Seminar: Parameter Estimation and Adaptive Control of Nonlinear Systems with Nonlinear Parameterizations, 22nd June, 1pm

When: Thursday 22nd of June, 1pm AEST

Where: This seminar will be partially presented at the Rose Street Seminar area (J04) and partially online via Zoom. RSVP

Speaker: Prof Romeo Ortega

Title: Parameter Estimation and Adaptive Control of Nonlinear Systems with Nonlinear Parameterizations


In this talk we address the challenging problem of designing globally convergent parameter estimators and adaptive controllers for nonlinear nonlinearly parameterized (NLP) systems. We provide solutions for two kind of parameterizations: (i) separable parameterizations—that is when we can factor the parameter dependent terms as

where u and y are measurable and θ is the unknown parameter vector—where we moreover assume that the mappings ψi(θ) satisfy a monotonizability property. (ii) non-separable NLP containing exponential terms of the form

These kind of NLP appears in many physical processes including Arrenhius laws in biochemical reactors, windmill systems, fuel cell systems, photovoltaic arrays and models of elastic moments in human musculoskeletal dynamics. In this talk we present a systematic methodology for the parameter identification and adaptive control of systems containing this kind of exponential terms. The applicability of our results is illustrated with examples of all the aforementioned list of physical systems.

The procedure does not assume that the parameters leave in known compact sets, that the nonlinearities satisfy some Lipschitzian properties, nor rely on injection of high-gain or the use of complex, computationally demanding methodologies. Instead, we propose to design a classical on-line estimator whose dynamics is described by an ordinary differential equation given in a compact precise form. A further contribution of the paper is the proof that parameter convergence is guaranteed with the extremely weak interval excitation requirement. To achieve this remarkable property we rely on the utilization of a recently introduced parameter estimator that seamlessly combines a classical least-squares search with the dynamic regressor extension and mixing estimation procedure.


Romeo Ortega was born in Mexico. He obtained his BSc in Electrical and Mechanical Engineering from the National University of Mexico, Master of Engineering from Polytechnical Institute of Leningrad, USSR, and the degree of Docteur D’Etat from the Polytechnical Institute of Grenoble, France in 1974, 1978 and1984 respectively.

He then joined the National University of Mexico, where he worked until 1989. He was a Visiting Professor at the University of Illinois in 1987-88 and at McGill University in 1991-1992, and a Fellow of the Japan Society for Promotion of Science in 1990-1991. He was a member of the French National Research Council (CNRS) from June 1992 to July 2020, where he was a Directeur de Recherche in the Laboratoire de Signauxet Systémes (CentraleSupelec)in Gif-sur-Yvette, France. Currently, he is a full time Professor at ITAM in Mexico. His research interests are in the fields of nonlinear and adaptive control, with special emphasis on applications.

Dr Ortega has published five books and more than 375 scientific papers in international journals, with an h-index of 92. He has supervised 35 PhD thesis. He is a Fellow Member of the IEEE since 1999 (Life2020), an IFAC Fellow since 2016 and an Emeritus member of the Mexican National Research System. He has served as chairman in several IFAC and IEEE committees and participated in various editorial boards of international journals. He is currently Editor in Chief of the Int. J. Adaptive Control and Signal Processing and Senior Editor of the Asian Journal of Control.


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