Time-varying output formation tracking of heterogeneous linear multi-agent systems with multiple leaders and switching topologies

This paper studies the time-varying output formation tracking problems for heterogeneous linear multi-agent systems with multiple leaders in the presence of switching directed topologies, where the agents can have different system dynamics and state dimensions. The outputs of followers are required to accomplish a given time-varying formation configuration and track the convex combination of leaders’ outputs simultaneously. Firstly, using the neighboring relative information, a distributed observer is constructed for each follower to estimate the convex combination of multiple leaders’ states under the influences of switching directed topologies. The convergence of the observer is proved based on the piecewise Lyapunov theory and the threshold for the average dwell time of the switching topologies is derived. Then, an output formation tracking protocol based on the distributed observer and an algorithm to determine the control parameters of the protocol are presented. Considering the features of heterogeneous dynamics, the time-varying formation tracking feasible constraints are provided, and a compensation input is applied to expand the feasible formation set. Sufficient conditions for the heterogeneous multi-agent systems with multiple leaders and switching directed topologies to achieve the desired time-varying output formation tracking under the designed protocol are proposed. Finally, simulation examples are given to validate the theoretical results.     

Distributed state estimation and fault diagnosis using reduced sensitivity to neighbor estimates with application to building control

This paper proposes solutions that reduce the inaccuracy of distributed state estimation and consequent performance deterioration of distributed model predictive control caused by faults and inaccurate models. A distributed state estimation method for large-scale systems is introduced. A local state estimation approach considers the uncertainty of neighbor estimates, which can improve the state estimation accuracy, whereas it keeps a low network communication burden. The method also incorporates the uncertainty of model parameters which improves the performance when using simplified models. The proposed method is extended with multiple models and estimates the probability of nominal and fault behavior models, which creates a distributed fault detection and diagnosis method. An example with application to the building heating control demonstrates that the proposed algorithm provides accurate state estimates to a controller and detects local or global faults while using simplified models.     

Time-varying formation tracking for high-order multi-agent systems with switching topologies and a leader of bounded unknown input

Time-varying formation tracking problems for high-order multi-agent systems with switching topologies are investigated. Different from the previous work, the states of the followers form a predefined time-varying formation while tracking the state of the leader with bounded unknown control input. Besides, the communication topology can be switching, and the dynamics of each agent can have nonlinearities. Firstly, a nonlinear time-varying formation tracking control protocol is presented which is constructed using only local neighboring information. Secondly, an algorithm with four steps is proposed to design the time-varying formation tracking protocol, where the time-varying formation tracking feasibility condition is introduced. Thirdly, by using the Lyapunov theory, the stability of the proposed algorithm is proven. It is proved that the high-order multi-agent system with switching topologies achieves the time-varying formation tracking if the feasibility condition holds and the dwell time is larger than a positive constant. Finally, a numerical example with six followers and one leader is given to demonstrate the effectiveness of the obtained results.     

Formation consensus for discrete-time heterogeneous multi-agent systems with link failures and actuator/sensor faults

In this paper, a distributed control protocol is presented for discrete-time heterogeneous multi-agent systems in order to achieve formation consensus against link failures and actuator/sensor faults under fixed and switching topologies. A model equivalent method is proposed to deal with the heterogeneous system consists of arbitrary order systems with different parameters. Based on graph theory and Lyapunov theory, stability conditions to solve formation consensus problem are developed for the underlying heterogeneous systems with communication link failures. In order to tolerate actuator/sensor faults, a distributed adaptive controller is proposed based on fault compensation. The desired control is designed by linear matrix inequality approach together with cone complementarity linearisation algorithm. After applying the new control scheme to heterogeneous systems under the directed topologies with link failures and faults, the resulting closed-loop heterogeneous system is validated to be stable. The effectiveness of the new formation consensus control strategy and its robustness are verified by simulations.     

EP-based adaptive tracker with observer and fault estimator for nonlinear time-varying sampled-data systems against actuator failures

An evolutionary programming-based adaptive observer is presented in this paper to improve the performance of state estimation of nonlinear time-varying sampled-data systems. Also, this paper presents a novel state-space adaptive tracker together with the proposed observer and estimation schemes for nonlinear time-varying sampled-data systems having actuator failures. For the class of slowly varying nonlinear time-varying systems, the proposed methodology is able to achieve the desired fault detection and performance recovery for the originally well-designed systems, as long as the controller having the high-gain property. For practical implementation, we utilize the advantages of digital redesign methodology to convert a well-designed high-gain analog controller/observer into its corresponding low-gain digital controller/observer. Illustrative examples are given to demonstrate the effectiveness of the proposed method. The developed digitally redesigned adaptive tracker with the proposed observer and estimator is suitable for implementation by using microprocessors.     

A survey on global pinning synchronization of complex networks

In practice, it is almost impossible to directly add a controller on each node in a complex dynamical network due to the high control cost and the difficulty of practical implementation, especially for large-scale networks. In order to address this issue, a pinning control strategy is introduced as a feasible alternative. The objective of this paper is first to recall some recent advancements in global pinning synchronization of complex networks with general communication topologies. A systematic review is presented thoroughly from the following aspects, including modeling, network topologies, control methodologies, theoretical analysis methods, and pinned node selection and localization schemes (pinning strategies). Fully distributed adaptive laws are proposed subsequently for the coupling strength as well as pinning control gains, and sufficient conditions are obtained to synchronize and pin a general complex network to a preassigned trajectory. Moreover, some open problems and future works in the field are also discussed.     

Distributed dynamic state estimation with parameter identification for large-scale systems

In this paper, we consider a distributed dynamic state estimation problem for time-varying systems. Based on the distributed maximum a posteriori (MAP) estimation algorithm proposed in our previous study, which studies the linear measurement models of each subsystem, and by weakening the constraint condition as that each time-varying subsystem is observable, this paper proves that the error covariances of state estimation and prediction obtained from the improved algorithm are respectively positive definite and have upper bounds, which verifies the feasibility of this algorithm. We also use new weighting functions and time-varying exponential smoothing method to ensure the robustness and improve the forecast accuracy of the distributed state estimation method. At last, an example is used to demonstrate the effectiveness of the proposed algorithm together with the parameter identification.     

Containment control of fractional-order multi-agent systems with time-varying delays

In this paper, containment control problems of networked fractional-order multi-agent systems with time-varying delays are studied. The normalized directed graphs are employed to characterize the communication topologies. Two sampled-data based containment control protocols are proposed, which can overcome the time-varying delays and switching topologies. It is interestingly found that the decays of the closed-loop systems correspond to the Mittag-Leffler function and its approximation, which are the extensions of the exponential function and its approximation, respectively. Based on the algebraic graph theory, the properties of row-stochastic matrix, and the relation between the topologies and the matrices, some conditions for containment control are established. For the fixed topology, a necessary and sufficient condition is obtained; and for the switching topology, a sufficient condition is provided. Finally, the theoretical results are illustrated by several numerical simulations.     

Consensus control for multi-agent systems with distributed parameter models via iterative learning algorithm

This paper deals with the problem of iterative learning control algorithm for a class of multi-agent systems with distributed parameter models. And the considered distributed parameter models are governed by the parabolic or hyperbolic partial differential equations. Based on the framework of network topologies, a consensus-based iterative learning control protocol is proposed by using the nearest neighbor knowledge. When the iterative learning control law is applied to the systems, the consensus errors between any two agents on L² space are bounded, and furthermore, the consensus errors on L² space can converge to zero as the iteration index tends to infinity in the absence of initial errors. Simulation examples illustrate the effectiveness of the proposed method.     

Leader-following consensus of multi-agent systems with connectivity-mixed attacks and actuator/sensor faults

This study investigates the leader-following consensus issue of multi-agent systems subject to simultaneous connectivity-mixed attacks, actuator/sensor faults and disturbances. Connectivity-mixed attacks are remodeled into connectivity-maintained and connectivity-paralyzed topologies in a switched version, and actuator/sensor faults are established with unified incipient-type and abrupt-type characteristics. Then, unknown input observer-based decoupling and estimation are incorporated to achieve unknown state and fault observations with the normalized technique, and the leader-following consensus-based compensation to faults, resilience to attacks and robustness to disturbances are also realized with the neighboring output information and sensor fault estimation through the distributed framework. Criteria of achieving exponential leader-following consensus of multi-agent systems under cyber-physical threats are derived with dual attack frequency and activation rate indicators. Simulation example is conducted to exemplify the validation and merits of the proposed leader-following consensus algorithm.     

Cooperative output regulation problem of multi-agent systems with stochastic packet dropout and time-varying communication delay

In this paper, we deal with the cooperative output regulation problem of linear multi-agent systems on a directed network topology subject to both stochastic packet dropout and time-varying communication delay. On the basis of introducing a queuing mechanism, a distributed state feedback control algorithm is proposed. Then the continuous-time multi-agent systems with piece-wise constant control are converted into discrete-time systems. Under some standard assumptions, the necessary and sufficient conditions under which the tracking errors of followers approach to the origin asymptotically are proposed for different exosystems. Finally, the proposed results are verified via two examples.     

Fuzzy gain-scheduled active fault-tolerant control of a wind turbine

Advanced fault detection and accommodation schemes are required for ensuring efficient and reliable operation of modern wind turbines. This paper presents a novel approach in designing a fault detection and diagnosis (FDD) and fault-tolerant control (FTC) scheme for a wind turbine using fuzzy modeling, identification and control techniques. First, an improved gain-scheduled proportional-integral (PI) control system based on fuzzy gain scheduling (FGS) technique for multi-input and multi-output wind turbine system is designed. Then, to accommodate sensor faults and based on a signal correction algorithm, an active fault-tolerant control system (AFTCS) is developed as an extension of the gain-scheduled PI control system. The AFTCS exploits the fault information from a model-based FDD scheme developed using fuzzy modeling and identification method. The proposed schemes are evaluated by a series of simulations on a well-known large off-shore wind turbine benchmark in the presence of wind turbulences, measurement noises, and different realistic fault scenarios. All results indicate high effectiveness and robustness of the designed control systems in both fault-free and faulty operations of the wind turbine.     

Fault detection and diagnosis for stochastic systems via output PDFs

This paper investigates a new type of fault detection and diagnosis (FDD) problem for non-Gaussian stochastic distribution systems via the output probability density function (PDF). The PDF can be approximated by using square root B-spline expansions. In this framework, an optimal fault detection algorithm is presented by introducing the tuning parameter such that the residual is as sensitive as possible to the fault. When the fault occurs, an adaptive network parameter-updating law is designed to approximate the fault. At last, paper-making process example is given to demonstrate the efficiency of the proposed approach.     

Cooperative containment control in time-delayed multi-agent systems with discrete-time high-order dynamics under dynamically changing topologies

This paper addresses the containment control problem for discrete-time high-order multi-agent systems (MASs) with dynamically changing topologies and time-varying delays. By considering the influence of switching topologies, a distributed containment control protocol that only involves the agent’s own information and its neighbors’ partial information is given to make all the followers enter and keep moving in the convex hull formed by the static leaders. A novel technique is employed to transform the high-order MAS with dynamically changing topologies into a switched augmented system with nonnegative coefficient matrices, and then convert the convergence problem of the switched augmented system to a product problem of infinite time-varying row stochastic matrices. With the help of graph theory and the properties of stochastic indecomposable and aperiodic (SIA) matrices, a sufficient condition in terms of communication topologies is derived, that is, the high-order containment control with dynamically changing topologies and time-varying delays can be achieved if the union of the effective communication topologies across any time intervals with some given length contains a spanning forest rooted at the leaders. Finally, computer simulations are conducted to illustrate the efficiency of the theoretical findings.     

Distributed coordination on state-dependent fuzzy graphs

Multiagent systems are increasingly becoming popular among researchers spanning multiple fields of study. However, existing studies only models communication interaction between agents as either fixed or switching topologies described by crisp graphs supported by algebraic graph theories. In this paper, we propose an alternative approach to describing agent interactions using fuzzy graphs. Our approach is aimed at opening up new research avenues and defining new problems in coordination control especially in terms of dynamics between agents’ states, graph topologies and coordination objectives. This paper studies distributed coordination on fuzzy graphs where the edge-weights modeling network topologies are dependent on the states of the agents in the network. In hindsight, the network weights are adjustable based on the situational state of the agents. First, we introduce the concept of fuzzy graphs and give some distinguishing features from the crisp or fixed graphs. Next, we provide some membership functions to define the state-dependent weights and finally we use some simulations to demonstrate the convergence of the proposed consensus algorithms especially for cases where the agents are subject to system failures.     

Adaptive fault-tolerant shape control for nonlinear Lipschitz stochastic distribution systems

This paper addresses the problem of adaptive fault estimation and fault-tolerant control for a class of nonlinear non-Gaussian stochastic systems subject to time-varying loss of control effectiveness faults. In this work, time-varying faults, Lipschitz nonlinear property and general stochastic characteristics are taken into consideration in a unified framework. Instead of using the system output signal, the output distribution is adopted for shape control. Both the states and faults are simultaneously estimated by an adaptive observer. Then, a fault tolerant shape controller is designed to compensate for the faults and realize stochastic output distribution tracking. Both the fault estimation and the fault tolerant control schemes are designed based on linear matrix inequality (LMI) technique. Satisfactory performance has been obtained for a numerical simulation example. Furthermore the proposed scheme is successfully tested in a case study of particle size distribution control for an emulsion polymerization reactor.     

Fault detection for discrete-time systems with randomly occurring nonlinearity and data missing: A quadrotor vehicle example

This paper concerns the fault detection (FD) problem for a class of discrete-time systems subject to data missing and randomly occurring nonlinearity modeled by two independent Bernoulli distributed random variables. We propose to design a set of fault detection filters, or residual generation systems, corresponding to each of the fault components, to guarantee that each subsystem is mean square stable and satisfies a prescribed disturbance attenuation level. Sufficient conditions are established in the form of linear matrix inequalities (LMIs). System faults can be effectively detected by generating the residues and comparing them with the dynamic fault thresholds. A quadrotor vehicle example with faults on angles and angular rates illustrates and verifies the effectiveness of the proposed algorithm.     

Consensus-based distributed fixed-time optimization for a class of resource allocation problems

A class of resource allocation problems with equality constraint are considered in this paper, such as economic dispatch problem in smart grid systems, which is essentially an optimization problem. Inspired by the Lagrange multiplier method, the resource allocation problem is transformed into a multi-agent consensus problem for large-scale networked distributed nodes. A consensus-based distributed fixed-time optimization algorithm is presented, where the information exchange network is depicted by a strongly connected and weight-balanced digraph. This type of communication network can ensure that the equality constraint always holds. Moreover, a new globally fixed-time stability theorem for nonlinear systems is first given in this paper. Based on this theorem and consensus theory, the optimal resource allocation scheme can be given in a fixed time. Finally, the application and comparison of the designed algorithm show that the algorithm can effectively solve the allocation problem of power resources such as economic dispatch.     

Finite impulse response filter based fault estimation with computational efficiency for linear discrete time-varying systems subject to multiplicative noise

The robust fault estimation problem for linear discrete time-varying (LDTV) systems subject to multiplicative noise is investigated by means of finite impulse response (FIR) filter. A novel analytical redundancy, expressed via all states of the previous time window, is originally established to construct the fault estimator. To ensure the satisfactory fault estimation accuracy in stochastic sense under the interference of random uncertainty, a new performance index in forms of matrix trace function is proposed. An easy-to-check necessary and sufficient condition is presented to obtain the optimal filter gain via minimizing the performance index at each time instant. It is analytically demonstrated that, the newly proposed fault estimation algorithm enjoys obvious computational advantages in updating the filter gain, especially as the length of the time window increases for time-varying systems. Simulation results are finally provided to verify its feasibility and superiority.