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:


Estimated results:

Obtained results / Rezultate obținute:

Research reports:

Overall results (2020-2022):

Results in 2022

  • 1 final research report.
  1. Radac M.-B., Trajectory Tracking within a Hierarchical Primitive-Based Learning Approach, Entropy, vol. 24, no 7, id:889, 2022, impact factor (IF) at publication = 2.738 (link).
  2. Lala T., Chirla D.-P., Radac M.-B., Model Reference Tracking Control Solutions for a Visual Servo System Based on a Virtual State from Unknown Dynamics, Energies, vol. 15, no 1., id:267, 2022, impact factor (IF) at publication = 3.004 (link).
  3. Radac M.-B. and Borlea, A.-B., "Learning Model-Free Reference Tracking Control with Affordable Systems" in Intelligent Techniques for Efficient Use of Valuable Resources - Knowledge and Cultural Resources, L. Ivascu et al. (eds), Springer Book Series, 2022, pp. 1-26. (link).
  4. Borlea, A.-B. and M.-B. Radac. "A hierarchical learning framework for generalizing tracking control behavior of a laboratory electrical system" in Proc. 2022 IEEE 17th International Conference on Control & Automation (ICCA) June 27-30, 2022 (Hybrid) Naples, Italy, pp 231-236. (link).
  5. Borlea, A.-B., Lala, T., and Radac, M.-B. 2022b. "A hierarchical learning approach for generalized trajectory tracking validated on a magnetic bearing system." in Proc. International Conference on Electrical, Computer and Energy Technologies (ICECET 2022), 20-22 July 2022, Prague-Czech Republic. (link).

Results in 2021

  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 (

Results in 2020: