Adaptive Fault-Tolerant Synchronization Control of a Class of Complex Dynamical Networks With General Input Distribution Matrices and Actuator Faults

Formation Tracking of Multiagent Systems With Time-Varying Actuator Faults and Its Application to Task-Space Cooperative Tracking of Manipulators

This article is concerned with a fault-tolerant formation tracking problem of nonlinear systems under unknown faults, where the leader's states are only accessible to a small set of followers via a directed graph. Under these faults, not only the amplitudes but also the signs of control coefficients become time-varying and unknown. The current setting will enhance the investigated problem's practical relevance and at the same time, it poses nontrivial design challenges of distributed control algorithms and convergence analysis. To solve this problem, a novel distributed control algorithm is developed by incorporating an estimation-based control framework together with a Nussbaum gain approach to guarantee an asymptotic cooperative formation tracking of nonlinear networked systems under unknown and dynamic actuator faults. Moreover, the proposed control framework is extended to ensure an asymptotic task-space coordination of multiple manipulators under unknown actuator faults, kinematics, and dynamics. Lastly, numerical simulation results are provided to validate the effectiveness of the proposed distributed designs.

Robust Sampled-Data Control for Switched Complex Dynamical Networks With Actuators Saturation

In this article, an aperiodic sampled-data control problem is investigated for polytopic uncertain switched complex dynamical networks subject to actuator saturation. Due to the constraint on the upper bound of the sampling interval being no greater than the dwell time, the issue concerning the asynchronization between the sampled-data controller mode and the system mode is hence considered to be caused by subsystems that may switch in a sampling interval. By considering the sampling interval without switching and the sampling interval with switching, the parameters-dependent loop-based Lyapunov functionals are constructed, respectively. With the help of the constructed functional, mean-square exponential stability criteria for the error polytopic uncertain switched complex dynamical networks are presented under the definition of average dwell time. Furthermore, based on the stability criteria, the asynchronous aperiodic sampled-data controller is designed for polytopic uncertain switched complex dynamical networks subject to actuator saturation. The polytopic uncertain switched complex dynamical networks can be guaranteed to exponentially synchronize with the target node based on the proposed stability conditions and aperiodic sampled-data controller design method. Finally, by transforming the proposed theoretical conditions into the LMI-based objective optimization problem, the domain of attraction of polytopic uncertain switched complex dynamical networks is estimated. An example based on switched Chua's circuit is applied to verify the effectiveness of the proposed method.

Adaptive Bearing-Only Formation Tracking Control for Nonholonomic Multiagent Systems

In this article, we consider the formation tracking problem of nonholonomic multiagent systems only using relative bearing measurements between the agents. Such a practical and important yet challenging issue has been taken into limited consideration by existing approaches, which usually requires additional measurements such as relative positions. The contributions of this article are two-fold. First, a fully distributed reference velocity estimator is proposed. Under the proposed adaptive estimator, each agent can estimate the time-varying reference velocity asymptotically. Second, an input-to-state stable controller is designed according to the bearing rigid theory. Under the proposed controller, the formation with bearing-only constraints can be achieved. Finally, the proposed scheme is demonstrated and its effectiveness is verified by presenting some simulation and experimental tests.

R-IMNet: Spatial-Temporal Evolution Analysis of Resource-Exhausted Urban Land Based on Residual-Intelligent Module Network

The transformation of resource-exhausted urban land is an urgent problem for sustainable urban development in the world today. Obtaining the urban land use type and analyzing the changes in their land use can lead to better management of the relationship between economic development and resource utilization. In this paper, a residual-intelligent module network was proposed to solve the problems of low classification accuracy and missing objects edge information in traditional computer classification methods. The classification of four Landsat-TM/OLI images from 1993–2020 for Jiaozuo city (the first batch of resource-exhausted cities in China) was realized by this method. The results (overall accuracy was 98.61%, in 2020 images) were better than the comparison models (support vector machine, 2D-convolutional neural network, hybrid convolution networks; overall accuracy was 87.12%, 96.16%, 98.46%, respectively) and effectively reduced the loss of information on the edge of the ground objects. On this basis, six main land use types were constructed by combining field surveys and other methods. The characteristics and driving forces of spatial-temporal change in land use were explored from the aspect of social, economic and policy factors. The results showed that from 1993 to 2020 the cultivated land, forest land, water body and other land types in the study area decreased by 690.97 km2, 57.54 km2, 47.04 km2 and 59.43 km2, respectively. The construction land and bare land increased by 839.38 km2 and 15.57 km2, respectively. The transfer of land use types was mainly from cultivated land to construction land, with a cumulative conversion of 920.95 km2 within 27 years. The driving forces of land use in the study area were analyzed by principal component analysis (PCA) and regression analysis. The spatial-temporal evolution of land use types was affected by policy changes, the level of social development and the adjustment in the economy, industry and agriculture structure. The investment in fixed assets and per capita net income in rural areas were the top two influencing factors and their cumulative contribution rate was 94.62%. The findings of this study can provide scientific reference and theoretical support for land use planning, land reclamation in mining areas, ecological protection and sustainable development in Jiaozuo and other resource-exhausted cities in the world.

Active Fault-Tolerant Control for Near-Space Hypersonic Vehicles

Due to the harsh working environment, Near-Space Hypersonic Vehicles (NSHVs) have the characteristics of frequent faults, which seriously affect flight safety. However, most researches focus on active fault-tolerant control for actuator faults. In order to fill the gap of active fault-tolerant control for sensor faults, this paper presents an Active Fault-Tolerant Control (AFTC) strategy for NSHVs based on Active Disturbance Rejection Control (ADRC) combined with fault diagnosis and evaluation. With the proposed AFTC strategy, both sensor faults and actuator faults can be compensated within 0.5 s. Wavelet packet decomposition and Kernel Extreme Learning Machine (KELM) are associated to ensure the high accuracy and real-time ability of fault diagnosis. Simulation results show that the proposed fault diagnosis method can significantly reduce the divergence of diagnosis results by up to 98%. The fault information is used to generate tolerant compensation, which is combined with the ADRC to achieve AFTC. Statistical results indicate that AFTC has significantly lower static error than ADRC. The proposed AFTC method endows NSHVs with the ability to complete missions even when various types of faults appear. Its advantages are demonstrated in comparison with other fault diagnosis and tolerant control methods.

Event-Triggered H∞ Control for T-S Fuzzy Systems via New Asynchronous Premise Reconstruction Approach

This article studies the problem of H_∞ controller design for discrete-time T-S fuzzy systems under an event-triggered (ET) communication mechanism. By proposing a new asynchronous premise reconstruction approach, new types of ET fuzzy controllers are designed to overcome the challenges caused by the mismatch of premise variables, in which the gains of the designed controllers are automatically updated at different triggering instants according to an online algorithm. By constructing discontinuous Lyapunov functions, it is proved that the proposed ET controllers guarantee the stability and H_∞ performance of the closed-loop systems. Two examples are provided to verify the validity of the proposed design method.

A New Method for Topology Identification of Complex Dynamical Networks

Topology identification of complex dynamical networks received extensive attention in the past decade. Most existing studies rely heavily on the linear independence condition (LIC). We find that a critical step in using this condition is not rigorous. Besides, it is difficult to verify this condition. Without regulating the original network, possible identification failure caused by network synchronization cannot be avoided. In this paper, we propose a new method to overcome these shortcomings. We add a regulation mechanism to the original network and construct an auxiliary network consisting of isolated nodes. Along with the outer synchronization between the regulated network and the auxiliary network, we show that the original network can be identified. Our method can avoid identification failure caused by network synchronization. Moreover, we show that there is no need to check the LIC. We finally provide some examples to demonstrate that our method is reliable and has good performances.

Output Synchronization of Complex Dynamical Networks With Multiple Output or Output Derivative Couplings

In this paper, the output synchronization problem for complex dynamical networks (CDNs) with multiple output or output derivative couplings is discussed in detail. Under the help of Lyapunov functional and inequality techniques, an output synchronization criterion is presented for CDNs with multiple output couplings (CDNMOCs). To ensure the output synchronization of CDNMOCs, an adaptive control scheme is also devised. Similarly, we also take into account the adaptive output synchronization and output synchronization of CDNs with multiple output derivative couplings. At last, several numerical examples are designed to testify the effectiveness of the proposed results.

Reliable cooperative control and plug-and-play operation for networked heterogeneous systems under cyber-physical attacks

This paper considers a reliable cooperative control and plug-and-play operation problem in networked heterogeneous systems (NHSs), and focuses on the scenario subjected to physical attack-induced actuator failures/uncertainties and Denial-of-Service (DoS) attacks on communication channels between physical nodes. To solve it, a new self-feedback control with an adaptive integral sliding-mode compensator is developed. The new one configures each heterogeneous system under the malicious actuator failures/uncertainties to be passivity-short (PS). The PS index quantifies the impact of each attacked heterogeneous dynamics on their networked operations. Furthermore, based on cloud networks, a novel network-level distributed control is proposed. It improves robustness of the systems against DoS with no frequency and duration constraints. And then, a technical condition is presented with the aid of the impact equivalence principle. Under the condition, the self-feedback and network-level controls designed separately but performed together ensure the leader-follower consensus and plug-and-play operation of the attacked NHSs. Finally, the effectiveness of the proposed method is verified by flight control systems.

Distributed Fault-Tolerant Control of Multiagent Systems: An Adaptive Learning Approach

This paper focuses on developing a distributed leader-following fault-tolerant tracking control scheme for a class of high-order nonlinear uncertain multiagent systems. Neural network-based adaptive learning algorithms are developed to learn unknown fault functions, guaranteeing the system stability and cooperative tracking even in the presence of multiple simultaneous process and actuator faults in the distributed agents. The time-varying leader's command is only communicated to a small portion of follower agents through directed links, and each follower agent exchanges local measurement information only with its neighbors through a bidirectional but asymmetric topology. Adaptive fault-tolerant algorithms are developed for two cases, i.e., with full-state measurement and with only limited output measurement, respectively. Under certain assumptions, the closed-loop stability and asymptotic leader-follower tracking properties are rigorously established.

Synchronization of complex dynamical networks with random coupling delay and actuator faults

This paper addresses the issue of passivity-based synchronization problem for a family of Markovian jump neutral complex dynamical networks (NCDNs) with coupling delay and actuator faults. Also, by considering the effect of random fluctuation in complex dynamical network systems, the occurrence of coupling delay are taken in terms of a stochastic distribution, which obeys the Bernoulli distribution. To handle the fault effects in actuators of proposed complex network systems, an actuator fault model is considered. The main objective of this paper is to develop a robust state feedback controller such that for all possible actuator failures and random coupling delays, all nodes of the proposed Markovian jump NCDNs is globally asymptotically synchronized to the reference node in mean square sense and guarantee the output strict passivity performance. By developing a suitable Lyapunov-Krasovskii functional and utilizing the Wirtinger-based integral inequality, the required a set of sufficient conditions for the synchronization of proposed system is established in form of linear matrix inequalities. Finally, three numerical examples including a 3-dimensional Lorenz chaotic model are provided to demonstrate the correctness and superiority of the proposed control scheme.

Model-Free Fault Tolerant Control for a Class of Complex Dynamical Networks With Derivative Couplings

This paper studies the fault tolerant synchronization control for a class of derivative coupled complex dynamical networks (CDNs). Different from the existing results, each subsystem model is assumed to be completely unknown and the coupling terms are mismatched with the control input. Within this framework, a novel model-free fault tolerant controller is designed. Under the proposed control law, the synchronization errors of CDNs are proved to asymptotically converge to zero, which means that the synchronization is successfully achieved. Especially, by combining an important spectral decomposition technique and some properties of Laplacian matrix, a data-based algorithm is provided to derive the controller parameter. In addition, the proposed method is also valid for the CDNs with unknown coupling weights. Finally, examples on circuit systems are given to verify the theoretical results, and some circuit realizations of the proposed control law are implemented based on the circuit theory.

Improved Time-Synchronization Algorithm Based on Direct Compensation of Disturbance Effects

In this paper, an improved time-synchronization algorithm is proposed. The improvement of time synchronizing performance was achieved by introducing a stochastic model-based direct compensation of the disturbance effects appearing in the IEEE 1588 Precision Time Protocol (PTP)-based time synchronization system. A dynamic model of PTP clock system was obtained by reflecting the three major sources of disturbances, i.e., clock frequency drift, clock rate offset, and network noise. With the application of the dynamic model of the PTP clock system, the effects of the disturbances can be effectively eliminated in the PTP time synchronization control loop. Computer simulations are performed to verify the performance of the proposed time synchronization algorithm by applying the various types of disturbances, including network noise and clock drift. The simulation results are compared with those of other representative time synchronization algorithms, i.e., IEEE 1588 PTP algorithm and Kalman-filter-based algorithm. It is shown that the proposed algorithm improves time synchronizing performance up to 84% with respect to that of the Kalman-filter-based synchronization algorithm when simulated with colored noise type disturbances. The proposed time synchronization algorithm is expected to contribute for the realization of future Ethernet-based industry-plant monitoring and control including IEC 61850-based digital substation.

Adaptive Fault Estimation for T-S Fuzzy Interconnected Systems Based on Persistent Excitation Condition via Reference Signals

This paper is concerned with the fault estimation (FE) problem for a class of interconnected nonlinear systems described by Takagi-Sugeno fuzzy models. Different from the existing FE approaches, where the reference inputs are always viewed as external disturbances, the considered reference signals are injected directly in the controller to excite the system states to ensure persistent excitation condition. Within this framework, a bank of distributed adaptive observers are then designed to effectively estimate the actuator fault parameters even in the presence of nonlinear interconnections. Moreover, in the disturbance-free case, by employing graph theory, a global Lyapunov function is constructed such that the corresponding fault parameter estimate errors are proved to be asymptotically convergent provided that the interconnected strengths are less than a given constant. Finally, two simulation examples are provided to demonstrate the validity of the presented FE scheme.

Artificial Immune Systems: An Overview for Faulting Actuators

This paper reviews Artificial Immune Systems (AIS) that can be implemented to compensate for actuators that are in a faulted state or operating abnormally. Eventually, all actuators will fail or wear out, and these actuator faults must be managed if a system is to operate safely. The AIS are adaptive algorithms which are inherently well-suited to these situations by treating these faults as infections that must be combated. However, the computational intensity of these algorithms has caused them to have limited success in real-time situations. With the advent of distributed and cloud-based computing these algorithms have begun to be feasible for diagnosing faulted actuators and then generating compensating controllers in near-real-time. To encourage the application of AIS to these situations, this work presents research for the fundamental operating principles of AIS, their applications, and a brief case-study on their applicability to fault compensation by considering an overactuated rover with four independent drive wheels and independent front and rear steering.

Disturbance and uncertainty rejection performance for fractional-order complex dynamical networks

This paper investigates the synchronization issue for a family of time-delayed fractional-order complex dynamical networks (FCDNs) with time delay, unknown bounded uncertainty and disturbance. A novel fractional uncertainty and disturbance estimator (FUDE) based feedback control strategy is proposed to not only synchronize the considered FCDNs but also guaranteeing the precise rejection of unmodelled system uncertainty and external disturbance. Especially, in FUDE-based approach, model uncertainties and external disturbance are integrated as a lumped disturbance and it does not require a completely known system model or a disturbance model. On the other hand, the design algorithm for the proposed control strategy is based on the state-space framework, rather than frequency-based design methodologies in the literature, which helps for predominant comprehension of the inner system behaviour. Also, by the temperance of Lyapunov stability theory and fractional calculus, a set of adequate conditions in the linear matrix inequality framework is obtained, which guarantees the robust synchronization of the closed-loop system. Furthermore, an iterative optimization algorithm is proposed to improve control robustness against the external disturbance and model uncertainties. Finally, two numerical illustrations including financial network model, where the influence of adjustment of macro-economic policies in the entire financial system are given to exhibit the rightness and important features of the acquired theoretical results.

Adaptive Intelligent Control for Nonlinear Strict-Feedback Systems With Virtual Control Coefficients and Uncertain Disturbances Based on Event-Triggered Mechanism

This paper investigates the problem of adaptive fuzzy control on the basis of an event-triggered mechanism for nonlinear strict-feedback systems with time-varying external disturbances and virtual control coefficients in the presence of actuator failures. Virtual control coefficients are correlated with the designed adaptive law and control signal. In the backstepping technique procedure, fuzzy logic systems are utilized to approximate an unknown nonlinear function, and the tuning function is implemented to cope with the destabilizing problem of the control design. To save communication resources, an adaptive fuzzy event-triggered control strategy is developed to update the control input when the triggering condition is satisfied. Then, all of the closed-loop signals can remain semi-globally uniformly ultimately bounded. The Zeno behavior can be excluded. Finally, a numerical example and a real system are provided to illustrate the effectiveness of the proposed approach.

Adaptive Fault-Tolerant Control for Nonlinear Systems With Multiple Sensor Faults and Unknown Control Directions

This paper investigates the problem of adaptive fault-tolerant control for a class of nonlinear parametric strict-feedback systems with multiple unknown control directions. Multiple sensor faults are first considered such that all real state variables are unavailable. Then, a constructive design method for the problem is set up by exploiting a parameter separation and regrouping technique. To circumvent the main obstacle caused by the coupling effects of multiple unknown control directions and sensor faults, a region-dependent segmentation analysis method is proposed. It is proven that the closed-loop system is globally exponentially stable. Simulation results are presented to illustrate the effectiveness of the proposed scheme.

Nonfragile Exponential Synchronization of Delayed Complex Dynamical Networks With Memory Sampled-Data Control

This paper considers nonfragile exponential synchronization for complex dynamical networks (CDNs) with time-varying coupling delay. The sampled-data feedback control, which is assumed to allow norm-bounded uncertainty and involves a constant signal transmission delay, is constructed for the first time in this paper. By constructing a suitable augmented Lyapunov function, and with the help of introduced integral inequalities and employing the convex combination technique, a sufficient condition is developed, such that the nonfragile exponential stability of the error system is guaranteed. As a result, for the case of sampled-data control free of norm-bound uncertainties, some sufficient conditions of sampled-data synchronization criteria for the CDNs with time-varying coupling delay are presented. As the formulations are in the framework of linear matrix inequality, these conditions can be easily solved and implemented. Two illustrative examples are presented to demonstrate the effectiveness and merits of the proposed feedback control.

Neural-Network-Based Adaptive Decentralized Fault-Tolerant Control for a Class of Interconnected Nonlinear Systems

This paper is concerned with the adaptive decentralized fault-tolerant tracking control problem for a class of uncertain interconnected nonlinear systems with unknown strong interconnections. An algebraic graph theory result is introduced to address the considered interconnections. In addition, to achieve the desirable tracking performance, a neural-network-based robust adaptive decentralized fault-tolerant control (FTC) scheme is given to compensate the actuator faults and system uncertainties. Furthermore, via the Lyapunov analysis method, it is proven that all the signals of the resulting closed-loop system are semiglobally bounded, and the tracking errors of each subsystem exponentially converge to a compact set, whose radius is adjustable by choosing different controller design parameters. Finally, the effectiveness and advantages of the proposed FTC approach are illustrated with two simulated examples.

High-Performance Consensus Control in Networked Systems With Limited Bandwidth Communication and Time-Varying Directed Topologies

Communication data rates and energy constraints are two important factors that have to be considered in the coordination control of multiagent networks. Although some encoder-decoder-based consensus protocols are available, there still exists a fundamental theoretical problem: how can we further reduce the update rate of control input for each agent without the changing consensus performance? In this paper, we consider the problem of average consensus over directed and time-varying digital networks of discrete-time first-order multiagent systems with limited communication data transmission rates. Each agent has a real-valued state but can only exchange binary symbolic sequence with its neighbors due to bandwidth constraints. A class of novel event-triggered dynamic encoding and decoding algorithms is proposed, based on which a kind of consensus protocol is presented. Moreover, we develop a scheme to select the numbers of time-varying quantization levels for each connected communication channel in the time-varying directed topologies at each time step. The analytical relation among system and network parameters is characterized explicitly. It is shown that the asymptotic convergence rate is related to the scale of the network, the number of quantization levels, the system parameter, and the network structure. It is also found that under the designed event-triggered protocol, for a directed and time-varying digital network, which uniformly contains a spanning tree over a time interval, the average consensus can be achieved with an exponential convergence rate based on merely 1-b information exchange between each pair of adjacent agents at each time step.