Performance-Barrier-Based Event-Triggered Control with Applications to Network Systems

This paper proposes a novel framework for resource-aware control design termed performance-barrier-based triggering. Given a feedback policy, along with a Lyapunov function certificate that guarantees its correctness, we examine the problem of designing its digital implementation through event-triggered control while ensuring a prescribed performance is met and triggers occur as sparingly as possible. Our methodology takes into account the performance residual, i.e., how well the system is doing in regards to the prescribed performance. Inspired by the notion of control barrier function, the trigger design allows the certificate to deviate from monotonically decreasing, with leeway specified as an increasing function of the performance residual, resulting in greater flexibility in prescribing update times. We study different types of performance specifications, with particular attention to quantifying the benefits of the proposed approach in the exponential case. We build on this to design intrinsically Zeno-free distributed triggers for network systems. A comparison of event-triggered approaches in a vehicle platooning problem shows how the proposed design meets the prescribed performance with a significantly lower number of controller updates.Comment: 15 pages, 2 figures, submitted to IEEE Transactions on Automatic Contro

Distributed Fault-Tolerant Consensus Tracking Control of Multi-Agent Systems under Fixed and Switching Topologies

This paper proposes a novel distributed fault-tolerant consensus tracking control design for multi-agent systems with abrupt and incipient actuator faults under fixed and switching topologies. The fault and state information of each individual agent is estimated by merging unknown input observer in the decentralized fault estimation hierarchy. Then, two kinds of distributed fault-tolerant consensus tracking control schemes with average dwelling time technique are developed to guarantee the mean-square exponential consensus convergence of multi-agent systems, respectively, on the basis of the relative neighboring output information as well as the estimated information in fault estimation. Simulation results demonstrate the effectiveness of the proposed fault-tolerant consensus tracking control algorithm

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

Fast-Convergent Dynamics for Distributed Resource Allocation Over Sparse Time-Varying Networks

In this paper, distributed dynamics are deployed to solve resource allocation over time-varying multi-agent networks. The state of each agent represents the amount of resources used/produced at that agent while the total amount of resources is fixed. The idea is to optimally allocate the resources among the group of agents by reducing the total cost functions subject to fixed amount of total resources. The information of each agent is restricted to its own state and cost function and those of its immediate neighbors. This is motivated by distributed applications such as in mobile edge-computing, economic dispatch over smart grids, and multi-agent coverage control. The non-Lipschitz dynamics proposed in this work shows fast convergence as compared to the linear and some nonlinear solutions in the literature. Further, the multi-agent network connectivity is more relaxed in this paper. To be more specific, the proposed dynamics even reaches optimal solution over time-varying disconnected undirected networks as far as the union of these networks over some bounded non-overlapping time-intervals includes a spanning-tree. The proposed convergence analysis can be applied for similar 1st-order resource allocation nonlinear dynamics. We provide simulations to verify our results

Consensus disturbance rejection for Lipschitz nonlinear multi-agent systems with input delay: a DOBC approach

In this paper, a new predictor-based consensus disturbance rejection method is proposed for high-order multi agent systems with Lipschitz nonlinearity and input delay. First, a distributed disturbance observer for consensus control is developed for each agent to estimate the disturbance under the delay constraint. Based on the conventional predictor feedback approach, a non-ideal predictor based control scheme is constructed for each agent by utilizing the estimate of the disturbance and the prediction of the relative state information. Then, rigorous analysis is carried out to ensure that the extra terms associated with disturbances and nonlinear functions are properly considered. Sufficient conditions for the consensus of the multi-agent systems with disturbance rejection are derived based on the analysis in the framework of Lyapunov-Krasovskii functionals. A simulation example is included to demonstrate the performance of the proposed control scheme. (C) 2016 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.National Natural Science Foundation of China [61673034]SCI(E)ARTICLE1,SI298-31535

Iterative learning control for multi-agent systems with impulsive consensus tracking

In this paper, we adopt D-type and PD-type learning laws with the initial state of iteration to achieve uniform tracking problem of multi-agent systems subjected to impulsive input. For the multi-agent system with impulse, we show that all agents are driven to achieve a given asymptotical consensus as the iteration number increases via the proposed learning laws if the virtual leader has a path to any follower agent. Finally, an example is illustrated to verify the effectiveness by tracking a continuous or piecewise continuous desired trajectory