Network Control Problem Research Articles

Joint optimization of communication and mission performance for multi-UAV collaboration network: A multi-agent reinforcement learning method

In emergency rescue, target search and other mission scenarios with Unmanned Aerial Vehicles (UAVs), the Relay UAVs (RUs) and Mission UAVs (MUs) can collaborate to accomplish tasks in unknown environments. In this paper, we investigate the problem of trajectory planning and power control for the MU and RU collaboration. Firstly, considering the characteristics of multi-hop data transmission between the MU and Ground Control Station, a multi-UAV collaborative coverage model is designed. Meanwhile, a UAV control algorithm named MUTTO is proposed based on multi-agent reinforcement learning. In order to solve the problem of the unknown information about the number and locations of targets, the geographic coverage rate is used to replace the target coverage rate for decision making. Then, the reward functions of two types of UAVs are designed separately for the purpose of better cooperation. By simultaneously planning the trajectory and transmission power of the RU and MU, the mission target coverage rate and network transmission rate are maximized while the energy consumption of the UAV is minimized. Finally, numerical simulations results show that MUTTO can solve the UAV network control problem in an efficient way and has better performance than the benchmark method.

Output feedback adaptive neural network control for uncertain nonsmooth systems with application

The output feedback adaptive neural network control problem is addressed for uncertain nonsmooth systems with network communication constraints. First, a funnel function is provided to ensure that the tracking error restrains the pre-specified performance and improves the transient and steady-state performance simultaneously. Second, an event-triggered controller including the control input, a fixed threshold and the tracking error is designed to alleviate the communication burden. It is rigorously mathematically proved that there exists a positive lower bound to avoid the Zeno behavior. With the designed observer, the control algorithm assures that all the signals are semi-globally uniformly ultimately bounded. Finally, the validity of the proposed scheme is demonstrated through an application example.

A quantization-coding scheme with variable data rates for cyber-physical systems under DoS attacks

This paper considers Cyber-Physical Systems (CPSs) in the presence of Denial-of-Service (DoS) attacks, and investigates the networked control problem under data rate limitations. We develop a framework for CPSs in the presence of DoS attacks, and present a state-estimate control scheme for CPSs under DoS attacks. For the different communication status, the information on the plant state is transmitted to the controller by employing a different data rate in order to mitigate the influence of DoS attacks. Sufficient conditions on the data rate for mean square observability and stabilization of CPSs are derived by applying information theoretic tools of source coding. Our result is less conservative than some existing results. An illustrative example is given to demonstrate the effectiveness of the proposed quantization-coding scheme.

Resilient event-triggering adaptive neural network control for networked systems under mixed cyber attacks

This paper addresses the resilient event-triggering adaptive neural network (NN) control problem for networked control systems under mixed cyber attacks. Compared with the conventional event-triggered mechanism (ETM) with constant threshold, a novel resilient ETM is designed to withstand the affect of denial-of-service attacks and conserve communication resources. Different from the energy-bounded deception attacks, an unknown state-dependent nonlinear attack signal is considered in this work. To identify the deception attack, the NN technique is utilized to approximate the unknown attack signal. Subsequently, an adaptive controller is established to compensate for the malicious affects of deception attacks on the system. Furthermore, sufficient conditions for the boundedness of the system are derived via applying the Lyapunov functional, and a co-design strategy for control gain and event-triggering parameter is provided. Finally, the feasibility of the proposed approach is validated through a robot manipulator system.

Neural networks-based adaptive command filter control for nonlinear systems with unknown backlash-like hysteresis and its application to single link robot manipulator

<abstract><p>In this paper, an adaptive neural network control problem for nonstrict-feedback nonlinear systems with an unknown backlash-like hysteresis and bounded disturbance was presented. Radial basis function neural networks (RBFNN) were used to approximate the unknown functions and the problem of the explosion of complexity problem was handled by utilizing the command filter method. Furthermore, the influence of an unknown backlash-like hysteresis input was addressed by approximating an intermediate variable. Based on the backstepping method and the command filter technique, an adaptive neural network controller was designed via the approximation abilities of RBFNN. With the help of the Lyapunov stability theory, the proposed controller ensures that all of the signals in closed-loop systems are bounded and that the tracking error fluctuates close to the origin within a bounded area. Finally, a real-world example based on the single-link manipulator was shown to demonstrate the viability of the presented approach.</p></abstract>

Partially observed optimal control of local and remote controllers

AbstractThe article investigates a networked optimal control problem with one remote controller and local controllers, where each local controller partially observes the local state and sends the observed information to remote controller through an unreliable channel and yet the channels from remote controller to local controllers are perfect. The objective of all the controllers is to cooperate to minimize a common cost function. In order to overcome the difficulties caused by partial observation of state, sufficient statistic of historical data is firstly investigated for a nonlinear non‐Gaussian partial observation model, which takes the same role as that of state in the full observation model. Then, a dynamic programming framework is given for solving the optimal control problem based on the sufficient statistic and common information approach. Furthermore, the explicit closed‐form solution is obtained for a linear‐quadratic‐Gaussian optimal control problem. Finally, a numerical example is given to show the effectiveness of the obtained results of this article.

OSWireless: Hiding specification complexity for zero-touch software-defined wireless networks

In the current practice of wireless engineering, to optimize wireless networks engineers usually need to grapple simultaneously with network modeling, algorithm and protocol design as well as their implementation on distributed edge nodes. This process is tedious and error prone. In this article we attempt to address this challenge by designing OSWireless, a new control plane for optimizing software-defined wireless networks. At the core of OSWireless is the virtualization of four control plane functionalities, including intent, mathematical, algorithmic and forwarding specifications, and then provide them as a service to network engineers. To this end, we design two new subplanes for the control plane: Wireless Network Abstraction Specification (WiNAS) Subplane and Optimization-as-a-Service (OaaS) Subplane. The former converts intent specifications defined using high-level Application Programming Interfaces (APIs) to the corresponding mathematical specifications, and the latter generates automatically operational (possibly distributed) algorithmic specifications. We prototype OSWireless and deploy it over NeXT, a newly developed software-defined experimentation testbed, and showcase the automated control program generation capability of OSWireless and the optimality of the resulting programs considering a variety of network control problems. We further test the applicability of OSWireless on UBSim, a newly-developed Python based simulator for integrated aerial-ground networks, considering location optimization of mobile nodes as an example. Through developing OSWireless, we hope to accelerate research towards future zero-touch software-defined wireless networks with reduced management complexity.

Application of linear ordinary differential equations to the stability control of long time lag networks

Abstract Optimal control based on the exact synchronization of linear ordinary differential equations can provide conditions for the existence of optimal control of long-time lag network stability. In this paper, we first generalize network control systems, time lag systems, and modern control system stability theory to discuss long-time lag network stability analysis and control problems. The numerical solution method for solving ordinary differential equations with boundary conditions is proposed by combining the numerical solution method for initial value problems of differential equations and the iterative method for solving nonlinear equations. Finally, the fixed-time synchronization problem of complex networks with time-varying time lags under periodic intermittent control is studied. Two intermittent control strategies are designed based on the linear ordinary differential equation algorithm, and the convergence analysis of the synchronization error and the synchronization criterion are given. Numerical values show that the synchronization error converges to zero (< 1.0e − 5) in 0.32s, while the convergence times are 0.55s and 0.90s. The fixed times of the three methods are calculated to be T * = 0.52, T 1 = 0.71 and T 2 = 1.32, respectively, and the synchronization error system converges faster under the method in the paper. The numerical simulation results verify the effectiveness of linear ordinary differential equations in controlling the stability of long-time lag networks.

Output-Feedback Adaptive Neural Network Control for Uncertain Nonsmooth Nonlinear Systems With Input Deadzone and Saturation.

Nonsmooth nonlinear systems can model many practical processes with discontinuous property and are difficult to be stabilized by classical control methods like smooth nonlinear systems. This article considers the output-feedback adaptive neural network (NN) control problem for nonsmooth nonlinear systems with input deadzone and saturation. First, the nonsmooth input deadzone and saturation is converted to a smooth function of affine form with bounded estimation error by means of the mean-value theorem. Second, with the help of approximation theorem and Filippov's differential inclusion theory, the given nonsmooth system is converted to an equivalent smooth system model. Then, by introducing a proper logarithmic barrier Lyapunov function (BLF), an output-feedback adaptive NN strategy is set up by constructing an appropriate observer and adopting the adaptive backstepping technique. A new stability criterion is established to guarantee that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB). Finally, comparative simulations through Chua's oscillator are offered to verify the effectiveness of the proposed control algorithm.

Adaptive finite-time neural network control for nonlinear stochastic systems with state constraints

This paper focuses on the adaptive finite-time neural network control problem for nonlinear stochastic systems with full state constraints. Adaptive controller and adaptive law are designed by backstepping design with log-type barrier Lyapunov function. Radial basis function neural networks are employed to approximate unknown system parameters. It is proved that the tracking error can achieve finite-time convergence to a small region of the origin in probability and the state constraints are confirmed in probability. Different from deterministic nonlinear systems, here the stochastic system is affected by two random terms including continuous Brownian motion and discontinuous Poisson jump process. Therefore, it will bring difficulties to the controller design and the estimations of unknown parameters. A simulation example is given to illustrate the effectiveness of the designed control method.

Event-Based Fuzzy Adaptive Consensus Tracking for Stochastic High-Order Nonlinear Multiagent Networks With Specified-Time Convergence

In this paper, a specified-time event-triggered fuzzy adaptive control algorithm is developed to solve the consensus tracking control problem for stochastic high-order nonlinear multi-agent networks, which is intrinsically challenging due to the existence of stochasticity and high-order (positive odd integers greater than one) terms. More precisely, a novel specified-time performance function (STPF) is incorporated into time-varying high-order tan-type barrier Lyapunov function to guarantee that the tracking errors remain under time-varying constraints within specified time. Combining fuzzy logic systems with the adding on power integrator technique, an adaptive approximation policy is introduced to handle the system uncertainties. Moreover, a new switching threshold event-triggered mechanism is devised to determine the control signals updating instants, which reduces the transmission and computation burden, while resizing the triggering threshold in real-time. The Zeno phenomenon is excluded by guaranteeing that the triggering intervals is lower bounded by a positive constant. Two simulation examples are provided to demonstrate the effectiveness of the designed algorithm.

Adaptive event-triggered synchronization of neural networks under stochastic cyber-attacks with application to Chua’s circuit

This paper focuses on the synchronization control problem for neural networks (NNs) subject to stochastic cyber-attacks. Firstly, an adaptive event-triggered scheme (AETS) is adopted to improve the utilization rate of network resources, and an output feedback controller is constructed for improving the performance of the system subject to the conventional deception attack and accumulated dynamic cyber-attack. Secondly, the synchronization problem of master–slave NNs is transformed into the stability analysis problem of the synchronization error system. Thirdly, by constructing a customized Lyapunov–Krasovskii functional (LKF), the adaptive event-triggered output feedback controller is designed to ensure the synchronization error system is asymptotically stable with a given H∞ performance index. Lastly, in the simulation part, two examples, including Chua’s circuit, illustrate the feasibility and universality of the related technologies in this paper.

Network optimality conditions

Abstract Optimality conditions for optimal control problems arising in network modeling are discussed. We confine ourselves to the steady state network models. Therefore, we consider only control systems described by ordinary differential equations. First, we derive optimality conditions for the nonlinear problem for a single beam. These conditions are formulated in terms of the local Pontryagin maximum principle and the matrix Riccati equation. Then, the optimality conditions for the control problem for networks posed on an arbitrary planar graph are discussed. This problem has a set of independent variables x i varying within their intervals [0, l i], associated with the corresponding beams at network edges. The lengths l i of intervals are not specified and must be determined. So, the optimization problem is non-standard, it is a combination of control and design of networks. However, using a linear change of the independent variables, it can be reduced to a standard one, and we show this. Two simple numerical examples for the single-beam problem are considered.

Stability analysis based on a control adjuster for switched neural networks by trajectory similarity

This paper focuses on a class of exponential stability control problem for the stochastic neural networks (SNNs) with time‐varying delays and Markov jump (TVDMJ). As a prerequisite to the main theorem, the existence and uniqueness of the solution for the main system are proved via contraction map principle. For the stability control of the system, we design a controller which can be adjusted the parameters to make the system stabilization. We use this controller to actively control the stability behavior of the system, rather than the papers by setting the appropriate parameters to make the system achieve a stable state. Based on the intermittent control, we design a novel trajectory similarity process control and obtain exponential stability results, which are less conservative than the existing theoretical results. Two examples based on some numerical calculation and simulation diagrams are presented to validate the effectiveness for the utilized techniques.

AI-Enabled Experience-Driven Networking: Vision, State-of-the-Art and Future Directions

Modern networks have become immensely complicated, while future networks are expected to be more highly dynamic and sophisticated. In such a complex network environment, it is challenging to design effective control schemes to allocate network resources and manage network systems. Recently, Artificial Intelligence (AI) has made tremendous successes and breakthroughs. Particularly, as a branch of AI technology, the model-free experience-based machine learning (ML) technology has attracted widespread attention in the field of Network Control Problems (NCPs). Motivated by recent breakthroughs in AI technology, we share our vision of deploying ML technology to the field of solving NCPs to achieve experiencedriven networking. Compared with the domain knowledge-based heuristic algorithms and fixed policies, which face mounting challenges in achieving desirable performance in highly dynamic and complicated networks, the ML-based methods can accumulate experiences by constantly collecting system feedback, and finally learn desirable/optimal control policies. In this paper, we first summarize the differences between the traditional solutions and the ML-based methods and highlight the advantages of experience-driven networking with ML. Then, we introduce several state-of-the-art AI-enabled experience-driven works for NCPs. In the end, we shed light on the future directions of adapting ML to more networking problems and the emerging opportunities for experience-driven networking. We hope that our work can help and encourage researchers to propose more innovative experience-driven solutions for the NCPs of modern network systems.

Intermittent Control for Synchronization of Discrete-Delayed Complex Cyber-Physical Networks under Mixed Attacks

This paper is concerned with the synchronization control problem for discrete-delayed complex cyber-physical networks under mixed attacks. To handle input delays and mixed attacks, the intermittent control mechanism is employed, which is distinctly different from the traditional control method. By utilizing the Lyapunov stability theorem, a novel synchronization control method is developed for the synchronization control of complex cyber-physical networks with mixed attacks. Then, sufficient conditions are derived to guarantee that the synchronization error dynamics are ultimately bounded. Moreover, the conditions for a special case where the absence of input delays. Subsequently, certain optimization problems are formulated with the aim to minimize the synchronization error. Finally, two numerical examples are given to verify the effectiveness and superiority of the proposed synchronization control strategy.

Ultra-Reliable Distributed Cloud Network Control With End-to-End Latency Constraints

We are entering a rapidly unfolding future driven by the delivery of real-time computation services, such as industrial automation and augmented reality, collectively referred to as augmented information (AgI) services, over highly distributed cloud/edge computing networks. The interaction intensive nature of AgI services is accelerating the need for networking solutions that provide strict latency guarantees. In contrast to most existing studies that can only characterize average delay performance, we focus on the critical goal of delivering AgI services ahead of corresponding deadlines on a per-packet basis, while minimizing overall cloud network operational cost. To this end, we design a novel queuing system able to track data packets’ lifetime and formalize the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">delay-constrained least-cost dynamic network control problem</i> . To address this challenging problem, we first study the setting with average capacity (or resource budget) constraints, for which we characterize the delay-constrained stability region and design a throughput-optimal control policy leveraging Lyapunov optimization theory on an equivalent virtual network. Guided by the same principle, we tackle the peak capacity constrained scenario by developing the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reliable cloud network control</i> (RCNC) algorithm, which employs a two-way optimization method to make actual and virtual network flow solutions converge in an iterative manner. Extensive numerical results show the superior performance of the proposed control policy compared with the state-of-the-art cloud network control algorithm, and the value of guaranteeing strict end-to-end deadlines for the delivery of next-generation AgI services.

Bi-objective design-for-control for improving the pressure management and resilience of water distribution networks

This paper investigates control and design-for-control strategies to improve the resilience of sectorized water distribution networks (WDN), while minimizing pressure induced pipe stress and leakage. Both evolutionary algorithms (EA) and gradient-based mathematical optimization approaches are investigated for the solution of the resulting large-scale non-linear (NLP) and bi-objective mixed-integer non-linear programs (BOMINLP). While EAs have been successfully applied to solve discrete network design problems for large-scale WDNs, gradient-based mathematical optimization methods are more computationally efficient when dealing with large search spaces associated with continuous variables in optimal network control problems. Considering the advantages of each method, we propose a sequential hybrid method for the optimal design-for-control of large-scale WDNs, where a refinement stage relying on gradient-based mathematical optimization is used to solve continuous optimal control problems corresponding to design solutions returned by an initial EA search. The proposed method is applied to compute the Pareto front of a bi-objective design-for-control problem for the operational network BWPnet, where we consider reopening closed connections between isolated supply areas. The results show that the considered design-for-control strategy increases the resilience of BWPnet while minimizing pressure induced leakage. Moreover, the refinement stage of the proposed hybrid method efficiently improves the coarse approximation computed by the initial EA search, returning a continuous and even Pareto front approximation.

Deep neural network based UAV deployment and dynamic power control for 6G-Envisioned intelligent warehouse logistics system

The intelligent warehouse logistics system (IWLS) is an essential component in the emerging industry 5.0. To well assist the IWLS, advanced network architecture and control policies with the adaptive capability to the variation of traffic should be specially designed. In this paper, we consider an air-and-ground cooperative wireless network that enables dynamic coverage to support the flexible scheduling of the automatic guided vehicles (AGVs) in the IWLS. Jointly considering the dynamic deployment of unmanned aerial vehicles (UAVs) as well as the adaptive power control of AGVs, we formulate a two time-scale network control problem to minimize the transmission power consumption of all AGVs under their individual rate requirement. On large time scales, we first propose a particle swarm optimization based algorithm (PSOA) to obtain the deployment position of the ABS. Then, using the results of the PSOA as training data, we design a deep neural network (DNN) framework aimed at reducing the computational time of the PSOA. On small time scales, we devise an online power control algorithm (OPCA) by using some of stochastic network optimization methods. With current channel conditions, the OPCA can generate the real-time power control policy and ensure the long-term rate requirement. Numerical simulations indicate that the DNN framework enhances the coverage performance of the network only consuming a few milliseconds of computation time. Incorporated with the OPCA, the total transmission power of the AGVs is significantly reduced.

Finite-time dissipative control for bidirectional associative memory neural networks with state-dependent switching and time-varying delays

This paper focuses on finite-time exponential dissipative analysis and control problems for bidirectional associative memory neural networks (BAMNNs) with state-dependent switching and time-varying delays. Firstly, the state-dependent switching parameters of BAMNNs are interpreted as interval parameters by the interval matrix method instead of differential inclusion theory and set-value map. Under the framework of Filippov’s solution and differential inclusions, some sufficient conditions of finite-time bounded (FTB) and finite-time exponential (Q,S,R)−γ dissipative (FTED) for BAMNNs are obtained based on Lyapunov–Krasovskii functional (LKF) and some inequality techniques. Then, the finite-time dissipative PI controllers are designed by solving linear matrix inequality (LMI). Finally, a numerical example is given to illustrate the correctness of the proposed results and the effectiveness of the designed controllers.