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Joël Bordeneuve-Guibé


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 Contact details :

- ISAE-Supaéro
- Département Conception et Conduite des Véhicules Aéronautiques et Spatiaux (DCAS)
- 10 avenue Édouard Belin - BP 54032 - 31055 Toulouse cedex 4
- Tel : +33 (0)5 61 33 84 24
Mail : joel.bordeneuve isae.fr

 Teaching activities :

Courses taught :

  • Modelisation and Control of Linear Systems
  • Linear Control Systems
  • Analysis and Control of Multi-Inputs Multi-Outputs Systems
  • Introduction to Adaptive Control
  • Introduction to Real Time Onboard Control Systems

 Research activities :

  • Adaptive Techniques for the Control and Guidance of UAVs : The control of Unmanned Aerial Vehicles (UAVs) is very challenging for the complexity and variety of flight scenarios. We investigate the potentialities of adaptive techniques for the low level control of flight : a controller with fixed structure and varying parameters is used together with an adaptation algorithm in order to track a desired reference model. This technique allows a high level of control performances together with a low complexity controller.
  • Adaptive Control of Flexible Structures : We consider the active damping of poorly damped mechanical structures. The combination of active techniques together with adap- tive controllers lead to a significant improvement of damping. Thus highly flexible structures can be efficiently controlled, even when its mechanical behavior is partially known or varying with time.
  • Multidisciplinary Design Optimization for Aerospace Vehicles : The problem of integrated design of control surfaces size and control laws for a blended wing-body aircraft is addressed here. Latest tools for non-smooth optimization of structured controllers are used to optimize, in a single loop, gains for longitudinal and lateral control laws together with minimizing control surfaces total span.
  • Modelization of Complex Dynamic Systems : In the field of systems biology, we address the development of models in order to analyze and predict some particular dysfunctions. It appears that some biological systems require hybrid complex models including some stochastic aspects. An engineering-based procedure will be developed in order to improve model fitting and analysis and then to allow the validation of new biological hypothesis.

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