• Journal of Institute of Control, Robotics and Systems
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• v.16 no.2
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• pp.140-144
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• 2010

In the case of a linear time-varying system, it is difficult to apply the conventional stability conditions of RHC (Receding Horizon Control) to real physical systems because of computational complexity comes from time-varying system and backward Riccati equation. Therefore, in this study, a frozen time RHC for a linear discrete time-varying system is proposed. Since the proposed control law is obtained by time-invariant Riccati equation solved by forward iterations at each control time, its stability can be ensured by matrix inequality condition and the stability condition based on horizon for a time-invariant system, and they can be applied to real physical systems effectively in comparison with the conventional RHC.

• 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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• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.9
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• pp.755-760
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• 2005

This paper proposes an improved implementation algorithm for the continuous-time receding horizon control (RHC). The proposed algorithm has a feature that it has better control performance than the existing algorithm. Main idea of the proposed algorithm is that we can approximate the original RHC problem better by assuming the predicted input trajectory on the prediction horizon has a continuous form, which is constructed from linear interpolation of finite number of vectors. This, in turn, leads to improved control performance. We derive a predictor such that it takes linear interpolation into account and proposes the method by which we can express the cost exactly. Through simulation study fur an inverted pendulum, we illustrate that the proposed algorithm has the better control performance than the existing one.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2471-2473
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• 2005

We present a receding horizon control [RHC] algorithm for compensation of backlash at the input of a stable linear system under control rate constraints. The problem is posed as a receding horizon optimal control [RHOptC] problem for a piecewise affine [PWA] system by modelling the backlash nonlinearity as a PWA system with a state space partition consisting of three regions. The RHC problem involves solving, at each step, $3^N$ quadratic programmes[QP], where N is the optimization horizon. This strategy leads, at the cost of some performance degradation, to much smaller computational load since a feasible rather than optimal solution has to be obtained at each step.

• 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.

• Proceedings of the KSME Conference
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• 2000.11a
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• pp.659-664
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• 2000

Fuel-consumption and catalyst-out emissions of a parallel hybrid electric vehicle are affected by operating region of an engine. In many researches, It is generally known that it is profitable in fuel- consumption to operate engine in OOL(Optimal Operating Line). We established the mathematical model of a parallel hybrid electric vehicle, which is linear time-invariant. To operate an engine in OOL, we applied RHC(Receding Horizon Control) to the driving control of a parallel hybrid electric vehicle. And it is known that the RHC has advantages such as good tracking performance under state and control constraints. This RHC is obtained by using linear matrix inequality (LMI) optimization. In this paper, there are three main topics. First, without state and control constraints, the optimal tracking of OOL was simulated. Second, with state and control constraints by engine and motor performances, the optimal tracking of OOL was simulated. In the last, we studied on the optimal gear ratio. That is to say, we combined the RHC and the iterative simulation to extract the optimal gear ratio. In this simulation, the vehicle is commanded to track the reference vehicle trajectory and the engine is operated in the optimal operating region which is made by the state constraints.

• 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.

• Advances in robotics research
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• v.2 no.2
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• pp.161-182
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• 2018

This paper proposes a novel receding horizon control (RHC) algorithm for formation control of a swarm of unmanned surface vehicles (USVs) using particle swarm optimization (PSO). The proposed control algorithm provides the coordinated path tracking of multi-agent USVs while preventing collisions and considering external disturbances such as ocean currents. A three degrees-of-freedom kinematic model of the USV is used for the RHC with guaranteed stability and convergence by incorporating a sequential Monte Carlo (SMC)-based particle initialization. An ocean current model-based estimator is designed to compensate for the effect of ocean currents on the USVs. This method is compared with the PSO-based RHC algorithms to demonstrate the performance of the formation control and the collision avoidance in the presence of ocean currents through numerical simulations.