Distributed optimization for multi-agent systems with communication delays and external disturbances under a directed network

This article studies the distributed optimization problem for multi-agent systems with communication delays and external disturbances in a directed network. Firstly, a distributed optimization algorithm is proposed based on the internal model principle in which the internal model term can effectively compensate for external environmental disturbances. Secondly, the relationship between the optimal solution and the equilibrium point of the system is discussed through the properties of the Laplacian matrix and graph theory. Some sufficient conditions are derived by using the Lyapunov–Razumikhin theory, which ensures all agents asymptotically reach the optimal value of the distributed optimization problem. Moreover, an aperiodic sampled-data control protocol is proposed, which can be well transformed into the proposed time-varying delay protocol and analyzed by using the Lyapunov–Razumikhin theory. Finally, an example is given to verify the effectiveness of the results

Bipartite Consensus for a Class of Nonlinear Multi-agent Systems Under Switching Topologies:A Disturbance Observer-Based Approach

This paper considers the leader-following bipartite consensus for a class of nonlinear multi-agent systems (MASs) subject to exogenous disturbances under directed fixed and switching topologies, respectively. Firstly, two new output feedback control protocols involving signs of link weights are introduced based on relative output measurements of neighboring agents. In order to estimate the disturbances produced by an exogenous system, a disturbance observer-based approach is developed. Then, sufficient conditions for leader-following bipartite consensus with directed fixed topologies are derived. Furthermore, by assuming that each switching topology contains a directed spanning tree, it is proved that the leader-following bipartite consensus can be realized with the designed output feedback control protocol if the dwell time is larger than a non-negative threshold. Finally, numerical simulations inspired by a real-world DC motors are provided to illustrate the effectiveness of the proposed controllers

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

Fast Convergence in Consensus Control of Leader-Follower Multi-Agent Systems

In this thesis, different distributed consensus control strategies are introduced for a multi-agent network with a leader-follower structure. The proposed strategies are based on the nearest neighbor rule, and are shown to reach consensus faster than conventional methods. Matrix equations are given to obtain equilibrium state of the network based on which the average-based control input is defined accordingly. Two network control rules are subsequently developed, where in one of them the control input is only applied to the leader, and in the other one it is applied to the leader and its neighbors. The results are then extended to the case of a time-varying network with switching topology and a relatively large number of agents. The convergence performance under the proposed strategies in the case of a time-invariant network with fixed topology is evaluated based on the location of the dominant eigenvalue of the closed-loop system. For the case of a time-varying network with switching topology, on the other hand, the state transition matrix of the system is investigated to analyze the stability of the proposed strategies. Finally, the input saturation in agents' dynamics is considered and the stability of the network under the proposed methods in the presence of saturation is studied

Synchronous MDADT-Based Fuzzy Adaptive Tracking Control for Switched Multiagent Systems via Modified Self-Triggered Mechanism

In this paper, a self-triggered fuzzy adaptive switched control strategy is proposed to address the synchronous tracking issue in switched stochastic multiagent systems (MASs) based on mode-dependent average dwell-time (MDADT) method. Firstly, a synchronous slow switching mechanism is considered in switched stochastic MASs and realized through a class of designed switching signals under MDADT property. By utilizing the information of both specific agents under switching dynamics and observers with switching features, the synchronous switching signals are designed, which reduces the design complexity. Then, a switched state observer via a switching-related output mask is proposed. The information of agents and their preserved neighbors is utilized to construct the observer and the observation performance of states is improved. Moreover, a modified self- triggered mechanism is designed to improve control performance via proposing auxiliary function. Finally, by analysing the re- lationship between the synchronous switching problem and the different switching features of the followers, the synchronous slow switching mechanism based on MDADT is obtained. Meanwhile, the designed self-triggered controller can guarantee that all signals of the closed-loop system are ultimately bounded under the switching signals. The effectiveness of the designed control method can be verified by some simulation results

Advancements in Adversarially-Resilient Consensus and Safety-Critical Control for Multi-Agent Networks

The capabilities of and demand for complex autonomous multi-agent systems, including networks of unmanned aerial vehicles and mobile robots, are rapidly increasing in both research and industry settings. As the size and complexity of these systems increase, dealing with faults and failures becomes a crucial element that must be accounted for when performing control design. In addition, the last decade has witnessed an ever-accelerating proliferation of adversarial attacks on cyber-physical systems across the globe. In response to these challenges, recent years have seen an increased focus on resilience of multi-agent systems to faults and adversarial attacks. Broadly speaking, resilience refers to the ability of a system to accomplish control or performance objectives despite the presence of faults or attacks. Ensuring the resilience of cyber-physical systems is an interdisciplinary endeavor that can be tackled using a variety of methodologies. This dissertation approaches the resilience of such systems from a control-theoretic viewpoint and presents several novel advancements in resilient control methodologies. First, advancements in resilient consensus techniques are presented that allow normally-behaving agents to achieve state agreement in the presence of adversarial misinformation. Second, graph theoretic tools for constructing and analyzing the resilience of multi-agent networks are derived. Third, a method for resilient broadcasting vector-valued information from a set of leaders to a set of followers in the presence of adversarial misinformation is presented, and these results are applied to the problem of propagating entire knowledge of time-varying Bezier-curve-based trajectories from leaders to followers. Finally, novel results are presented for guaranteeing safety preservation of heterogeneous control-affine multi-agent systems with sampled-data dynamics in the presence of adversarial agents.PHDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168102/1/usevitch_1.pd

Event-triggered distributed optimization of multi-agent systems with time delay

In this article, the distributed optimization based on multi-agent systems was studied, where the global optimization objective of the optimization problem is a convex combination of local objective functions. In order to avoid continuous communication among neighboring agents, an event-triggering algorithm was proposed. Time delay was also considered in the designed algorithm. The triggering time of each agent was determined by the state measurement error, the state of its neighbors at the latest triggering instant and the exponential decay threshold. Some sufficient conditions for optimal consistency were obtained. In addition, Zeno-behavior in triggering time sequence was eliminated. Finally, a numerical simulation was given to prove the effectiveness of the proposed algorithm