Intelligent control systems with generalizable behaviour from learned primitives (INTELICOS), 382'049 RON (78'500 EUR), national research grant Young Teams (TE), financed by the Executive Agency for Higher Education, Research, Development and Innovation Funding - UEFISCDI, 2020-2022, project code: PN-III-P1-1.1-TE-2019-1089

The team:

• Assist. Prof. Dr. Mircea-Bogdan Radac - director, principal investigator.
• M.Sc. Eng. Alexandra-Bianca Borlea - Ph.D. student
• Eng. Timotei Lala - M.Sc. student

Abstract:

• The project proposal aims at the continuous development of an hierarchical primitives-based learning concept for intelligent control systems (CSs). The idea is to induce feedback CSs with a generalization capability towards tracking tasks, inspired by intelligent living beings who can extrapolate learned optimal behavior to new unseen tasks without learning by repetitions. The framework operates on three levels: L1) low level feedback CS design in model-free data-driven manner to ensure reference tracking, disturbance rejection and indirect CS linearization;L2) learning tracking tasks (in terms of CS reference input + controlled output pairs, called primitives) by repeted executions via model-free data-driven Iterative Learning Control (ILC), over the feedback CS, in terms of a given optimal criterion; L3) extrapolate the learned optimal tracking behavior to new tracking tasks, without needing repetitions. To make the above framework impactful, improvement is needed to: a) ensure strong CS linearization at lower level, in an output reference model tracking problem setting, since the generalizability of the learned tracking bevahvior relies on the superposition principle of the linear CS; b) ensure learning convergence at level L2 via ILC, while reducing the number of dedicated gradient experiments; c) deal with tracking tasks of different time length (shorter/longer) than that of the learned primtives and with operational constraints. The project’s main goals are: to improve the issues a), b), c) and to experimentally validate the hierarchical learning framework on different processes of different nature (tracking tasks are ubiquitous); publish the results in visible journals and conferences; solve the project management issues.

Estimated results:

• The publication of papers in leading journals, participation and presentation of papers in international academic conferences, three scientific reports (two intermediate and a final one).

Research reports:

• Research report for 2021 (in Romanian).
• Research report for 2020 (in Romanian).

Overall results (2020-2022):

1. Radac M.-B., Lala T., Hierarchical Cognitive Control for Unknown Dynamic Systems Tracking, Mathematics, vol. 9, no 21., id:2752, 2021, impact factor (IF) at publication = 2.258 (link).
2. Radac M.-B., Lala, T., A Hierarchical Primitive-Based Learning Tracking Framework for Unknown Observable Systems Based on a New State Representation, in Proc. 2021 European Control Conference (ECC), Rotterdam, Netherlands, 2021, pp. 1466-1472. (link).
3. Lala, T., Radac M.-B., Learning to extrapolate an optimal tracking control behavior towards new tracking tasks in a hierarchical primitive-based framework, in Proc. 2021 29th Mediterranean Conference on Control and Automation (MED), Bari, Italy, 2021, pp. 421-427. (link).
4. Radac M.-B., Borlea A.-I., Virtual State Feedback Reference Tuning and Value Iteration Reinforcement Learning for Unknown Observable Systems Control, Energies, vol. 14, no 4., id:1006, 2021, impact factor (IF) at publication = 3.004 (link).
5. Chereji E., Radac M.-B., Szedlak-Stinean A.-I., Sliding Mode Control Algorithms for Anti-Lock Braking Systems with Performance Comparisons, Algorithms, vol. 14, no. 1, id:2, 2021 (link).
6. Radac M.-B., Lala T., Robust Control of Unknown Observable Nonlinear Systems Solved as a Zero-Sum Game, IEEE Access, vol. 8, pp. 214153-214165, 2020, impact factor (IF) at publication = 3.745 ( ieeexplore.ieee.org).

Results in 2020:

• 1 intermediate research report.