Coalitional Distributed Model Predictive Control Strategies for Dynamically Coupled Systems
Financed by The Executive Agency for Higher Education, Research, Development and Innovation Funding (UEFISCDI), project number PN-III-P1-1.1-PD-2019-0757, Human Resources Programme, Postdoctoral research projects, Duration: 01.09.2019-31.08.2022
In recent years, the competing European Union market has led to increasing demands on efficiency and performance in the existing complex processes. These processes usually consist of more and more independent (from the goal point of view), but interacting (through dynamics or common resources) sub-systems (or modules), becoming more complex from a global control perspective. Such processes are commonly controlled in a distributed mode, in which each module locally implements a control policy taking into account relevant information received from the nearby sub-systems. The main goal of this project is to develop coalitional control strategies in the distributed model predictive control (DMPC) framework. The idea is to introduce a flexible control architecture, which can operate in either non-cooperative design (all modules are locally controlled), or partial cooperative scheme. In the latter, groups of modules are merged in one unit, whereas the remaining ones remain independently controlled and interconnected within the network. The interdisciplinary context within this project relies on adopting known control strategies and designing an innovative approach for dealing with dynamically coupled systems.
The main objective of this project is to develop coalitional distributed model predictive control algorithms suitable for coupled sub-systems and to test the aforementioned strategies on simulation and in a laboratory scale experimental setup.
To achieve the main target proposed for this project, a set of specific objectives suitable for solving the intermediary development tasks are defined as follows:
The project is be structured in 4 Work Packages (WPs), each containing several key tasks:
Work Package 1 (WP1): Develop a coalitional control methodology in the DMPC framework
Task 1.1. Investigate the existing procedures available for the coalitional control with respect to the selection of the agents inside a coalition and when to activate the potential coalitions.
Task 1.2. Develop a comprehensive procedure for the selection of agents that will form a coalition.
Task 1.3. Develop a complete procedure for the coalition activation at a given time (when are the agents expected to merge).
Task 1.4. Analyze the procedures for the coalition selection and activation
Deliverable 1: a report with the description of the coalition selection procedures
Deliverable 2: a report with the description of the coalition activation procedures
Milestone 1: different procedures for selecting the agents involved in a coalition
Milestone 2: different procedures for choosing when to form a coalition between the agents
Work Package 2 (WP2): Develop a coalitional DMPC algorithm for dynamically coupled sub-systems
Task 2.1. Develop a coalitional DMPC strategy suitable for dynamically coupled sub-systems.
Task 2.2. Test the developed coalitional DMPC algorithm using with different coalition selection and activation procedures designed in WP1 (Task 1.2 and Task 1.3).
Task 2.3. Performance analysis of the developed coalitional DMPC algorithm.
Task 2.4. Comparative analysis between the developed coalitional strategies using the procedures from Task 2.2.
Deliverable 3: a report with the description of the coalitional DMPC algorithm
Deliverable 4: a report with the simulation results and the performance analysis
Milestone 3: availability of a coalitional DMPC methodology with different procedures for coalition selection and activation
Work Package 3 (WP3): Simulation implementation of a vehicle platooning application and performance analysis
Task 3.1. Select the most recent models for vehicle platooning which take into account the coupling between the agents.
Task 3.2. Develop a realistic model for a vehicle platooning application suitable for coalitional DMPC strategy.
Task 3.3. Test the coalitional DMPC algorithm designed in WP2, which has the best performance according to Task 2.4.
Task 3.4. Performance analysis of the simulation results achieved in the vehicle platooning application.
Deliverable 5: a report with the description of the realistic vehicle platooning model
Deliverable 6: a report with the simulation results and the performance analysis of the coalitional DMPC strategy
Milestone 4: availability of the Matlab emulator for the coalitional DMPC algorithm
Work Package 4 (WP4): Real-time implementation and performance analysis
Task 4.1. Develop the real-time software for implementing the proposed coalitional DMPC algorithm on embedded systems.
Task 4.2. Test the developed coalitional DMPC strategy on an experimental vehicle platooning setup.
Task 4.3. Performance analysis of the real-time coalitional DMPC algorithm.
Task 4.4. Compare the experimental results of the developed coalitional DMPC strategy with respect to the simulation results.
Deliverable 7: a report with the description of the real-time software
Deliverable 8: a report with the experimental results and performance analysis
Milestone 5: availability of the real-time controller for the coalitional DMPC strategy
The most significant result obtained during this project is the development of a novel coalitional control strategy for dynamically coupled systems. Such systems are composed of various interconnected sub-systems. The proposed control methodology was tested in particular, on a vehicle platooning application, with a leader-follower, uni-directional communication configuration, in both simulation tests and real experiments.
Thus, for simulation purposes solely, the coalitional control algorithm based on distributed model predictive control (DMPC) strategy is recommended, whereas, for real-time experiments, a simplified version of the above-mentioned algorithm is proposed. The latter was formulated starting from the gain feedback control methodology and has the advantage of a law computational description, which is appropriate when the available resources are scarce (i.e., on embedded systems).
Raport Stiintific si Tehnic – Etapa 1 – 2020: RST2020
Raport Stiintific si Tehnic – Etapa 2 – 2021: RST2021
Raport Stiintific Final – 2022: RST2022
Pauca O., MaximA., and Caruntu C.F., Vehicle Trajectory Planning for Collision Avoidance with Mobile Obstacles, 26th International Conference on System Theory, Control and Computing (ICSTCC), Sinaia, Romania, 2022 – accepted.
Maxim A., Pauca O., Maestre J.M., and Caruntu C.F., Assessment of computation methods for coalitional feedback controllers, European Control Conference (ECC), London, United Kingdom, pp. 1448-1453, 2022. (IEEE Xplore)
https://ieeexplore.ieee.org/document/9838577
Pauca O., Maxim A., and Caruntu C.F., Communication topologies evaluation for a vehicle merging into a platoon on highway, 30th Mediterranean Conference on Control and Automation (MED), Athens, Greece, pp. 957-962, 2022. (IEEE Xplore)
https://ieeexplore.ieee.org/document/9837195
Maxim A., and Caruntu C.F., A Coalitional Distributed Model Predictive Control Perspective for a Cyber-Physical Multi-Agent Application, Sensors, 21 (12), pp. 4041, 2021. (IF: 3.576 – Q1)
https://www.mdpi.com/1424-8220/21/12/4041
Pauca O., Maxim A., and Caruntu C.F., Multivariable Optimisation for Waiting-Time Minimisation at Roundabout Intersections in a Cyber-Physical Framework, Sensors, 21 (12), pp. 3968, 2021. (IF: 3.576 – Q1)
https://www.mdpi.com/1424-8220/21/12/3968
Maxim A., and Copot D., Closed-loop control of anesthesia and hemodynamic system: a Model Predictive Control approach, 11th IFAC Symposium on Biological and Medical Systems (BMS), Ghent, Belgium, pp. 37-42, 2021. (ISI Proceedings)
https://www.sciencedirect.com/science/article/pii/S2405896321016311
Pauca O., Maxim A., and Caruntu C.F., Cooperative Platoons Merging for Obstacle Avoidance on Highways, 25th International Conference on System Theory, Control and Computing (ICSTCC), Iasi, Romania, pp. 25-30, 2021. (IEEE Xplore)
https://ieeexplore.ieee.org/document/9607199
Pauca O., Maxim A., and Caruntu C.F., Trajectory Planner based on Third-order Polynomials applied for Platoon Merging and Splitting, 29th Mediterranean Conference on Control and Automation (MED), Bari, Puglia, Italy, pp. 83-88, 2021. (IEEE Xplore)
https://ieeexplore.ieee.org/document/9480261
Pauca O., Maxim A., and Caruntu C.F., DMPC-based Data-packet Dropout Compensation in Vehicle Platooning Applications using V2V Communications, European Control Conference (ECC), Delft, The Netherlands, pp. 2639-2644, 2021. (ISI Proceedings)
https://ieeexplore.ieee.org/document/9654918
Maxim A., Pauca O., Caruntu C.F., and Lazar C., Distributed model predictive control algorithm with time-varying communication delays for a CACC vehicle platoon, 24th International Conference on System Theory, Control and Computing, Sinaia, Romania, pp. 775-780, 2020. (ISI Proceedings)
https://ieeexplore.ieee.org/document/9259772
Maxim A., J.M. Maestre, C.F. Caruntu, and C. Lazar, Min-max coalitional model predictive control algorithm, 22nd International Conference on Control Systems and Computer Science, Bucuresti, Romania, pp.24-29, 2019. (ISI proceedings)
https://ieeexplore.ieee.org/document/8745299
Maxim A., J.M. Maestre, C.F. Caruntu, and C. Lazar, Robust coalitional distributed model predictive control algorithm with stability via terminal constraint, 2nd IEEE Conference on Control Technology and Applications, Copenhagen, Denmark, pp. 964–969, 2018. (ISI proceedings)
https://ieeexplore.ieee.org/document/8511436
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