• Journal of Institute of Control, Robotics and Systems
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• v.19 no.3
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• pp.177-182
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• 2013

This paper proposes an input-constrained reference tracking control of a converter model. To this end, first it is shown that the bilinear converter model can be equivalently represented by a linear uncertain model belonging to a polytopic set. Then, an input-constrained tracking control scheme for the linear uncertain model is designed based on recently proposed tracking control scheme. The control scheme yields not only a stabilizing control gain but also a feasible and invariant set for the converter model. Finally, simulation results show that the state trajectory always stays in the feasible and invariant set and that the output tracks the given reference while satisfying the input constraint.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.1
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• pp.19-30
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• 2013

Recently, the constrained index tracking problem, in which the task of trading a set of stocks is performed so as to closely follow an index value under some constraints, has often been considered as an important application domain for control theory. Because this problem can be conveniently viewed and formulated as an optimal decision-making problem in a highly uncertain and stochastic environment, approaches based on stochastic optimal control methods are particularly pertinent. Since stochastic optimal control problems cannot be solved exactly except in very simple cases, approximations are required in most practical problems to obtain good suboptimal policies. In this paper, we present a procedure for finding a suboptimal solution to the constrained index tracking problem based on approximate dynamic programming. Illustrative simulation results show that this procedure works well when applied to a set of real financial market data.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.3
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• pp.143-148
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• 2001

A lot of robot trajectory tracking methods are proposed to enhance the tracking error, but irregular tracking errors are always accompanied and very hard to reduce it. Up to now, these irregular tracking errors are reduced by introducing more complicated control algorithms. But, it is intuitively obvious to reduce only the big errors selectively in the irregular ones for the better performance instead of using more complicated control algorithms. By the characteristics of the robot, big tracking errors of the end-effector are generated mostly due to the fast moving of joint. So, in this paper, we introduce a new method which reduce the big tracking errors by clippings the joint velocity with the constraint of given path. Using this method, desired trajectory tracking is obtained within the far reduced error bound. Also, this method is successfully applied to generate the path-constrained error reducing trajectories for 2-axis SCARA type robot.

• Journal of the Korean Society for Precision Engineering
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• v.27 no.12
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• pp.48-58
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• 2010

In this study, position tracking and force reflection control of a master-slave manipulator which will be used for handling objects contaminated by radioactivity has been addressed. Since available measurements concerning on dynamic motion of the master-slave manipulator are restricted, a simple constrained control structure was suggested. In the consideration of the uncertain dynamic behaviors of the slave manipulator which is dependent upon mass and shape of work pieces grasped and dynamic properties of the environment contacted, a simple structured sliding mode control was suggested to guarantee robustness with respect to parameter uncertainties and external disturbances. The proposed control was applied to a 1-DOF master-slave link system. The control performances were verified along with some computer simulation results.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.460-467
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• 2018

Metaheuristic optimization approach has become the new framework for control synthesis. The main purposes of the control design are command (input) tracking and load (disturbance) regulating. This article proposes an optimal proportional-integral-derivative (PID) controller design for the DC motor speed control system with tracking and regulating constrained optimization by using the cuckoo search (CS), one of the most efficient population-based metaheuristic optimization techniques. The sum-squared error between the referent input and the controlled output is set as the objective function to be minimized. The rise time, the maximum overshoot, settling time and steady-state error are set as inequality constraints for tracking purpose, while the regulating time and the maximum overshoot of load regulation are set as inequality constraints for regulating purpose. Results obtained by the CS will be compared with those obtained by the conventional design method named Ziegler-Nichols (Z-N) tuning rules. From simulation results, it was found that the Z-N provides an impractical PID controller with very high gains, whereas the CS gives an optimal PID controller for DC motor speed control system satisfying the preset tracking and regulating constraints. In addition, the simulation results are confirmed by the experimental ones from the DC motor speed control system developed by analog technology.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.4
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• pp.221-227
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• 2000

This paper combines the constrained model predictive control with the feedback linearization to solve a nonlinear system control problem with input constraints. The combined approach consists of two steps: Firstly, the nonlinear model is linearized by the feedback linearization. Secondly, based on the linearized model, the constrained model predictive controller is designed taking input constraints into consideration. The proposed controller is applied to two link robot system, and tracking performances of the controller are investigated via some simulations, where the comparisons are done for the cases of unconstrained, constrained input in feedback linearization.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.3
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• pp.159-163
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• 2001

An input constrained receding horizon predictive control algorithm for uncertain systems with nonzero set points is proposed. for constant nonzero set points, models with uncertainty can be converted into an augmented incremental system through the use of integrators and the problem is transformed into a zero-state regulation problem for the incremental system. But the original constraints on inputs are converted into constraints on the sum of control inputs at each time instants, which have not been dealt in earlier constrained robust receding horizon control problems. Recursive state bounding technique and worst case minimizing strategy developed in earlier works are applied to the augmented incremental system to yield an offset error free controller. The resulting algorithm is formulated so that it can be solved using LP.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.160-163
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• 1996

This paper deals with MCMBPC(Multivariable Constrained Model Based Predictive Controller) for nonlinear boiler system with noise and disturbance. MCMBPC is designed by linear state space model obtained from some operating point of nonlinear boiler system and Kalman filter is used to estimate the state with noise and disturbance. The solution of optimization of the cost function constrained on input and/or output variables is achieved using quadratic programming, viz. singular value decomposition (SVD). The controller designed is shown to have excellent tracking performance via simulation applied to nonlinear dynamic drum boiler turbine model for 16OMW unit.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.346-349
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• 1994

This paper is concerned with a motion control of a manipulator under parametric uncertainties and external disturbances. The parametric uncertainties are regarded as internally generated disturbances in the manipulator. Based on this idea, we formulate a model reference control problem with desired disturbance attenuation. The solution of this control problem not only reduces the worst-case effect on tracking error due to internal and external disturbances (combined disturbances) as much as possible, but also achieve optimal tracking when perturbations are absent. In order to solve the control problem which is formulated in this paper we reduce it to a constrained minmax cost control problem. A differential game theory is used to treat this constrained minmax cost control problem. The differential game theory leads to a sufficient condition for the global solvability of the model reference control problem with desired disturbance attenuation.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.6
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• pp.397-402
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• 2016

This paper proposes a robust tracking control for constrained uncertain linear systems by combining a disturbance observer (DOB) and linear matrix inequality (LMI) based state feedback control. To this end, the state feedback control is designed for the nominal system and then a DOB based feed-forward control is added to reject uncertainties. In doing so, the DOB and state feedback controller are joined in a way that the combined control satisfies the input constraints and closed loop stability is guaranteed. Simulation results are provided to show that the proposed control scheme successfully stabilizes uncertain systems.