Identification and Control of Linear Systems


New developments in many fields, which include biology and medicine, communications, computers and networks, create new systems that require a significant expansion of control design methodologies.  These new systems are generally large scale, distributed, interconnected, time varying and stochastic. A common feature is that there is an enormous amount of data, which can be processed and used to provide high performance in the presence of uncertainties. The use of direct data-driven controller design methods can relieve the modeling task of these complicated systems. Moreover, model-based controller design methods should take into account the parametric uncertainty and time-varying characteristics of these type of systems.

Keywords : Linear; Control;

related publications related research projects related students projects

Data-Driven Controller Tuning (Time-Domain Approaches)

Data-driven model-free controller-tuning approaches try to lump the identification, controller design and  controller reduction together and present a direct “data-to-control” algorithm. This is very attractive, particularly when a mathematical model of the plant is not available and/or the nonlinear behavior of the plant cannot be identified easily and considered for controller design.  

Data-Driven Controller Tuning (Frequency-Domain Approaches)

For LTI systems, frequency-domain nonparametric models (spectral models) can be obtained by spectral analysis from time-domain data. In this type of models the information is not condensed into a small set of parameters thus avoiding errors of unmodeled dynamics that appear in parametric models. Moreover, identification of spectral models need less a priori knowledge than the parametric identification methods (sampling period, time-delay, model structure, order of polynomials, etc.). On the other hand, several time-domain performance measures can be obtained from the frequency response of the system. The Nyquist stability criterion and model uncertainty can be  represented in the frequency-domain. It is well known that major advances were obtained only when the control problem was regarded as a loop shaping problem in the frequency domain.

Robust Control Design Using the Polynomial Approach

There are many well-developed control strategies based on an appropriate dynamic plant model. In many cases, dynamic systems can be expressed in terms of a linear time-invariant transfer function with parameter uncertainty or multiple models. This type of uncertainty can be taken directly into account using the polynomial approach for robust controller design. The following research projects are considered in this area: Related projects