, it deals with realistic constraints of the mobile robot and achieves admirable performance for trajectory tracking. In addition, the intention of the robot can be properly realized by adjusting the weight of the cost function component. This control technique is applied to the local planner of the navigation component so that the mobile robot can operate in real environment. Using the Robot Operating System (ROS), which has transcendent advantages in robot development, we have ensured that the algorithm works in the simulation and real experiment.

• International Journal of Safety
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• 제5권2호
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• pp.6-11
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• 2006

The time delay in servo control on LonWorks/IP Virtual Device Network (VDN) is highly stochastic in nature. LonWorks/IP VDN induced time delay deteriorates the performance and stability of the real-time distributed control system and hinders an effective preventive and predictive maintenance. Especially in real-time distributed servo applications on the factory floor, timely response is essential for predictive and preventive maintenance. In order to guarantee the stability and performance of the system for effective preventive and predictive maintenance, LonWorks/IP VDN induced time delay needs to be predicted and compensated for. In this paper position control simulation of DC servo motor using Zero Phase Error Tracking Controller (ZPETC) as a feedforward controller, and Internal Model Controllers (IMC) based on Smith predictor with disturbance observer as a feedback controller is performed. The validity of the proposed control scheme is demonstrated by comparing the IMC based on Smith predictor with disturbance observer.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.263-264
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• 2007

This paper presents a model predictive controller for tracking control of the wheeled mobile robots (WMRs) subject to nonholonomic constraint. The input-output feedback-linearization method and the mode transformation are used. The performance of the proposed control algorithm is verified via computer simulation. It is shown that the control strategy is feasible.

• 로봇학회논문지
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• 제19권1호
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• pp.8-15
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• 2024

This paper proposes a linear model predictive control of 6-DOF remotely operated underwater vehicles using nonlinear robust internal-loop compensator (NRIC). First, we design a integrator embedded linear model prediction controller for a linear nominal model, and then let the real model follow the values calculated through forward dynamics. This work is carried out through an NRIC and in this process, modeling errors and external disturbance are compensated. This concept is similar to disturbance observer-based control, but it has the difference that H_∞ optimality is guaranteed. Finally, tracking results at trajectory containing the velocity discontinuity point and the position tracking performance in the disturbance environment is confirmed through the comparative study with a traditional inverse dynamics PD controller.

• 한국정보통신학회논문지
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• 제21권10호
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• pp.1879-1885
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• 2017

본 논문에서는 시간에 따라 방향 속도와 위치가 변하는 무인자동차의 궤적 추적 제어시스템에 대해 논한다. 무인자동차는 운전자의 도움이 없어도 스스로 주위환경을 인식하여 지정된 도로를 주행할 수 있는 자동차로 올바른 주행을 위해 고려해야 할 변수가 다양하다. 무인자동차의 궤적 추적 시스템에서 인식한 정보는 이산적인 값을 가지므로 센스 간의 간격으로 인하여 비연속성 및 비선형성을 가지고 있다. 이로 인하여 목표 궤적을 정확하게 추적하는 것 어렵다. 본 논문은 차량의 운동학 모델링을 통하여 선형오차, 제약 조건, 제어 목표함수의 세 가지 조건을 갖는 무인자동차 궤적 추적시스템을 제안한다. 제안된 궤적 추적시스템을 기반으로 동적 시뮬레이션 소프트웨어-카심(Dynamic Simulation Software-CarSim)의 결합시뮬레이션을 통해 시스템의 성능을 평가하였고, 그 결과로 더욱 정밀하게 목표 궤적을 추적할 수 있음을 확인하였다.