Non-fragile robust model predictive controller design for uncertain time-delay systems with input constraints

ABSTRACT

This paper addresses a non-fragile robust model predictive control design for a class of continuous-time uncertain systems with multiple state-delay and constrained control signals. The parameters of the system and the control gain are assumed to have perturbation in the additive form. The Lyapunov–Krasovskii functional approach is employed to derive sufficient conditions for determining a non-fragile robust state-feedback controller for all admissible uncertainties by minimising the upper bound of the defined quadratic cost function with respect to some linear matrix inequalities (LMIs). An additional inequality condition is imposed to address the constraint associated with the control signals. Numerical simulations are provided to assess the performance of the proposed controller.

KEYWORDS:

• Non-fragile controller
• robust control
• model predictive control
• input constraints
• time-delay systems
• linear matrix inequality

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article.

Additional information

Notes on contributors

Fateme Baneshi

Fateme Baneshi is a PhD student at the Department of Bioengineering and Aerospace Engineering, Universidad Carlos III de Madrid. She received her B.Sc. and M.Ss degrees in control engineering in 2019, and 2021, respectively. Her research interests include Optimal Control, Robust Control, Trajectory Optimization, Aviation, Climate Change, and Air Traffic Management Research.

Valiollah Ghaffari

Valiollah Ghaffari received the B.Sc., M.Sc., and Ph.D. degrees in electrical engineering from Shiraz University, Shiraz, Iran, in 2006, 2009, and 2014, respectively. He is currently an Associate Professor with the Department of Electrical Engineering, Persian Gulf University. His research interests include robust control, nonlinear control systems, model predictive control, adaptive control, and hybrid control systems.

Manuel Soler

Manuel soler is an Associate Professor in the Department of Aerospace Engineering at UC3M, He is Director of the Doctoral Program in Aerospace Engineering and a member of the research group in Aerospace Engineering, where he leads the UC3M Aeronautical Operations Laboratory. He has been visiting scholar at the ETH Zürich and the U.C. Berkeley. He is currently developing two lines of research: 1) the application of artificial intelligence techniques to problems related to aeronautical meteorology, air traffic and climate change; 2) optimization of aircraft trajectories and climate change. He has been PI of 12 competitive projects (7 European, 2 of them as coordinator) and he has directed or is directing 13 doctoral theses. He has published more than 30 JCR articles, 3 book chapters and more than 50 articles in conference proceedings, many of these publications in international collaboration.