Team 2: New technologies for industrial process control

Project Leader

Messaoud Hassani

Position: Professor

Involved Faculty Members

PhD Students to be mobilized within the project

Preamble

The rapid development of technologies and the intense competition among industrialists to gain the maximum share of national and international markets opens the way for researchers to develop new approaches and/or techniques to better control industrial processes in order to satisfy the performance suggested by the customer. These objectives require a perfect knowledge of these processes as well as the adaptation of existing approaches and the synthesis of new ones for the control of such processes, which can be of different natures, including continuous processes that can be linear, non-linear, singular, delayed, etc., and discrete processes or more precisely discrete event processes.

The proposed approaches concern the identification, diagnosis and control of these systems, emphasizing the insertion of artificial intelligence in the design operation. The real processes addressed in this project are robots and autonomous vehicles, electric vehicles, thermal engines, mechatronic systems, railway systems, electrical networks and transport systems.

Objectives

For identification we are led to design techniques that make it possible to estimate the model that best reproduces the behavior of the system for any set of measurements taken from the latter. In this theme, we opt for methods using statistical learning and we deal with all forms of the considered processes (linear, non-linear, stationary or not, with or without delay, etc.

For control we are interested in two types of systems, continuous systems and discrete event systems. Given the multitude of results proposed in the literature in terms of control algorithms, we will focus essentially on the design of new approaches for systems with particular specifications and we will place more emphasis on applications. In this context, we are interested in approaches using concepts from artificial intelligence such as meta-heuristics, artificial neural networks and more precisely those using deep learning for neural control of systems with variable delay and especially singular systems.

As applications we are interested in the rate of pollutant gases from a thermal engine, electric vehicles, robots and autonomous vehicles, electrical energy networks,

For observers where we are interested in particular cases concerning the synthesis of observers for singular delayed systems with unknown inputs and especially in the presence of uncertainties on the system parameters. We also propose the synthesis of filters for these systems with delay on the state and on the input and excited by finite energy disturbances.

When the singular system is non-linear and is affected by unknown inputs, we propose a state and unknown input estimation method using a multi-model approach. We also propose the synthesis of a filter in the frequency domain and thus apply control approaches.

Similarly when the singular system is bilinear we propose to synthesize correctors and observers for this type of system when they have delays and are subject to unknown inputs present at the state equation and the output equation.

Finally the case of uncertain linear systems will be the subject of the same studies.

Team organization

There are four teams contributing to the project, which interfere and deal with several themes at the same time. It should be noted that all contributions will be validated either on the laboratories' own processes or on processes within other laboratories abroad with which our laboratory maintains collaborative links.

Project implementation timeline

International partners

• 1st organization: G-SCOP, Grenoble - France
• 2nd organization: CRISTAL in Lille
• 3rd organization: Laboratoire des technologies innovantes (LTI) Amiens, France
• 4th organization: Laboratoire Universitaire de Recherche en Production Avancée (LURPA) Paris Saclay, France
• 5th organization: Institut de Recherche en Systèmes Electroniques Embarqués (IRSEEM) Rouen, France

Expected scientific productions

Expected results include:

• Scientific publications in international journals
• Patent filings for technical innovations
• Writing and defense of doctoral theses
• Preparation of habilitations to direct research
• Development of prototypes and demonstrators

Economic and social impacts

• Providing the concerned industry with expertise on the control of discrete systems.
• Applications of intelligent control strategies using new techniques based on deep learning
• Providing the concerned industry with expertise on the classification and detection of medical image defects.