ENACT - Commande tolérante aux défauts des systèmes dynamiques basée sur les données et augmentée par l'IA

Keywords

commande basée données, apprentissage machine, commande optimale, tolérance aux fautes, apprentissage par renforcement, efficacité énergétique

Subject details

Recent advances in computing power and the development of robust data analytics have revitalized the use of data-driven approaches [3,4,5] in the control of complex dynamical systems. Building on the pioneering works in fault-tolerant control (FTC) [1, 2] and the formulation of predictive control via behavioral system theory [6,7,8], this project proposes to merge classical data-driven FTC with state-of-the-art artificial intelligence (AI) techniques [12]. The objective is to develop fault adaptive control schemes for dynamical systems by integrating reinforcement learning (RL) [9] strategies with dimensionality reduction methods, with specific applications targeted toward multi-zone building HVAC systems [10,11] as a practical validation platform. The main issues or research questions with regards to the above objective can be broken down as follows: 1) How to design RL-based fault detection mechanisms and controllers that can adapt to both abrupt and incipient faults in dynamical systems ?. 2) How to integrate dimensionality reduction methods to handle high-dimensional sensor data for effective fault diagnosis and fault tolerance ?. 3) How to design dual learning schemes where the RL agent learns both the control policy and the fault-tolerant strategy simultaneously in real-time ?. 4) How to address the key challenge for ensuring the stability and robustness of AI-based control techniques that are mathematically critical with respect to rigorous guarantees on the boundedness of system trajectories, convergence properties, and the resilience of the control policy under disturbances and faults ? The generic nature of the expected results should enable them to be applied to a wide range of systems. In particular, the results will be validated on CRAN's newly-built Eco-sûr platform, dedicated to control issues for the Energy Efficiency of Buildings. The platform is equipped with a large number of measurement systems that provide data on a wide range of variables relating to indoor and outdoor climatic conditions in buildings. In particular, the validated results will highlight how AI can harness large volumes of real-time data to make more informed decisions and how AI-based systems can optimize indoor comfort (temperature, humidity, air quality) while minimizing energy use as this balance is crucial for creating sustainable buildings that are both eco-friendly and economically viable, supporting long-term ecological benefits. Research Plan and Expected Outcomes: Building on the core objectives, the project will follow a streamlined research plan focused on : - In the initial phase, a state-of-the-art of the artificial intelligence (AI) techniques will be performed with a focus on Control Theory. - Then, theoretical tools will be developed for AI-augmented control architecture that unifies behavioral system theory with modern Reinforcement Learning algorithms. These tools should be able to harness nominal system trajectories as well as real time faulty behaviors. - The next phase involves implementing this framework in a simulation environment using Python and MATLAB/Simulink, where its performance will be rigorously evaluated under a range of fault scenarios. Real-world experiments will then be conducted on the Eco-sûr platform to validate the approach's effectiveness in enhancing energy efficiency and fault resilience in multi-zone building Heating Ventilation and Air-Conditioning systems.