 LAB FOR COOPERATIVE ARTIFICIAL
INTELLIGENCE AND ADVANCED CONTROL SYSTEMS |
Prof Adnan Tahirovic
University of Sarajevo,
Faculty of Electrical Engineering,
Department of Automatic Control
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RESEARCH FOCUS
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The research focus of the Lab for Cooperative Arificial Intelligence and Advanced Control Systems includes:
- Nonlinear systems (optimal control, optimal estimation)
- Network systems (optimal control, Multi-agent Systems, multi-agent reinfocement learning, complex networks, Graph Neural Networks, Graph Adversarial Networks)
- Mobile robotics (motion planning in difficult environments, autonomous and multi-agent systems)
- Computational neuroscience (brain modeling, optimal control of network systems, complex networks, network flow)
- Artificial intelligence and machine learning
(multi-agent reinforcement learning, transfer learning in multi-agent systems, optimal control and reinforcement learning, motion
planning for fast learning in multi-agent systems)
The research group of Prof. Tahirovic has recently made significant contributions in tackling the long-lasting challenge of developing curse-of-dimensionality-free solutions for general optimal control problems in nonlinear systems—an open problem of central importance in control theory. The work introduces a novel approach eliminating the need to solve the Hamilton-Jacobi-Bellman equation, a nonlinear partial differential equation known to be exceedingly difficult or, in many cases, impossible to solve. This breakthrough has had a substantial impact, not only within the control field, but also across related areas, including networked systems, multi-agent systems, optimal estimation, artificial intelligence, game theory, and computational neuroscience. (A. Tahirovic and A. Astolfi, Linear-like Policy Iteration based Optimal Control for Continuous Time Nonlinear Systems, IEEE Transactions on Automatic Control, 2023.; A. Tahirovic and A. Astolfi, Optimal Control for Continuous-Time Nonlinear Systems Based on a Linear-Like Policy Iteration, The 55th IEEE Conference on Decision and Control, CDC 2019, December, Nice, France.)
Since 2012, Prof. Tahirovic has actively supervised and mentored over 40 master’s and PhD students. Nearly all of these projects were formulated around focused, long-term research efforts, typically extending over nine months. Around a third of master’s projects resulted in published work at prestigious venues, with students moving on to PhD or research positions at renowned international institutions. The projects encompass a wide range of topics, including the development of optimal control for nonlinear systems, multi-agent system algorithms, and motion planning for complex and challenging environments. The group’s mentorship has further supported projects that integrate machine learning and AI, motion planning, and optimal control across various fields, with special focus on applications in medicine.
Below is a list of past and current master’s and PhD students.
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Master students
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| Candidate | Thesis | Institution | Optimal Control | Motion Planning | MAS | Medicine | AI/ML | 1.
| Aida Čaršimamović | Model predictive control | BH Telekom | | | | | | 2. | Amel Selimović | Terrain Classification Techniques for Autonomous Vehicle Control on Rough Terrains | Municipality Zenica | | | | | | 3. | Aldin Bostan | Cooperative Control of Multi-agent Systems | Amazon Robotics | | | | | | 4. | Benjamin Seferagić | Model Predictive Control of Multi-agent Systems | Robert Bosch Manufacturing Solutions GmbH | | | | | | 5. | Delić Faris | Model Predictive Control of Thermal Power Plants | FUJIFILM Diosynth Biotechnologies | | | | | | 6. | Goran Huskić, PhD | Flatness-based Control | BMW | | | | | | 7. | Edin Šalaka | Stochastic Model Predictive Control | Dynatrace Linz
| | | | | | 8. | Samir Dzuzdanovic | Model Predictive Control of Nonlinear Systems | MeshMinds | | | | | | 9. | Selma Musić, PhD | Automatic Geophone Deployment Using a Humanoid Robot | TU Munich | | | | | | 10. | Naida Škaljić | Application of Advanced Control Techniques in Biological and Medical Systems | - | | | | | | 11. | Haris Čaušević | Model Predictive Control for Solar Power Plants | EP BiH | | | | | | 12. | Nadir Kapetanović, PhD | Model Predictive Control for Motion Planning | UNIZG-FER | | | | | | 13. | Faris Janjoš, PhD | Motion planning for Control - LQR-RRT | Bosch Research | | | | | | 14. | Muhamed Kurić, PhD Candidate | Fault Tolerant Control | TU Graz | | | | | | 15. | Zlatan Tucaković, PhD Candidate | Explicit Model Predictive Control based on State-dependant Riccati Equation | Austrian Institute of Technology | | | | | | 16. | Kemal Korjenić | Model Predictive Control Based on Motion Planning (RRT) | - | | | | | | 17. | Mina Ferizbegović, PhD Candidate | Motion Planning in Difficult Environments | KTH | | | | | | 18. | Nejra Pinjo | Motion Plannng in Uncertain Environments | Secondary school teacher | | | | | | 19. | Muhamed Vila, PhD | Explicit Model Predictive Control of Nonlinear Systems based on Machine Learning | Politecnico di Milano | | | | | | 20. | Belmin Puščul | Optimal Control Based on Model Predictive Control and State-dependent Riccati Equation | BBI | | | | | | 21. | Amar Čivgin | Classification of object in motion planning of autonomous systems | Canva Vienna | | | | | | 22. | Kenan Ahmić, PhD Candidate | Distributed Control of Multi-agent Systems | TU Graz | | | | | | 23. | Emir Poplata | Control of Network Systems | OSB Connagtive GmbH | | | | | | 24. | Tarik Selimović, PhD Candidate | Model
Predictive Control with a Finite Set of System States and Control
Actions for Medical Therapy: A Case Study on Automated Lipid
Concentration Management in Blood | UNIZG-FER | | | | | | 25. | Suad Krilašević, PhD | Suboptimal Control of Nonlinear Systems based on LQR-RRT motion planning | TU Delft | | | | | | 26. | Arman Hasanbegović | Analysis
of the Use of Configuration Space Sampling Algorithm (Rapidly-exploring
Random Vines - RRV) in Machine Learning Applications | Klika | | | | | | 27. | Dino Živojević | Cooperative Intelligence of Autonomous Agents | AtlantBH | | | | | | 28. | Aid Tabak | Robust Image Analysis based on Neural Networks and Motion Planning Algorithms (RRV) | FabLab | | | | | | 29. | Emina Zahirović | Using Rapidly-exploring Random Vines (RRV) in Machine Learning | Alstom Sweden | | | | | | 30. | Faris Kantić | Optimal Control of Nonlinear Systems based on Policy-iteration | Social Explorer | | | | | | 31. | Halil Lačević, PhD Candidate | Modeling of an Air Compression System for Performance Analysis and Optimization of Process Parameters | UCL | | | | | | 32. | Mirza Balta | Inverted Pendulum: Model Parameter Estimation, Optimal Control, and Friction Compensation | Bosnalijek | | | | | | 33. | Ibrahim Alispahić | Multi-agent Reinforcement Learning | Kodecta | | | | | | 34. | Azra Redžović | Optimal Estimation of Nonlinear Systems | UNSA-ETF | | | | | | 35. | Amina Pojskić | Reinforcement Learning based on Motion Planning Algorithms | UNSA-ETF | | | | | | 36. | Sabahudin Alagić
| Reinforcement Learning and Optimal Control | UNSA-ETF | | | | | |
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PhD students
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| Person | Thesis | Institution | Optimal Control | Motion Planning | MAS | Medicine | AI/ML | 37. | Mehmed Brkić | Muli-agent Reinforcement Learning for Muli-agent Coordination | UNSA-ETF | | | | | | 38. | Nadir Kapetanović | Online seabed coverage path planning for an autonomous marine vehicle based on sonar data | UNIZG-FER | | | | | | 39. | Tarik Selimović | Consensus-based Reinforcement Learning For Multi-agent Systems | UNZG-FER | | | | | | 40. | Anel Tahirbegović | Transfer Learning in Multi-agent Reinforcement Learning | UNZG-FER | | | | | |
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