• Journal of Institute of Control, Robotics and Systems
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• v.20 no.3
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• pp.235-244
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• 2014

This article introduces recent trends in RHC (Receding Horizon Control), also known as MPC (Model Predictive Control), that has been well recognized in industry and academy as a systematic approach for optimal design and constraint management. Constrained and robust RHCs will be briefly reviewed with milestone results. Among the diverse developments and achievements of RHCs, implementation issues will be focused on, together with the latest applications. In particular, this article introduces results on how to solve a finite horizon open-loop optimal control problem in an efficient way, together with code generation for real-time execution and easy implementation. Instead of traditional applications such as refineries and petrochemical plants, this article highlights some selected emerging applications, such as energy management systems and mechatronics, that have resulted from state-of-the-art high performance computing power and advanced numerical schemes.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.429-432
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• 2004

In this paper, we derive the guaranteed bound of the horizon size for the stabilizing receding horizon control(RHC). From the convergence property of the solution to the Riccati equation, it is shown that the lower bound can be represented in terms of the parameters in the given system model, which makes an off-line calculation possible. Additionally, it is shown to be able to obtain the stabilizing RHC without respect to the final weighting matrix. The proposed guaranteed bound is obtained numerically via simulation.

• Proceedings of the IEEK Conference
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• 2001.06a
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• pp.133-136
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• 2001

In this work, the problem of regulating and tracking available bit rate (ABR) traffic in ATM network. The issue of providing control signals to throttled sources at distant location from bottlenecked node is of particular interest. Network modeling and design of controller is outlined. To obtain optimal control, receding horizon control (RHC) theory is applied. Simulation results are presented in views of regulation and tracking problems with or without constraints.

• Journal of applied mathematics & informatics
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• v.18 no.1_2
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• pp.95-112
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• 2005

This work concerns the stabilization of uninfected steady state of an ordinary differential equation system modeling the interaction of the HIV virus and the immune system of the human body. The control variable is the drug dose, which, in turn, affects the rate of infection of $CD4^{+}$ T cells by HIV virus. The feedback controller is constructed by a variant of the receding horizon control (RHC) method. Simulation results are discussed.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.88-94
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• 1993

In this paper we analyze the RHLQG/FIRF optimal.contol law presented in [4,5] in order to stabilizes a stochastic linear time varying systems with modeling uncertainty. It is shown by the frequency domain analysis that the RHC is robuster than the LQ control law. Explicit LTR procedures are given to improve the robust performance of RHLQC/FIRF cotrol law. Using the mismatching function technique [8], we propose an LTR method which makes the RHLQG/FIRF controller recover the feedback properties of the R.HC law. Also we compare the LTR performance of the RHLQC/FIRF via simulation with conventional LTR methods.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.115-118
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• 2003

In recent years, a receding horizon guidance law based on receding horizon control and optimal control is proposed. A receding horizon guidance law considering autopilot lag and constraints is proposed. The performance of receding horizon guidance law in the presence of target maneuvers is confirmed by simulation results. Through many simulation, a suitable selection of weighting matrix can minimize effect of disturbance, target acceleration. which is meaning of this paper.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.83.4-83
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• 2002

$\bullet$ We present a state-feedback RHC for discrete-time TS fuzzy systems with input constriants. $\bullet$ The controller employ the current and one-step past information on the fuzzy weighting functions. $\bullet$ It is obtained from the finite horizon optimization problem with the invariant ellipsoid constraint $\bullet$ Under parameterized LMI conditions on the terminal weighting matrix $\bullet$ The closed-loop system stability is guaranteed. $\bullet$ The parameterized linear matrix inequalities are relaxed to a finite number of solvable LMIs.

• 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.