Bipartite containment of heterogeneous multi-agent systems under denial-of-service attacks: a historical information-based control scheme

A distributed control scheme based on historical information is designed to solve the problem of stable control of multi-agent systems under denial of service (DoS) attacks in this article. It achieves the control objective of bipartite output containment control, that is, the output states of the followers smoothly enter the target area. The control scheme updates the states of followers through historical information in the control protocol when agents are subjected to DoS attacks. A distributed state observer with a storage module is designed to efficiently estimate the state of followers and store the observed information as history information. The historical information of control protocol calls is not necessarily the real state information in the existence of DoS attacks. Consequently, a closed-loop feedback state compensator is designed. Then, the state compensator is converted from the time domain to the frequency domain for stability analysis using the Nyquist criterion. It is obtained that an upper bound on the amount of historical information can achieve the bipartite output trajectories containment of the controlled system. The output trajectories of the followers converge into two dynamic convex hulls, one of which is surrounded by multiple leaders, and the other is a convex hull with opposite signs of the leaders. Finally, a numerical simulation is used to verify the proposed control scheme, and the operability of the scheme is further demonstrated in a physical experiment

Bipartite consensus of nonlinear agents in the presence of communication noise

In this paper, a Distributed Nonlinear Dynamic Inversion (DNDI)-based consensus protocol is designed to achieve the bipartite consensus of nonlinear agents over a signed graph. DNDI inherits the advantage of nonlinear dynamic inversion theory, and the application to the bipartite problem is a new idea. Moreover, communication noise is considered to make the scenario more realistic. The convergence study provides a solid theoretical base, and a realistic simulation study shows the effectiveness of the proposed protocol.Engineering and Physical Sciences Research Council (EPSRC): EP/R009953/

Distributed L1-state-and-fault estimation for Multi-agent systems

In this paper, we propose a distributed state-and-fault estimation scheme for multi-agent systems. The proposed estimator is based on an $\ell_1$-norm optimization problem, which is inspired by sparse signal recovery in the field of compressive sampling. Two theoretical results are given to analyze the correctness of the proposed approach. First, we provide a necessary and sufficient condition such that state and fault signals are correctly estimated. The result presents a fundamental limitation of the algorithm, which shows how many faulty nodes are allowed to ensure a correct estimation. Second, we provide a sufficient condition for the estimation error of fault signals when numerical errors of solving the optimization problem are present. An illustrative example is given to validate the effectiveness of the proposed approach

A Survey on Aerial Swarm Robotics

The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional space and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of this paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas