In this paper, a receding horizon control approach is developed for voltage stabilization in power systems. The primary focus is on a benchmark system proposed in the context of a European project. The system is termed hybrid due to the finite set of control inputs, state automata description of the voltage primary controller and their interactions with the load nonlinear continuous dynamics. M...

The optimal Bayesian filter for a single target is known to provide the best tracking performance in a cluttered environment. However, its main drawback is the increase of memory size and computation quantity with time. In this paper, the inevitable problem of the optimal Bayesian filter is resolved in a suboptimal fashion by using a receding horizon strategy. As a result, the problem of memory...

Iterative learning control is a control technique used for the tracking of a finite duration trajectory. Iterative learning control (ILC) with focus on speed of tracking specific points and tracking error on these points is analyzed in this paper. A technique is introduced which employs the receding horizon optimization to track the points along with the iterative learning control is introduced...

This paper studies the performance of networked control systems with a receding horizon controller. It is also assumed that there exists exogenous noise signal in feedback channel, modeled as a stochastic process. The impact of this noise on the closed-loop system performance is examined through both theoretical analysis and numerical experiments. An adaptive compensator is proposed to assist t...

This paper describes a model of the immunologic response of the human immunodeficiency virus (HIV) in individuals. It then illustrates how a Receding Horizon Control (RHC) methodology can be used to drive the system to a stable equilibrium in which a strong immune response controls the viral load in the absence of drug treatment. We also illustrate how this feedback methodology can overcome unp...

In this paper, robust RHC (Receding Horizon Control) method with adaptive DA converter for robotic wheelchair using EEG (Electroencephalogram) based BMI (Brain Machine Interface) is discussed. The method is developed to apply to the obstacle avoidance system of the robotic wheelchair. Some numerical examples are also included to demonstrate the effectiveness of the proposed method. Key–Words: R...

[14] G. De Nicolao, L. Magni, and R. Scattolini, “Stabilizing receding-horizon control of nonlinear time-varying systems,” IEEE Trans. Automat. Contr., vol. 43, pp. 1030–1036, July 1998. [15] T. Parisini and R. Zoppoli, “A receding horizon regulator for nonlinear systems and a neural approximation,” Automatica, vol. 31, pp. 1443–1451, 1995. [16] J. A. Primbs, V. Nevistić, and J. C. Doyle, “A re...

We consider a receding horizon control scheme without terminal constraints in which the stage cost is defined by economic criteria, i.e., not necessarily linked to a stabilization or tracking problem. We analyze the performance of the resulting receding horizon controller with a particular focus on the case of optimal steady states for the corresponding averaged infinite horizon problem. Using ...

It is well known that exponentially unstable linear systems can not be globally stabilized in the presence of input constraints. In the case where the linear system is neutrally stable, one can achieve global asymptotic stability using a particular Control Lyapunov Function (CLF)based controller. Using this particular CLF as terminal cost in a receding horizon scheme, we obtain a receding horiz...

The optimal Bayesian filter for a single target is known to provide the best tracking performance in a cluttered environment. However, its main drawback is the increase in memory size and computation quantity over time. In this paper, the inevitable predicament of the optimal Bayesian filter is resolved in a suboptimal fashion through the use of a receding horizon strategy. As a result, the pro...