Alignment of velocity fields for video surveillance

Velocity fields play an important role in surveillance since they describe typical motion behaviors of video objects (e.g., pedestrians) in the scene. This paper presents an algorithm for the alignment of velocity fields acquired by different cameras, at different time intervals, from different viewpoints. Velocity fields are aligned using a warping function which maps corresponding points and vectors in both fields. The warping parameters are estimated by minimizing a non-linear least squares energy. Experimental tests show that the proposed model is able to compensate significant misalignments, including translation, rotation and scaling

Computing Parity Space Residuals’ Computational Form With MIMO Predictive Models

Trabalho apresentado em 12th Mediterranean Conference on Control and Automation MED’04, 6-9 de Junho de 2004, Kusadasi, Turquia.N/

Stability of Discrete Systems Controlled in the Presence ofIntermittent Sensor Faults

info:eu-repo/semantics/publishedVersio

Stability in the Pseudo-state Formalism of Discrete Systems Controlled in the Presence of Intermittent Sensor Faults

Trabalho apresentado em IFAC Workshop on Adaptation and Learning in Control and Signal Processing, 26-28 de Agosto de 2010, Antalya, Turquia.N/

A control Lyapunov function approach to Adaptive Control of HIV-1 Infection

This paper presents an algorithm for nonlinear adaptive control of the viral load in HIV-1 infection. The infection model considered is a reduced complexity nonlinear state-space model with two state variables, that represent the plasma concentration of uninfected and infected CD4+ T-cells of the human immune system. The viral load is assumed to be proportional to the concentration of infected cells. First, a change of variables that exactly linearizes the system is obtained. For the resulting linear system the manipulated variable is obtained by state feedback. To compensate for the uncertainty in the infection parameter of the model an estimator based on a Control Lyapunov Function is designed

Optimization strategies for metabolic networks

<p>Abstract</p> <p>Background</p> <p>The increasing availability of models and data for metabolic networks poses new challenges in what concerns optimization for biological systems. Due to the high level of complexity and uncertainty associated to these networks the suggested models often lack detail and liability, required to determine the proper optimization strategies. A possible approach to overcome this limitation is the combination of both kinetic and stoichiometric models. In this paper three control optimization methods, with different levels of complexity and assuming various degrees of process information, are presented and their results compared using a prototype network.</p> <p>Results</p> <p>The results obtained show that Bi-Level optimization lead to a good approximation of the optimum attainable with the full information on the original network. Furthermore, using Pontryagin's Maximum Principle it is shown that the optimal control for the network in question, can only assume values on the extremes of the interval of its possible values.</p> <p>Conclusions</p> <p>It is shown that, for a class of networks in which the product that favors cell growth competes with the desired product yield, the optimal control that explores this trade-off assumes only extreme values. The proposed Bi-Level optimization led to a good approximation of the original network, allowing to overcome the limitation on the available information, often present in metabolic network models. Although the prototype network considered, it is stressed that the results obtained concern methods, and provide guidelines that are valid in a wider context.</p

Optimal Control for Vehicle Cruise Speed Transfer

The contribution of this paper consists in a procedure to solve the optimal cruise control problem that consists in transferring the car velocity between two specified values, in a fixed interval of time, with minimum fuel consumption. The solution is obtained by applying a recursive numerical algorithm that provides an approximation to the condition provided by Pontryagin’s Optimum Principle. This solution is compared with the one obtained by using a reduced complexity linear model for the car dynamics that allows an exact (“analytical”) solution of the corresponding optimal control problem. This work has been performed within the framework of activity 2.4.1 – Smart drive control of project SE2A - Nanoelectronics for Safe, Fuel Efficient and Environment Friendly Automotive Solutions, ENIAC initiative

Experience of a predictive adaptive controller on pilot and industrial plants with transport phenomena

The existing experience on using MUSMAR, a predictive adaptive controller, on industrial and large scale pilot plants with transport phenomena is discussed. The processes to control have been selected because their dynamics depends not only on time, but also on space, being therefore described by partial differential equations, and implying increase difficulties for the controller. Case studies on an industrial boiler, an arc-welding machine, a distributed collector solar field and a water distribution canal are used to illustrate the main difficulties and the corresponding solutions when using MUSMAR. These include plant model uncertainty and start-up adaptation transients, large and uncertain plant i/o transport delay, existence of un-modelled dynamics, closed-loop response shaping and constraints. The emphasis of the presentation is on the practical impact of the theoretical properties of the MUSMAR algorithm and on their illustration by means of actual experiments on the real processes mentioned above

Multiple model adaptive control of neuromuscular blockade: Design guidelines and clinical cases

The high level of uncertainty of the dynamic response of patients subject to anaesthesia motivates the use of adaptive control methods. This paper proposes an approach based on Switched Multiple Model Adaptive Control (SMMAC) to tackle this problem in what concerns the control of the neuromuscular blockade level. It is shown how to design the different elements of the SMMAC controller, enhancing the importance of the observer polynomial, that is shown to be instrumental to stabilize the loop. Clinical results using atracurium as blocking agent are reported, thereby illustrating the application of the proposed approach in actual clinical practice

Nonlinear and Adaptive Control of a HIV-1 Infection Model

This paper presents algorithms for nonlinear and adaptive control of the viral load in a HIV-1 infection model. The model considered is a reduced complexity nonlinear state-space model with two state variables, representing the plasma concentration of un-infected and infected CD4+ T-cells of the human immune system. The viral load is assumed to be proportional to the concentration of infected cells. First, a change of variables that exactly linearizes this system is obtained. For the resulting linear system the manipulated variable is obtained by state feedback. To compensate for uncertainty in the infection parameter of the model an adaptation mechanism based on a Control Lyapunov Function is designed. Since the dependency on parameters is not linear, an approximation is made using a first order Taylor expansion