In this paper, we study the mean-square consensusability problem of discrete-time linear multiagent systems over directed networks with constant communication delay and packet dropouts. When the ...packet dropouts in different communication channels are identical, the consensusability problem is transformed into a simultaneous mean-square stabilization problem of systems with constant time delay and packet dropout. When the packet dropouts are nonidentical, we build the dynamics over edges based on the notion of compressed edge Laplacian for directed graphs to separate the packet dropouts from the network topology. Sufficient consensusability conditions are obtained for both cases in terms of the communication delay, the packet dropout rates, the communication network topology, and the agent dynamics.
This paper studies a distributed linear consensus protocol for second-order multi-agent systems under limited agent interaction ranges. In particular, two agents can interact with each other only if ...their distance is within a certain range. Under a linear consensus protocol with the relative state feedback, we derive a sufficient condition on the initial states and the interaction range to achieve the second-order consensus by using a Lyapunov functional approach. Finally, simulation examples are included to validate the theoretical result.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Communication efficiency is a major bottleneck in the applications of distributed networks. To address the problem, the problem of quantized distributed optimization has attracted a lot of attention. ...However, most of the existing quantized distributed optimization algorithms can only converge sublinearly. To achieve linear convergence, this article proposes a novel quantized distributed gradient tracking algorithm (Q-DGT) to minimize a finite sum of local objective functions over directed networks. Moreover, we explicitly derive lower bounds for the number of quantization levels, and prove that Q-DGT can converge linearly even when the exchanged variables are respectively quantized with three quantization levels. Numerical results also confirm the efficiency of the proposed algorithm.
This article studies the distributed state estimation in sensor network, where <inline-formula><tex-math notation="LaTeX">m</tex-math></inline-formula> sensors are deployed to infer the ...<inline-formula><tex-math notation="LaTeX">n</tex-math></inline-formula>-dimensional state of a linear time-invariant Gaussian system. By a lossless decomposition of the optimal steady-state Kalman filter, we show that the problem of distributed estimation can be reformulated as that of the synchronization of homogeneous linear systems. Based on such decomposition, a distributed estimator is proposed, where each sensor node runs a local filter using only its own measurement, alongside with a consensus algorithm to fuse the local estimate of every node. We prove that the average of local estimates from all sensors coincides with the optimal Kalman estimate, and under certain condition on the graph Laplacian matrix and the system matrix, the covariance of local estimation error is bounded and the asymptotic error covariance is derived. As a result, the distributed estimator is stable for each single node. We further show that the proposed algorithm has a low message complexity of <inline-formula><tex-math notation="LaTeX">\min (m,n)</tex-math></inline-formula>. Numerical examples are provided in the end to illustrate the efficiency of the proposed algorithm.
This paper considers the distributed optimization problem where each node of a peer-to-peer network minimizes a finite sum of objective functions by communicating with its neighboring nodes. In sharp ...contrast to the existing literature where the fastest distributed algorithms converge either with a global linear or a local superlinear rate, we propose a distributed adaptive Newton (DAN) algorithm with a global quadratic convergence rate. Our key idea lies in the design of a finite-time set-consensus method with Polyak’s adaptive stepsize. Moreover, we introduce a low-rank matrix approximation (LA) technique to compress the innovation of Hessian matrix so that each node only needs to transmit message of dimension O(p) (where p is the dimension of decision vectors) per iteration, which is essentially the same as that of first-order methods. Nevertheless, the resulting DAN-LA converges to an optimal solution with a global superlinear rate. Numerical experiments on logistic regression problems are conducted to validate their advantages over existing methods.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Asynchronous Networked Aggregative Games Zhu, Rongping; Zhang, Jiaqi; You, Keyou ...
Automatica (Oxford),
February 2022, 2022-02-00, Volume:
136
Journal Article
Peer reviewed
Open access
We propose a fully asynchronous networked aggregative game (Asy-NAG) where each player minimizes a local cost function that depends on its own action and the aggregate of all players’ actions. In ...sharp contrast to the existing NAGs, each player in our Asy-NAG can compute an estimate of the aggregate action at any wall-clock time by only using (possibly stale) information from neighboring players of a directed network. Such an asynchronous update does not require any coordination among players. Moreover, we design a novel distributed algorithm with an aggressive mechanism for each player to adaptively adjust its optimization stepsize per update. Particularly, the slow players in terms of updating their estimates smartly increase their stepsizes to catch up with the fast ones. Then, we adopt an augmented network approach to address the asynchronicity and the information delays between players, and rigorously show the convergence to a Nash equilibrium of the Asy-NAG via a perturbed coordinate algorithm which is also of independent interest. Finally, we evaluate the performance of the distributed algorithm through numerical simulations.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
•We propose a Wasserstein distributionally robust shortest path model to fully exploit samples.•The model can be solved via a mixed 0–1 convex problem.•The worst-case distribution is explicitly ...derived by simply perturbating each sample.•Experiments show its advantages in terms of out-of-sample performance and computational complexity.•The model is extended to solve the distributionally robust bi-criteria shortest path problem as well as minimum flow cost problems.
This paper proposes a data-driven distributionally robust shortest path (DRSP) model where the distribution of the travel time in the transportation network can only be partially observed through a finite number of samples. Specifically, we aim to find an optimal path to minimize the worst-case α-reliable mean-excess travel time (METT) over a Wasserstein ball, which is centered at the empirical distribution of the sample dataset and the ball radius quantifies the level of its confidence. In sharp contrast to the existing DRSP models, our model is equivalently reformulated as a tractable mixed 0–1 convex problem, e.g., 0–1 linear program or 0–1 second-order cone program. Moreover, we also explicitly derive the distribution achieving the worst-case METT by simply perturbing each sample. Experiments demonstrate the advantages of our DRSP model in terms of the out-of-sample performance and computational complexity. Finally, our DRSP model is easily extended to solve the distributionally robust bi-criteria shortest path problem and the minimum cost flow problem.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
This article studies the informative trajectory planning problem of an autonomous vehicle for field exploration. In contrast to existing works concerned with maximizing the amount of information ...about spatial fields, this work considers efficient exploration of spatiotemporal fields with unknown distributions and seeks minimum-time trajectories of the vehicle while respecting a cumulative information constraint. In this work, upon adopting the observability constant as an information measure for expressing the cumulative information constraint, the existence of a minimum-time trajectory is proven under mild conditions. Given the spatiotemporal nature, the problem is modeled as a Markov decision process (MDP), for which a reinforcement learning (RL) algorithm is proposed to learn a continuous planning policy. To accelerate the policy learning, we design a new reward function by leveraging field approximations, which is demonstrated to yield dense rewards. Simulations show that the learned policy can steer the vehicle to achieve an efficient exploration, and it outperforms the commonly-used coverage planning method in terms of exploration time for sufficient cumulative information.
This work studies the standoff tracking problem to drive an unmanned aerial vehicle (UAV) to slide on a desired circle over a moving target at a constant height. We propose a novel Lyapunov guidance ...vector (LGV) field with tunable convergence rates for the UAV's trajectory planning and a deep neural network (DNN)-based model predictive control (MPC) scheme to track the reference trajectory. Then, we show how to collect samples for training the DNN offline and design an integral module (IM) to refine the tracking performance of our DNN-based MPC. Moreover, the hardware-in-the-loop (HIL) simulation with a field-programmable gate array (FPGA) at 200 MHz demonstrates that our method is a valid alternative to embedded implementations of MPC for addressing complex systems and applications which is impossible for directly solving the MPC optimization problems.
PDF
