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This page lists the research projects I am currently involved with. Please contact me if you need additional information about any of these projects.

Grant of the Romanian National Authority for Scientific Research, CNCS - UEFISCDI, project number PN-III-P1-1.1-TE-2016-1265

  • ECOPACE: Estimation and control of delayed periodic systems: Application to engine optimization

    Start: October 2018      Duration: 2 years

    Participants: dr. Zsofia Lendek, Principal Investigator; dr. Cosmin Marcu, Member; Eng.Zoltan Nagy, Member.

    Automotive industry is a major economic sector of the EU, and technological demand for industrially applicable solutions at low costs is growing in this area. The latest engine control approaches focus on the design of increasingly economic and less polluting cars while keeping good performance in terms of power. However, currently used algorithms require the oversimplification of existing first-principle models, by reducing the order of the model and linearizing it. Furthermore, while the actuators exist for individual per-cylinder control, control methods cannot be implemented due to the fact that relevant required variables cannot be measured. The need for algorithms that do not suffer from these limitations on model complexity and unknown variables has become critical.

    ECOPACE addresses this need by proposing novel, advanced estimation and control algorithms for the cylinder-by-cylinder control of the engine. To improve performance in terms of consumption and pollution, we consider observer and controller design based on the cyclic phenomena occurring due to period cylinder motion. The algorithms will be developed in the mathematically rigorous framework of Takagi-Sugeno fuzzy systems, in which performance guarantees will be established. The main fundamental novelty will be the native ability of these algorithms to handle the nonlinear, periodic, and delayed nature of the engine system. The developed methods will be tested and validated in extensive simulations and on measured data from real engine testbenches. The expertise of the PI in nonlinear estimation and control, together with the balanced project team, ensure the feasibility of the project. Successful unknown-variable estimation and per-cylinder control will lead to better performance and high reliability of the engine, together with reduced resource consumption and costs - potentially leading to significant economic and social benefits.

    Keywords: Takagi-Sugeno fuzzy models, periodic systems, estimation and control.

    More details can be found on the project's website.

Finalized Grants

  • Handling non-smooth effects in control of real robotic systems, CNCS - UEFISCDI, project number PN-II-RU-TE-2014-4-0942

    Start: October 2015      Duration: 2 years

    Participants: dr. Zsofia Lendek, Principal Investigator; dr. Cosmin Marcu, Member; eng. Antal-Koppany Mathe, Member; Eng.Elod Pall, Member; Eng.Zoltan Nagy, Member.

    Robotics has a growing impact on our everyday life. Traditional applications are complemented by the integration of robots in the human environment. With the availability of low cost sensors, aerial robotics also became an active area of research. However, many of the practical challenges associated to the real time control of robotic systems are not yet resolved. Control of these systems requires methods that are able to reliably estimate variables of interest while compensating for sensor limitations and disturbances; and achieve the desired control objective in spite of limitations and significant changes in the model due to external effects.

    The aim of this project is to develop novel methods and algorithms that can handle non-smoothness effects and nonlinearities and are practically applicable for the control and monitoring of robotic systems. Non-smoothness, e.g., actuator and variable saturation and singularities due to practical constraints frequently appear in robotic systems. We will build on and further develop state-of-the art methods for nonlinear control and estimation. To efficiently address the nonlinear dynamics and at the same time keep it in a natural form, Takagi-Sugeno models in descriptor form will be used. We focus on assistive robotics and aerial vehicles. Assistive robotics is motivated by the societal need of increasing the independence of elderly and disabled people. Aerial vehicles have numerous applications, such as surveillance or mapping.

    Keywords: Takagi-Sugeno fuzzy models, saturation, robotic systems.

    More details can be found on the project's website.

  • Observer design for structured distributed dynamic systems, CNCS - UEFISCDI, project number PN-II-RU-TE-2011-3-0043

    Start: October 2011      Duration: 3 years

    Participants: dr. Zsofia Lendek, Principal Investigator; dr. Paula Raica, Member; eng. Paul Petrehus, Member.

    Power systems, traffic and communication networks, irrigation systems, hydropower valleys, or smart grids are composed of structured interconnections of lower-dimensional subsystems. To monitor such systems, one has to know the values of the variables in the system. Since in general not all these variables can be measured, they must be estimated, based on the system model and available measurements. However, there is no general method to design estimators for nonlinear systems. The challenge of designing an estimator becomes even more difficult if the system is distributed.

    This project aims to develop novel methods and algorithms to estimate the states and thereby monitor structured distributed systems. In order to efficiently address the nonlinear dynamics, a Takagi-Sugeno fuzzy model framework is used, and to efficiently represent the structures, graph theory will be employed. Consequently, a novel framework that efficiently combines graph theory and nonlinear systemsí theory is realized. The first and foremost contribution of this research is fundamental, by developing generic methods for observer design. The research also addresses monitoring of applications such as large-scale industrial processes, traffic networks, and energy or water distribution networks.

    Keywords: monitoring, structured interconnections, Takagi-Sugeno fuzzy models, distributed systems.

    More details can be found on the project's website.

Finalized PhD Projects

  • Efficient output feedback synthesis for quasi-LPV systems: application for disabled

    Start: October 2012      Duration: 3 years

    Participants: M.Sc.Victor Estrada Manzo, Ph.D. student; Prof. dr. Thierry Marie Guerra, supervisor; Prof. dr.Philippe Pudlo, supervisor; dr. Zsofia Lendek, co-advisor.

    Nowadays, stability and performances conditions using a Lyapunov quadratic function for LPV models or quasi-LPV models (so-called Takagi-Sugeno models) are well-known (Scherer & Weiland 04). They are generally put into the form of LMI (Linear Matrix Inequalities) and/or SOS (Sum-of-Squares) constraint problems (Boyd et al. 94). We are particularly interested in the quasi-LPV context in this PhD.

  • Adaptive state estimation and control of a hopper dredger

    Start: January 2009      Duration: 4 years

    Participants: M.Sc.Pawel Stano, PhD student; Prof. dr. Robert Babuska, supervisor; dr.Arjan den Dekker, supervisor; dr.Jelmer Braaksma, supervisor; Cees de Keizer, supervisor; dr. Zsofia Lendek, co-advisor.

    The trailing suction hopper dredger (TSHD) is a ship that excavates sand and sediments from the sea bottom while sailing. Modern TSHDs are advanced ships, equipped with many local automation systems controlled from the bridge via a computer system. A comprehensive mathematical model has been developed in previous research, integrating several sub-processes. The model is used as a basis for model-based predictive control.

    The main research topic of this research project is to investigate various methods and develop new techniques for adaptive estimation and control that can be applied for the performance improvement of a hopper-dredger. The research efforts is focus on algorithm and methodology development for distributed control systems with emphasis on developing and testing methods for parameter and state estimation in an uncertain environment.