Quantized Communication Research Articles

Distributed finite-time sliding mode containment control for multi-unmanned surface vehicles with input and output quantization

To solve the containment control problem of multi-unmanned surface vehicles (USVs) with input and output quantization communication, we propose a distributed containment method using sliding mode control (SMC) technology. First, with only the quantized value of position information being available, a super-twisting algorithm (STA)-based observer is used to recover the unmeasured velocities of the follower USVs in a finite time. Second, based on the STA-based observer, a distributed SMC strategy is developed to guide the follower USVs closely into the convex hull formed by the leader USVs under quantized communication. Furthermore, the stability analysis is given to guarantee that sliding variables and containment position errors can converge to specified ranges related to the quantization parameters and the parameters of the STA-based observer. Finally, simulation results demonstrate the feasibility of the designed SMC approach.

Distributed output consensus of heterogeneous linear multi‐agent systems with dynamic quantization

AbstractThe output consensus problem for heterogeneous linear multi‐agent systems (MASs) without strongly connected communication graphs is investigated via quantization communication. In this work, we propose an efficient distributed control law composed of distributed quantized observers and compensators to accomplish this task. Particularly, we first employ a dynamic encoding–decoding scheme to design the distributed quantized observer, which is able to accurately estimate the state of the leader for MASs. Then, an appropriate compensator is constructed to show that asymptotic output consensus can be achieved with 5‐level quantizer, even if neither global knowledge of communication graphs nor the information about initial states of all agents is required. Finally, a numerical example is used to validate the effectiveness of our distributed control law for MASs.

Observer‐based fixed‐time task‐space cooperative tracking control of networked Lagrangian agents with quantized communication

AbstractIn this article, the task‐space cooperative tracking control (TSCTC) problem of networked Lagrangian agents (NLAs) with quantized communication is investigated in fixed time, where every Lagrangian agent suffers from imprecise dynamics and external disturbances. A hierarchical observer‐based fixed‐time control (HOBFTC) algorithm is proposed, where there are four parts: a prescribed‐time input observer is proposed to recover the unknown input of the leader, a distributed estimator control is designed to force the estimated values to converge the states (position and velocity) of the leader, a fixed‐time disturbance observer is presented to recover the total disturbances of NLAs, and a nonsingular fixed‐time local control is given to guarantee that the task‐space cooperative tracking can be achieved. The salient features are twofold: (1) by employing the hierarchical control strategy, the proposed control algorithm is clearly structured to solve the complex cooperative tracking problem with multiple constraints including quantized communication, imprecise dynamics and external disturbances. (2) the convergence time of the proposed control algorithm can be set freely by selecting some parameters, which are independent of the initial conditions. Some sufficient conditions are obtained and some simulation examples are given to validate the effectiveness of the proposed algorithm.

Adaptive fault-tolerant control for a class of nonlinear multi-agent systems with multiple unknown time-varying control directions

In this paper, we investigate the consensus tracking control for a class of heterogeneous multi-agent systems with multiple unknown time-varying control directions and unknown direction actuator faults. Different from existing work, the directions of the multiple time-varying control coefficients are subject to fault-induced sign-switching. To address this challenge, a series of high-order Lyapunov functions and differentiable functions are introduced to avoid non-integrable terms. Then, a novel contradiction statement and some Nussbaum functions are used to handle the summation of multiple unknown control coefficients with time-varying amplitudes and directions. Meanwhile, a novel distributed observer with quantized communication is introduced to track a reference trajectory with unknown dynamics. It is shown that all closed-loop signals are globally uniformly bounded and the tracking errors converge to residual sets that can be made arbitrarily small. Simulation results illustrate the effectiveness of the proposed scheme.

Distributed Dynamic Event-Triggered Control for Multi-Agent Systems With Quantization Communication

Distributed Dynamic Event-Triggered Control for Multi-Agent Systems With Quantization Communication

Differentially Private Consensus for Second-Order Multiagent Systems With Quantized Communication.

This article considers the differentially private consensus problem of discrete-time second-order multiagent systems with partially measurable states and limited communication channel capacity, where only the integer-value information of agents can be transmitted. To reduce the potential risk of state information disclosure in digital communication, a differentially private consensus algorithm via dynamic encoding-decoding is proposed for the second-order multiagent system to make agents achieve mean-square consensus by transmitting quantized integer values with privacy protection. To deal with the uncertainty of the quantizer saturation, the statistical analysis is given for the boundedness of the input of quantizers. It is shown that the expectation of the minimum memory capacity of quantizers is 2 bits. Finally, some simulation results are given to visualize our conclusions.

Adaptive Attitude Control for QUAVs Using Event-Triggered Control and Optimized Quantized Communication with Prescribed Performance

This paper proposes an appointed-time prescribed performance adaptive attitude controller with an event-triggered (ET) mechanism and optimized quantized communication for quadrotor unmanned aerial vehicles (QUAVs) in an uncertain environment. An appointed-time prescribed performance function is first designed to improve the convergence performance of attitude tracking errors considering different convergence time requirements and the convergence rate. In addition, to reduce the communication burden, a switching threshold ET mechanism and an optimized hybrid quantizer are adopted by using the K-means method. The ET mechanism not only involves the fixed threshold but also refers to the change rate of calculated control law flexibly and predictively. The optimized hybrid quantizer creatively classifies signals by their value and maximizes the advantage of each quantization method to reduce excessive quantization errors. It is shown that the proposed control scheme ensures that the closed-loop signals are uniformly ultimately bounded according to the Lyapunov method. Finally, the simulation experiments are implemented on a QUAV to demonstrate the effectiveness of the presented control scheme.

Distributed simultaneous state-input estimation over sensor networks under quantized communication

This paper is concerned with the distributed state estimation problem over sensor networks with a careful eye towards unknown inputs and quantized communication. Based on singular value decomposition, a unified estimator is developed to simultaneously estimate system states and unknown inputs, in which the estimator gain is determined by minimizing an upper bound on the updated error covariance. Then, a novel distributed state estimator is constructed by enforcing that each node uniformly quantizes the local estimates and the upper bounds on local error covariances before transmission. Furthermore, it is proved that the fused estimation error in each node is uniformly bounded in mean square. Finally, an illustrative example is provided to show the practical effectiveness of the proposed techniques.

Distributed Nash Equilibrium Seeking Strategies Under Quantized Communication

This paper is concerned with distributed Nash equilibrium seeking strategies under quantized communication. In the proposed seeking strategy, a projection operator is synthesized with a gradient search method to achieve the optimization of players' objective functions while restricting their actions within required non-empty, convex and compact domains. In addition, a leader-following consensus protocol, in which quantized information flows are utilized, is employed for information sharing among players. More specifically, logarithmic quantizers and uniform quantizers are investigated under both undirected and connected communication graphs and strongly connected digraphs, respectively. Through Lyapunov stability analysis, it is shown that players' actions can be steered to a neighborhood of the Nash equilibrium with logarithmic and uniform quantizers, and the quantified convergence error depends on the parameter of the quantizer for both undirected and directed cases. A numerical example is given to verify the theoretical results.

Resilient Discrete-Time Quantization Communication for Distributed Secondary Control of AC Microgrids Under DoS Attacks

Resilient Discrete-Time Quantization Communication for Distributed Secondary Control of AC Microgrids Under DoS Attacks

Quantized Primal-Dual Algorithms for Network Optimization With Linear Convergence

This note investigates a network optimization problem in which a group of agents cooperate to minimize a global function under the practical constraint of finite bandwidth communication. We propose an adaptive encoding-decoding scheme to handle the quantization communication between agents. Based on this scheme, we develop a continuous-time quantized distributed primal-dual (QDPD) algorithm for the network optimization problem. Our algorithm achieves linear convergence to an exact optimal solution. Furthermore, we obtain the relationship between the communication bandwidth and the convergence rate. Finally, we use a distributed logistic regression problem to illustrate the effectiveness of our methods.

Distributed Economic Dispatch Algorithm With Quantized Communication Mechanism

Distributed Economic Dispatch Algorithm With Quantized Communication Mechanism

The bottleneck and ceiling effects in quantized tracking control of heterogeneous multi-agent systems under DoS attacks

In this paper, we investigate tracking control of heterogeneous multi-agent systems under Denial-of-Service (DoS) attacks and state quantization. Dynamic quantized mechanisms are designed for inter-follower communication and leader–follower communication. Zooming-in and out factors, and data rates of both mechanisms for preventing quantizer saturation are provided. Our results show that by tuning the inter-follower quantized controller, one cannot improve the resilience beyond a level determined by the data rate of leader–follower quantized communication, i.e., the ceiling effect. Otherwise, overflow of followers’ state quantizer can occur. On the other hand, if one selects a “large” data rate for leader–follower quantized communication, then the inter-follower quantized communication determines the resilience, and further increasing the data rate for leader–follower quantized communication cannot improve the resilience, i.e., the bottleneck effect. Simulation examples are provided to justify the results of our paper.

Finite-Level Quantized Min-Consensus Control Based on Encoding-Decoding.

This article studies the min-consensus control of continuous-time real-valued multiagent systems, with sampled information, quantized communication, and switching topologies. Due to the limited bandwidth of the digital communication network, only finite-bit binary symbolic sequence can be exchanged among the agents. In order to realize the min-consensus control with quantized communication and limited bandwidth, a novel finite-level biased quantizer and a nonstrict decreasing scaling function are designed, and correspondingly a set of switching encoders and decoders are constructed. By means of the proposed encoders and decoders, the according sampled-data min-consensus control inputs are carefully constructed, and the memory variables are introduced into the control inputs and are monotonically decreasing no matter how the communication topology is switched. The proposed encoding-decoding-based control scheme can achieve accurate min-consensus with limited bandwidth, as long as the communication graphs are jointly strongly connected. The numerical simulations show the effectiveness of the proposed control scheme.

Differentially Private Average Consensus With Logarithmic Dynamic Encoding-Decoding Scheme.

This article is concerned with the differentially private average consensus (DPAC) problem for a class of multiagent systems with quantized communication. By constructing a pair of auxiliary dynamic equations, a logarithmic dynamic encoding-decoding (LDED) scheme is developed and then utilized during the process of data transmission, thereby eliminating the effect of quantization errors on the consensus accuracy. The primary purpose of this article is to establish a unified framework that integrates the convergence analysis, the accuracy evaluation, and the privacy level for the developed DPAC algorithm under the LDED communication scheme. By means of the matrix eigenvalue analysis method, the Jury stability criterion, and the probability theory, a sufficient condition (with respect to the quantization accuracy, the coupling strength, and the communication topology) is first derived to ensure the almost sure convergence of the proposed DPAC algorithm, and the convergence accuracy and privacy level are thoroughly investigated by resorting to the Chebyshev inequality and ϵ -differential privacy index. Finally, simulation results are provided to illustrate the correctness and validity of the developed algorithm.

An event-triggered quantization communication strategy for distributed optimal resource allocation

An event-triggered quantization communication strategy for distributed optimal resource allocation

Distributed Event-Triggered Secondary Control of Microgrids With Quantization Communication

Taken communication networks with limited resources in practical microgrids into consideration, a communication scheme, combined with an event-triggered communication scheme and quantization technology, is designed in the distributed optimal secondary control for islanded AC microgrids to reduce communication loads. In addition, switching communication networks are considered in microgrids to enhance the flexibility and the extendibility. In comparison with the existing distributed secondary control methods, the proposed distributed control strategy based on the designed communication scheme not only achieves the frequency and voltage restoration and the optimal active power sharing, but also reduces both the communication frequency and the amount of transmitted data in networks. Moreover, the presented communication scheme can exclude the Zeno behavior. Simulation results are given to evaluate the effectiveness of the proposed method.

Federated Multi-Agent Deep Reinforcement Learning (Fed-MADRL) for Dynamic Spectrum Access

Dynamic spectrum access (DSA) has been introduced as a promising technology that allows a secondary system to access the licensed spectrum of the primary system to improve spectrum utilization. In this paper, we introduce Fed-MADRL by incorporating federated learning (FL) and multi-agent deep reinforcement learning (MADRL) to design a collaborative DSA strategy. Our Fed-MADRL scheme employs FL to enable multiple users to collaboratively optimize the system goal without sharing their training data. By keeping all the training data at the user end, FL improves the communication efficiency and strengthens user data privacy. To further reduce the communication overheads, each user only shares quantized information. We provide the convergence analysis to characterize the trade-off between the communication efficiency and the system performance. In particular, we show that the introduced method converges at a rate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (1/ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/4</sup> ), where <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> is the number of FL iterations. To the best of our knowledge, Fed-MADRL is the first work that utilizes FL in DSA networks under quantized communication. Performance evaluation results show that the introduced Fed-MADRL method outperforms the independent learning method and achieves comparable performance with the centralized MADRL method, which requires much higher communication overheads.

A penalty ADMM with quantized communication for distributed optimization over multi-agent systems

A penalty ADMM with quantized communication for distributed optimization over multi-agent systems

Emergent Quantized Communication

The field of emergent communication aims to understand the characteristics of communication as it emerges from artificial agents solving tasks that require information exchange. Communication with discrete messages is considered a desired characteristic, for scientific and applied reasons. However, training a multi-agent system with discrete communication is not straightforward, requiring either reinforcement learning algorithms or relaxing the discreteness requirement via a continuous approximation such as the Gumbel-softmax. Both these solutions result in poor performance compared to fully continuous communication. In this work, we propose an alternative approach to achieve discrete communication -- quantization of communicated message. Using message quantization allows us to train the model end-to-end, achieving superior performance in multiple setups. Moreover, quantization is a natural framework that runs the gamut from continuous to discrete communication. Thus, it sets the ground for a broader view of multi-agent communication in the deep learning era.