LMI-based algorithm for strictly positive real systems with static output feedback

An algorithm based on Linear Matrix Inequalities (LMIs) is proposed to find a constant output feedback matrix K-0 and a constant output tandem matrix F such that the controlled system {A - BK0C, B, FC} is Strictly Positive Real (SPR). The number of output variables of the plant {A, B, C} is greater than or equal to the number of its input variables. Considering that SPR systems with static output feedback are related to SPR systems with static state feedback, as shown in this manuscript, the first step of the algorithm is to find a matrix F such that all transmission zeros of the system {A, B. FC} have negative real parts. After finding this matrix F. an output feedback matrix K-0 such that the system {A - BK0C, B, FC} is SPR is found. Another algorithm is proposed to specify a decay rate. The results are applied to the simulation of electrical stimulation for paraplegic patients, to vary knee joint angle from 0 degrees to 60 degrees. (C) 2012 Elsevier B.V. All rights reserved.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

Robust controller design of a wheelchair mobile via LMI approach to SPR systems with feedback output

This article discusses the design of robust controller applied to Wheelchair Furniture via Linear Matrix Inequalities (LMI), to obtain Strictly Positive Real (SPR) systems. The contributions of this work were the choice of a mathematical model for wheelchair: mobile with uncertainty about the position of the center of gravity (CG), the decoupling of the kinematic and dynamical systems, linearization of the models, the headquarters building of parametric uncertainties, the proposal of the control loop and control law with a specified decay rate

Hardware implementation of an analog neural nonderivative optimizer

Analog neural systems that can automatically find the minimum value of the outputs of unknown analog systems, described by convex functions, are studied. When information about derivative or gradient are not used, these systems are called analog nonderivative optimizers. An electronic circuit for the analog neural nonderivative optimizer proposed by Teixeira and Zak, and its simulation with software PSPICE, is presented. With the simulation results and hardware implementation of the system, the validity of the proposed optimizer can be verified. These results are original, from the best of the authors knowledge