• Journal of Institute of Control, Robotics and Systems
• /
• v.20 no.3
• /
• pp.235-244
• /
• 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
• /
• 2002.10a
• /
• pp.52.1-52
• /
• 2002

Young Sam Lee : He is currently a PhD candidate student. His research interest includes time-delay systems, signal processing, and receding horizon control. Wook Hyun Kwon : His research interest includes time-delay systems, signal processing, receding horizon control, and robust control. He is the president of IFAC 2008 which is to be held in Korea. Soo Hee Han : He is currently a PhD candidate student. His research interest includes time-delay systems, signal processing, receding horizon control, and communication.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.33B no.10
• /
• pp.1-11
• /
• 1996

In this paper, a robust receding horizon control algorithm, which can be employed for a wide class of nonlinear systems with control and state constraints, modeling errors, and disturbances, is considered. In a neighborhood of the origin, a linear feedback controlelr for the linearized system is applied. Outside this neighborhood, a receding horizon control is applied. Robust stability is proved considering the time taken to solve an optimal control problem so that the proposed algorithm can be applied as an on-line controller.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.1896-1900
• /
• 2003

In this paper, a new type of output feedback control, called a receding horizon finite memory control (RHFMC), is proposed for stochastic discrete-time state space systems. Constraints such as linearity and finite memory structure with respect to an input and an output, and unbiasedness from the optimal state feedback control are required in advance. The proposed RHFMC is chosen to minimize an optimal criterion with these constraints. The RHFMC is obtained in an explicit closed form using the output and input information on the recent time interval. It is shown that the RHFMC consists of a receding horizon control and an FIR filter. The stability of the RHFMC is investigated for stochastic systems.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.27.2-27
• /
• 2001

This paper proposes a receding horizon predictive control (RHPC) for nonlinear time-delay systems. The control law is obtained by minimizing finite horizon cost with a terminal weighting functional. An inequality condition on the terminal weighting functional is presented, under which the closed-loop stability of RHPC is guaranteed, A special class of nonlinear time-delay systems is introduced and a systematic method to find a terminal weighting functional satisfying the proposed inequality condition is given for these systems. Through a simulation example, it is demonstrated that the proposed RHPC has the guaranteed closed-loop stability for nonlinear time-delay systems.

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

• Bulletin of the Korean Mathematical Society
• /
• v.57 no.2
• /
• pp.311-330
• /
• 2020

This paper investigates infinite horizon optimal control problems driven by a class of backward stochastic delay differential equations in Hilbert spaces. We first obtain a prior estimate for the solutions of state equations, by which the existence and uniqueness results are proved. Meanwhile, necessary and sufficient conditions for optimal control problems on an infinite horizon are derived by introducing time-advanced stochastic differential equations as adjoint equations. Finally, the theoretical results are applied to a linear-quadratic control problem.

• International Journal of Control, Automation, and Systems
• /
• v.1 no.3
• /
• pp.282-288
• /
• 2003

It is known that HIV (Human Immunodeficiency Virus) infection, which causes AIDS after some latent period, is a dynamic process that can be modeled mathematically. Effects of available anti-viral drugs, which prevent HIV from infecting healthy cells, can also be included in the model. In this paper we illustrate control theory can be applied to a model of HIV infection. In particular, the drug dose is regarded as control input and the goal is to excite an immune response so that the symptom of infected patient should not be developed into AIDS. Finite horizon optimal control is employed to obtain the optimal schedule of drug dose since the model is highly nonlinear and we want maximum performance for enhancing the immune response. From the simulation studies, we found that gradual reduction of drug dose is important for the optimality. We also demonstrate the obtained open-loop optimal control is vulnerable to parameter variation of the model and measurement noise. To overcome this difficulty, we finally present nonlinear receding horizon control to incorporate feedback in the drug treatment.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.1951-1956
• /
• 2003

It is known that HIV (Human Immunodeficiency Virus) infection, which causes AIDS after some latent period, is a dynamic process that can be modeled mathematically. Effects of available anti-viral drugs, which prevent HIV from infecting healthy cells, can also be included in the model. In this paper we illustrate control theory can be applied to a model of HIV infection. In particular, the drug dose is regarded as control input and the goal is to excite an immune response so that the symptom of infected patient should not be developed into AIDS. Finite horizon optimal control is employed to obtain the optimal schedule of drug dose since the model is highly nonlinear and we want maximum performance for enhancing the immune response. From the simulation studies, we find that gradual reduction of drug dose is important for the optimality. We also demonstrate the obtained open-loop optimal control is vulnerable to parameter variation of the model and measurement noise. To overcome this difficulty, we finally present nonlinear receding horizon control to incorporate feedback in the drug treatment.

• International Journal of Control, Automation, and Systems
• /
• v.2 no.1
• /
• pp.15-22
• /
• 2004

Some recent advances in stability and optimality for the nonlinear receding horizon control (NRHC) or the nonlinear model predictive control (NMPC) are assessed. The NRHCs with terminal conditions are surveyed in terms of a terminal state equality constraint, a terminal cost, and a terminal constraint set. Other NRHCs without terminal conditions are surveyed in terms of a control Lyapunov function (CLF) and cost monotonicity. Additional approaches such as output feedback, fuzzy, and neural network are introduced. This paper excludes the results for linear receding horizon controls and concentrates only on the analytical results of NRHCs, not including applications of NRHCs. Stability and optimality are focused on rather than robustness.