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1

Robust control of depth of anesthesia based on H∞ design

Caiado D. V., Lemos J. M., Costa B. A.

Archives of Control Sciences

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2013

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Vol. 23, no. 1

41--59

EN

This paper presents a case study on the design of a robust controller for the depth of anesthesia (DoA) induced by the drug propofol. This process is represented by a linear model together with a non-parametric uncertainty description that is evaluated using a patient model bank with 20 patients undergoing sedation. By using H∞ methods, the controller is aimed to comply with robust stability and performance specifications for the class of patient models considered. A minimization problem of sensitivity and complementary sensitivity is made to design the controller. The controller that results from this procedure is approximated by a controller with a lower order, that in turn is redesigned in discrete time for computer control application. The resulting controller is evaluated in simulations using a realistic nonlinear model of DoA.

2

A control Lyapunov function approach to adaptive control of HIV-1 infection

Lemos J. M., Barao M. S.

Archives of Control Sciences

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2012

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Vol. 22, no. 3

273-284

EN

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.

3

Supervised multiple model adaptive control of a heating fan

Lemos J. M., Marreiros L., Costa B.

Archives of Control Sciences

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2008

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Vol. 18, no. 1

5-16

EN

The paper presents a case study on the application of supervised switched multiple model adaptive control (SMMAC) to drive the temperature in an air heating fan. The plant considered is of distributed parameter type and its linearized dynamics changes with the value of air flow. The compromises in the design of the different elements of SMMAC are discussed, with emphasis on the trade-off involved in selecting the observer poles. Experimental results show the impromevent in performance of SMMAC with respect to the use of a single linear controller.

4

Self-tuning control under input variance constraint

Królikowski A., Kubiak T., Lemos J.M.

Systems Science

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2003

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Vol. 29, nr 1

135-149

EN

In this paper, a variance-constrained self-tuning control is considered for a plant given by discrete-time ARMAX model. A minimization of a quadratic cost function under constraint is approached by LQG and stochastic approximation (SA) methods, as well as by MUSMAR, a predictive adaptive controller based on multiple identifiers. The optimization algorithms obtained are simulated for unstable plant model and different structures of the controller.