Adaptive Control

SUMMARYThis article presents a switched MRAC controller for discrete-time piecewise linear systems. In the spirit of the work by Landau [1], the proof of asymptotic stability of the corresponding closed-loop error is carried out recasting its dynamics as a feedback system and showing the appropriate passivity features of the feedforward and the feedback paths. The challenge lies on the fact that both loops can be piecewise linear. Numerical results show excellent performance of the proposed controller even in the face of sudden variations of the plant parameters.

show abstract

“…The material in this section has been mainly extracted from [19] and [12]. Consider a generic state-space system Σ of the form:…”

Section: A Basics Of Discrete-time Passivitymentioning

confidence: 99%

“…The notable difference is that in our case, both the feedforward and feedback paths of such system can be PWA. Then, by using the notion of passivity for discrete-time Linear Time-Varying (LTV) systems in [12], we show that the MRAC scheme proposed in this paper renders the origin of the error system globally asymptotically stable.…”

Section: Introductionmentioning

confidence: 99%

Model reference adaptive control of discrete‐time piecewise linear systems

Bernardo

Montanaro

Olm

et al. 2012

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

show abstract

“…Self-tuning adaptive control based on linear parametric models is a well-established methodology for linear or linearized processes, especially for the regulation problem (AstroK m & Wittenmark, 1995;Landau, Lozano & M'Saad, 1998). In the case of non-linear processes, the linear parametric approach for modelling can be unsatisfactory because the linear model may not be able to represent appropriately the process in the entire region spanned by the reference.…”

Section: Introductionmentioning

confidence: 99%

Adaptive RBFNN versus conventional self-tuning: comparison of two parametric model approaches for non-linear control

Pereira

Henriques

Dourado

2000

Control Engineering Practice

View full text Add to dashboard Cite

In this work a practical study evaluates two parametric modelling approaches * linear and non-linear (neural) * for automatic adaptive control. The neural adaptive control is based on a developed hybrid learning technique using an adaptive (on-line) learning rate for a Gaussian radial basis function neural network. The linear approach is used for a self-tuning pole-placement controller. A selective forgetting factor method is applied to both control schemes: in the neural case to estimate on-line the second-layer weights and in the linear case to estimate the parameters of the linear process model. These two techniques are applied to a laboratory-scaled bench plant with the possibility of dynamic changes and di!erent types of disturbances. Experimental results show the superior performance of the neural approach particularly when there are dynamic changes in the process.

show abstract

“…Moreover, the laboratory model is a nonlinear system with variable parameters and its description by a linear model is valid only in a neighbourhood of a steady state. Self-tuning controllers [11], [12] are a possible approach to the control of this kind of system. However, the state -space description is not quite suitable for a recursive identification of the parameters of the process which is performed during control with self -tuning controllers.…”

mentioning

confidence: 99%

Adaptive Predictive Control of Three - Tank - System

Kubalcik¹,

Bobál²

2010

Modelling, Identification, and Control

View full text Add to dashboard Cite

Abstract-This paper is focused in application of a self -tuning predictive controller for real -time control of a three -tank -system laboratory model. The objective laboratory model is a two inputtwo output (TITO) nonlinear system. It is based on experience with authentic industrial control applications. The controller integrates a predictive control synthesis based on a multivariable state -space model of the controlled system and an on -line identification of an ARX model corresponding to the state -space model. The model parameters are recursively estimated using the recursive least squares method with the directional forgetting. The control algorithm is based on the Generalised Predictive Control (GPC) method. The optimization was realized by minimization of a quadratic objective function. Results of real-time experiments are also included. Keywords-About four key words or phrases in alphabeticalorder, separated by commas.

show abstract

Adaptive Control

Cited by 278 publications

References 75 publications

Model reference adaptive control of discrete‐time piecewise linear systems

Model reference adaptive control of discrete‐time piecewise linear systems

Adaptive RBFNN versus conventional self-tuning: comparison of two parametric model approaches for non-linear control

Adaptive Predictive Control of Three - Tank - System

Contact Info

Product

Resources

About