Networked Systems Control

Safe Control of Multi-Agent Systems Using Control Barrier/Lyapunov Functions

Introduction: Safety has become an important issue for the control of multi-agent systems, especially those with complex dynamic models or in complicated environments. For example, search and rescue have to deal with navigation tasks in rugged terrains with unpredictable obstacles. Also, the coverage control of multiple robots has to achieve effective coverage without leaving the assigned area. The predominant approach follows a mapping, planning, and tracking control decoupled paradigm that forgoes theoretical guarantees for efficient practical applications. Each layer in this decoupled paradigm is designed to assume perfect execution of the connected layers. However, the actual performance of each layer in real-world environments often deviates from desirable expectations, which causes cascading errors in the classical map-plan-track architecture. These cascading errors and the associated influence are hard to theoretically characterize and thus further be addressed. For example, the tracking controller is designed assuming an available dynamically feasible and safe reference trajectory, which might be unavailable due to the infeasibility of the numerical optimization planning algorithm; Besides, the gap between the actual execution trajectory and the desired trajectory, caused by the inefficiency of the tracking controller, might result in unsafe issues even though a safe reference trajectory is provided by the planning layer. Besides, the decoupled paradigm poses high requirements of hardware for efficient execution of each module, which is unavailable for low-cost platforms. Control barrier functions (CBF) and control Lyapunov functions (CLF) provide powerful tools for dynamic systems to incorporate safety constraints such that safe control can be achieved with closed-form solutions. In this research, we use CBFs and CLFs to facilitate hard safe constraints to the coordination control of multi-agent systems with complex dynamics or complicated tasks. The CBFs and CLFs are determined according to individual cases with the feasibility, invariance, and stability proved. Through this research, we are dedicated to developing novel safe-critic control approaches for practical multi-agent systems.

Period: 2019.10 - 2021.09

Publications:

• Q. Liu+, Z. Zhang*+, N. K. Le+, J. Qin, F. Liu, and S. Hirche, "Distributed Coverage Control of Constrained Constant-Speed Unicycle Multi-Agent Systems". [arXiv]

• C. Li, Z. Zhang*, A. Nesrin, Q. Liu, F. Liu, M. Buss, "Instantaneous local control barrier function: An online learning approach for collision avoidance" (Accepted). [arXiv]

Robust Control and Filtering of Social Networks and Epidemic Dynamics

Introduction: Social networks constituted by social agents and their social relations are ubiquitous in our daily lives. Dynamic processes over social networks, which are highly related to our social activities and decision-making, are prominent research topics in both theory and practice. Analogous to the information diffusion process that spreads in social networks, networked systems are also introduced to analyze and synthesize epidemic processes, which is a serious topic receiving increasing attention. For example, the coronavirus disease 2019 (COVID-19) has taken tens of millions of lives all over the world and has significantly changed the pattern of human lives.  Existence of the endemic and disease-free equilibria is thoroughly studied as well as their stability conditions. Based on careful inspection of the properties of social networks, the next important steps are proposing effective control and filtering approaches to guarantee the achievement of the desired performance of socialization or epidemic elimination. Note that control and filtering themselves for such networked systems are nontrivial due to their complex dynamics. In this research, we focus on the control and filtering of networked systems with deterministic disturbances and stochastic noise. We propose novel control and filtering approaches that fully incorporate the nonlinear dynamic models of the networked systems to achieve optimal control and filtering specifications. Beyond this, we are also interested in investigating data-driven and adaptive controllers for epidemic networks with unknown knowledge about system dynamics or without persistent excitation conditions. Based on this, we aim at a novel optimal control framework that is promising to solve the open problem of optimal control for information epidemics. The objectives of this research are elaborated as follows.

• Proposing optimal robust control controller for networked epidemic systems with deterministic uncertainties.

• Proposing optimal filtering and control approaches for networked epidemic systems with stochastic uncertainties.

• Proposing data-driven and adaptive observers for networked systems with deterministic uncertainties.

The outputs of this research are expected to provide novel approaches to the studies in social networks, especially in epistemic prevention and control.

Period: 2017.10 - 2020.09

Publications:

• T. Liu, Z. Zhang, F. Liu*, M. Buss, "Adaptive Observer for a Class of Systems with Switched Unknown Parameters Using DREM", in IEEE Transactions on Automatic Control, (Early Access), doi: 10.1109/TAC.2023.3309228. [IEEEXplore ][arXiv]

• Z. Zhang, F. Liu*, T. Liu, J. Qiu, and M. Buss, "A Persistent-Excitation-Free Method for System Disturbance Estimation Using Concurrent Learning", in IEEE Transactions on Circuits and Systems I: Regular Papers,  vol. 70, no. 8, pp. 3305-3315, Aug. 2023, doi: 10.1109/TCSI.2023.3274558. [IEEEXplore][arXiv]

• F. Liu, Z. Zhang*, and M. Buss. "Optimal filtering and control of network information epidemics", in at - Automatisierungstechnik, vol. 69, no. 2, pp. 122-130, 2021, doi: 10.1515/auto-2020-0096. [De Gruyter]

• F. Liu*, Z. Zhang, and Martin Buss. "Robust optimal control of deterministic information epidemics with noisy transition rates." in Physica A: Statistical Mechanics and its Applications, vol. 517, pp. 577-587, Mar. 2019, doi: 10.1016/j.physa.2018.11.025. [ScienceDirect]