Scalable Formal Control

Scalable Risk-Aware Formal Analysis and Control of Multi-Agent Systems

Introduction: The growing demand for automation and intelligentization of manufacturing and social services requires the application of a larger number of autonomous devices, such as robots, autonomous driving vehicles, and unmanned aerial vehicles to more complicated environments and more sophisticated tasks than before. This leads to larger challenges to the controller design of large-scale robot teams due to the difficulty in massive computation, decentralized stabilization, uncertainty quantification, risk mitigation, and fault handling. In this sense, the conventional control approaches that are sensitive to the dynamic system models are facing more and more challenges when dealing with these applications. On the one hand, the development of stochastic analysis, computational technologies, and artificial intelligence provides powerful tools to infer useful knowledge for control from data. On the other hand, the conventional control approaches based on transfer functions or Lyapunov theory cannot provide sufficient support to incorporate this knowledge. Therefore, novel control frameworks are needed to cope with the control of large-scale systems with complicated task specifications while being capable of incorporating the knowledge inferred through data-driven approaches.

Formal control is a promising control methodology that can hopefully help us achieve this goal. Compared to the conventional informal control approaches that are highly dependent on the proper selection of Lyapunov functions and control parameters, formal control methods are dedicated to proposing a set of feasible control specifications in the form of temporal logic formulas. Then, a controller is automatically solved via a synthesis process using model-checking-based or optimization-based approaches. Such control methods are referred to as formal since they are formally given by predefined specifications and can be automatically solved. Nevertheless, solving a formal controller is usually computationally expensive. This brings challenges to formal control of large-scale systems, such as networked systems, multi-agent systems, and cyber-physical systems. In this research, we are investigating novel approaches and technologies to improve the scalability of formal control methods to large-scale systems. This will be achieved by several different perspectives which include but are not limited by:

1. Splitting and decomposing large and complex temporal logic specifications into smaller formulas.

2. Proposing decentralized controllers or grouping methods to reduce the dimensionality of large systems.

3. Using AI-facilitated approaches to improve the solving speed of control synthesis.

4. Using data-driven methods to allow incremental learning of system uncertainties and policies.

Under this research topic, we are dedicated to developing a new conceptual framework of efficient and scalable formal control for multi-agent systems and cyber-physical systems. In the meantime, we are also consistently promoting the application of novel formal control approaches to practical scenarios, such as autonomous driving, industrial manufacturing, and aerial management.

Period: 2022.10 - present

Project: Symbolic Logic Framework for Situational Awareness in Mixed Autonomy (SymAware) [Details]

Publications:

• S. Qi, Z. Zhang*, S. Haesaert, and Z. Sun, "Automated Formation Control Synthesis from Temporal Logic Specifications",  62nd IEEE Conference on Decision and Control (CDC 2023), Singapore, 13-15 Dec 2023.

• Z. Zhang* and S. Haesaert, "Modularized Control Synthesis for Complex Signal Temporal Logic Specifications",   62nd IEEE Conference on Decision and Control (CDC 2023), Singapore, 13-15 Dec 2023.

• L. C. Wu, Z. Zhang*, S. Haesaert, Z. Ma, and Z. Sun, "Risk-Aware Reward Shaping of Reinforcement Learning Agents for Autonomous Driving", 49th Annual Conference of the IEEE Industrial Electronics Society (IECON 2023), Singapore, 16-19 Oct 2023. 

• N. Dang, T. Shi, Z. Zhang, W. Jin, M. Leibold, and M.Buss, "Identifying Reaction-Aware Driving Styles of Stochastic Model Predictive Controlled Vehicles by Inverse Reinforcement Learning", 26th IEEE International Conference on Intelligent Transportation Systems (ITSC 2023), Bilbao, Spain, 24-28 Sept 2023. 

• N. Dang*, T. Brüdigam, Z. Zhang, F. Liu, M. Leibold, and M. Buss, "Distributed Stochastic Model Predictive Control for a Microscopic Interactive Traffic Model", in Electronics, vol. 12, no. 6, pp.1270, Mar. 2023, doi: 10.3390/electronics12061270. [MDPI]

• Z. Zhang, Q. Liu*, M. H. Mamduhi, S. Hirche, "Average Communication Rate for Event-Triggered Stochastic Control Systems". [arXiv]