Nonlinear and Multi-Agent Systems: Modeling, Control and Optimization


Prof. Dr. Jiangping Hu
School of Automation Engineering,
University of Electronic Science and Technology of China
Chengdu, China.


Prof Dr. Bijoy Kumar Ghosh
Department of Mathematics & Statistics,
Texas Tech University,
Lubbock, USA.

Prof. Dr. Xiaoming Hu
Optimization and Systems Theory
Royal Institute of Technology,
Stockholm, Sweden.

Dr. Volodymyr Lynnyk
Institute of Information Theory and Automation,
The Czech Academy of Sciences,
Prague, Czech Republic.

Prof. Dr. Štěpán Papáček
Institute of Information Theory and Automation,
The Czech Academy of Sciences,
Prague, Czech Republic.

Dr. Branislav Rehák
Institute of Information Theory and Automation,
The Czech Academy of Sciences,
Prague, Czech Republic.


In the past decade, extensive studies have been conducted for the so-called complex systems, multi-agent systems, and networked systems in the control community. Particularly, multi-agent systems are integrations of information and control systems with physical processes interacting with the environment. Significant progress notwithstanding, many important topics remain insufficiently explored so far. This special issue aims to bring papers focusing on modeling and control of nonlinear multi-agent systems, optimization and control of cyber-physical systems (CPSs), and cooperative control of multi-robot systems.

Much has been done on cooperative control of multi-agent systems with linear dynamics. However, as is well known, all physical systems are inherently nonlinear and at most of the time, getting the exact model of a system is impossible. This motivates a recent research interest on multi-agent systems with nonlinear dynamics. Moreover, in practical application, the graph topology is dynamical, and the communication channels are never perfect. Communication noises are inevitably to be introduced. Also, in case of sensor failures or longer distance than the sensor radius, an agent can only sense its neighbors’ information intermittently. In addition, convergence rate is also a key factor of control design and thus finite-time control is still important for multi-agent control. These issues introduce difficulties for control of the multi-agent systems.

CPS are engineered systems that are built by integrating computation and physical components. Application areas include power, agriculture, aeronautics, healthcare, manufacturing and transportation. This problem requires engineering-based solutions that can improve data collection and streaming, information networking, and data-driven decision-making. An “Internet of Things,” or a cyber-physical system that connects uniquely identifiable “Things” to the Internet, provides an ideal model of the cyber-based system. For example, “Things” of the cyber-based system can be actors, sensors, actuators and computing devices.

An important hallmark of a multi-agent team of robots is implementation of sensor guided controllers for the purpose of formation-control. The robot team is equipped with sensors either individually or in a network. The sensor network effectively interacts with the control network of the robots. In addition, the multi-agent robot team moves in a constrained state space. The robot team is able to learn to better respond to the environment, which is an example of a human-machine learning control.


  • Distributed nonlinear control
  • Multi-agent modelling and control
  • Distributed optimization and learning
  • Swarm intelligence
  • Systems biology
  • Coordination control of robots


Submission Deadline: October 31th, 2022
Authors Notification: December 15th, 2022
Revised Submission: January 31th, 2023
Final Decision Notification: March 1st, 2023
SI release: Spring 2023


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