Anna Patete

Self-tuning control based on generalized minimum variance criterion for auto-regressive models

Theoretical problems on self-tuning control include stability, performance and convergence of the recursive algorithm involved. In this paper, the problem of controlling minimum or non-minimum phase auto-regressive models with constant but unknown parameters is considered. The stability of an algorithm obtained by combining a recursive estimator for the controller parameters and a generalized minimum variance criterion is proved. The main result is the theorem which assures the overall stability for the closed-loop system in presence of white noise in the input-output relation, where the estimated parameters do not necessarily converge to the true values. The algorithm is proved by the Lyapunov theory.

Picard discretization of nonlinear systems: symbolic or numeric implementation?

In this paper we report the numeric implementation of previously proposed symbolic non-standard discretization methods for nonlinear dynamical control systems. We discuss the advantages and disadvantages of both symbolic and numeric implementations, and illustrate them through their applications to case studies.

Improved non-standard discretization methods for nonlinear dynamical control systems

In this paper we describe two modified versions of the Euler-Picard and Euler-Taylor-Picard discretization methods for nonlinear dynamical control systems. We use an upper bound for the absolute difference of each pair of consecutive Picard iterations, in the first case to control the number of Picard iterations, and in the second case to control the sampling frequency, while keeping the maximum allowed number of Picard iterations fixed. These non-standard discretization methods are used to support the construction of computer animated mimics of nonlinear dynamical control systems.

Self-tuning control based on generalized minimum variance criterion

Abstract The stability of adaptive control systems has been studied extensively for minimum phase systems, mainly for model reference adaptive systems, but complete stability proof for non-minimum phase systems have not been given. In this paper, the stability of two types of self-tuning controllers for discrete time minimum and non-minimum phase plants is studied, namely: recursive estimation of the implicit self-tuning controller parameters based on generalized minimum variance criterion (REGMVC), and another based on generalized minimum variance criterion - β equivalent control approach (REGMVC-β). Stability of the algorithms are proved by the Lyapunov theory.

Self-tuning of repetitive controllers based on generalized minimum variance criterion

This paper deals with discrete time robust repetitive control, for minimum and non-minimum phase plants. The paper is divided in two parts. The first part is devoted to the repetitive control design based on generalize minimum variance criterion. In the second part, self-tuning of repetitive controllers based on generalized minimum variance criterion is proposed. Also analyzed is robustness of the repetitive control system in presence of parametric uncertainties in the plant model. We will show through simulation examples the fast converge of the output signal to the repetitive reference signal (tracking problem), under the proposed self-tuning method

Design of a multivariable implicit self-tuning controller

In this paper, an implicit multivariable self-tuning controller is designed based on the Lyapunov function. The STC parameters convergence is proved when the numbers of plant input and output signals are same. The obtained result is a generalization of the work of Patete et al. (2008) to the multivariable case.

CONTROL POR SEGUIMIENTO DE REFERENCIA PARA EL SISTEMA CAPSUBOT

This paper present a new technique to control, in closed loop, the Capsubot system. The technique combines the generalized minimum variance criterion with the sliding mode control concept in discrete -time. The control objective is to follow the reference signal, through the minimization of the controlled variable. The reference is chosen as the desired velocity dynamic for the internal mass in the Capsubot system. This will produce the appropriate movement for the Capsubot system. The nonlinear Capsubot model is simplified to a linear model, so the proposed control technique may be applied.

Self-tuning control of time-varying systems based on generalized minimum variance criterion

Important issues on self-tuning control include stability, performance and convergence of the recursive algorithm involved. Based on a Lyapunov function, this paper proves the stability of a self-tuning control combining recursive controller parameters estimation considering a forgetting factor and a generalized minimum variance criterion for time- varying systems. The main result is the theorem which assures the overall stability for the closed-loop system. The system parameters are considered to be changing continuously but slowly or change abruptly but infrequently.