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The problem of memory feedback tracking control for unmanned aerial vehicle systems subject to nonlinear actuator faults and disturbances is investigated in this paper. First, a generalized extended state observer is constructed to get the estimates of the system states and the disturbances. Then, by incorporating the obtained estimates in the control design, an anti-disturbance memory feedback tracking control law is designed such that the resulting closed-loop augmented system is asymptotically stable, and thereby the states of the unmanned aerial vehicle system track the given reference trajectories with satisfactory disturbance rejection performance. Notably, by constructing appropriate asymmetric Lyapunov–Krasovskii functional, the stability and stabilization conditions for guaranteeing the desired tracking and disturbance attenuation performance are derived in terms of linear matrix inequalities. Further, the observer gains are evaluated using the pole placement approach. Finally, numerical simulations are given to verify that the proposed control strategy is viable. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive neural network dynamic event-triggered consensus method for nonlinear multi-agent systems (MASs) under sensor deception attacks and actuator faults. A single parameter learning method is integrated into backstepping technique to simplify the design procedure. The neural network is utilized to compensate for the unknown dynamics of MASs. The designed controller can withstand sensor deception attacks and accommodate actuator faults. Moreover, the dynamic event-triggered mechanism is designed to conserve communication resources. The designed control law ensures that all signals of the MASs are uniformly bounded. An expository simulation example reveals the virtue of the presented method. [ABSTRACT FROM AUTHOR]

This study investigates fault-tolerant consensus tracking for discrete-time multi-agent systems (MASs) subject to external eavesdropping threats and additive actuator faults. First, actuator faults are modeled by difference equations, and decentralized observers are constructed to estimate actuator faults as well as system states. To offset fault-induced effects, ensure secure communication, and alleviate communication congestion, neighboring encrypted state information based on the encryption–decryption strategy (EDS) and estimated fault are integrated into a distributed active fault-tolerant consensus tracking control (FCTC) protocol. Through the properties of compatible norms, criteria for the controller, observer, and dynamic encryption key in EDS are derived to achieve leader-following consensus (LFC) of MASs with bias and drift actuator faults. Simulation results confirm the validity of the encryption–decryption-based distributed FCTC strategy. • The design of consensus tracking control protocols combine privacy preservation and fault-tolerant control technologies when multi-agent systems suffer from cyber and physical security problems. • A sufficient condition guarantees the performance of leader-following consensus tracking and serves as a criterion for designing the controller, observer, and symmetric encryption key. • A new modified Riccati-based controller design method separates the graph properties from the feedback design problem. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of leader-follower consistency control for high-order nonlinear multi-agent systems affected by time-varying bounded communication faults and actuator faults. First, an estimator is designed for each follower to address certain followers' unawareness of the leader system coefficient matrix. Further, an event-triggered communication mechanism is designed to reduce the communication burden among agents. Then state observers for both the leader and followers are designed. Afterwards, a distributed control strategy is proposed utilizing observation information and backstepping technology to ensure that the system can achieve the leader-follower consistency control under time-varying bounded communication faults and actuator faults. The stability of the leader-follower control system is proved using Lyapunov theory, while the Zeno behavior can be excluded for each agent. Finally, the effectiveness of the proposed distributed control strategy is verified by a simulation example. [ABSTRACT FROM AUTHOR]

This paper proposes a nonfragile prescribed performance control (PPC) scheme for robot manipulators with actuator faults, which can address the fragility problem of the existing prescribed performance control and guarantee the transient steady-state performance of the tracking error. Firstly, a novel performance function with small overshoot, finite time convergence, and an adjustment term is proposed. Its adjustment term can adjust the constraint range when the error approaches the boundary, thus avoiding the control singularity problem. Then, error transformation is employed to convert the tracking problem with performance constraints into a stabilization problem for the new system. On this basis, fuzzy neural networks are utilized to address the model uncertainty. Stability analysis of the designed controller is conducted utilizing the Lyapunov method. Finally, numerical simulations are employed to verify the effectiveness and superiority of the proposed scheme. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper presents a fixed‐time state observer‐based robust adaptive neural fault‐tolerant control (RANFTC) for attitude and altitude tracking and control of quadrotor unmanned aerial vehicles (UAVs), considering multiple actuator faults, parametric uncertainty, and unknown external disturbances simultaneously. A novel fixed‐time state error estimation based on sliding mode observer is designed, which is independent of initial conditions. A proportional–integral–derivative (PID) based sliding mode control (SMC) is proposed to handle actuator faults and unknown disturbances in combination with the fixed‐time observer within the fault‐tolerant control (FTC) design scheme. The radial basis function neural network (RBFNN) is employed with the controller to approximate the uncertain parameters of the system. Furthermore, two new adaptive laws are designed to estimate the sudden actuator fault and the unknown upper bound of disturbances independently. Implementing these estimation schemes avoids overestimation, enhances the robustness of the presented controller, and substantially eliminates the control chattering problem. By applying the Lyapunov stability concept, the suggested control strategy guarantees that the states of the quadrotor UAV converge to the origin in a finite time. Finally, simulation studies are conducted to demonstrate the tracking performance and highlight the effectiveness of the proposed FTC design compared to the existing FTC methods. [ABSTRACT FROM AUTHOR]

ABSTRACT In this brief, an optimal active fuzzy fault‐tolerant control (OAFFTC) scheme is proposed for a class of unknown perturbed multi‐input multi‐output (MIMO) nonlinear systems with nonaffine nonlinear actuator faults and time‐varying sensor faults. The control strategy can handle automatically (online updating) with three additives (bias, drift, and loss of accuracy) along with one multiplicative (loss of effectiveness) sensor faults and nonlinear state‐dependent actuator faults. In order to deal with uncertain system dynamics, sensor and actuator faults, and external disturbances, fuzzy systems (FSs) and backstepping techniques were combined to provide the adaptive control term as well as a robust control term. Butterworth filter is used to get rid the algebraic loop problem. The suggested robust term, can deal with approximation errors of the fuzzy systems (FSs). To automatically optimize the adaptive parameters and the starting conditions, particle swarm optimization (PSO) approach is introduced. The Lyapunov approach is employed to demonstrate the closed‐loop system's stability. To assess the efficacy and correctness of the suggested scheme, quadrotor dynamic model is performed on the simulation part. [ABSTRACT FROM AUTHOR]

In this paper, the issue of extended dissipative control for interval type-2 fuzzy Markov jump systems subject to actuator faults is investigated. It is different from the traditional T-S fuzzy method, the interval type-2 fuzzy set is introduced to model the system, and the nonlinearity with parameter uncertainty is captured using the interval type-2 Takagi-Sugeno fuzzy model. During the implementation of the control process, the possible actuator failures are fully considered when designing a controller, and a fault-tolerant control strategy is employed to enhance the elasticity of the designed controller. It is worth noting that, in order to better adapt to the characteristics of interval type-2 fuzzy system, the designed controller follows non-parallel distribution compensation rules, that is, the designed controller does not have the same premise structure and fuzzy rules as the system model, which further improves the flexibility of controller design. Some sufficient conditions ensuring the stability with the extended dissipative performance of the systems are established. In the meanwhile, the obtained results with less conservatism are ensured owing to the boundary information of membership functions considered in the stability analysis. Finally, two numerical examples are provided to indicate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In the fault-tolerant control tasks of nonlinear systems, unmeasurable states, time delay and actuator faults are considered to be the main factors hindering effective controller design and tracking performance improvement. To solve these problems, firstly, a state observer is established to estimate the unmeasurable of the system and the prescribed performance control based on error transformation is presented to maintain system efficiency and reliability. Secondly, the problem of controller design caused by input delay is effectively solved by constructing an auxiliary tracking error and auxiliary system and combining with the well-designed Lyapunov-Krasovskii functionals. Thirdly, the newly employed damping term in the intermediate control law is utilized to compensate for the possibly unlimited number of faults. Then, it is proved that all signals are semi-global practical finite-time stable based on the backstepping technique. Meanwhile, the tracking error can converge to a specified range within finite time. Finally, comparative simulations are presented to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Four-wheel independent drive and four-wheel independent steering (4WID-4WIS) vehicles provide increased redundancy in fault-tolerant control (FTC) schemes, enhancing heterogeneous fault-tolerant capabilities. This paper addresses the challenge of maintaining vehicle safety and maneuverability in the presence of actuator faults in autonomous vehicles, focusing on 4WID-4WIS systems. A novel unified hierarchical active FTC strategy is proposed to handle various actuator failures. The strategy includes an upper-layer motion controller that determines resultant force requirements based on trajectory tracking errors and a middle-layer allocation system that redistributes tire forces to fault-free actuators using fault information. This study, for the first time, considers multi-fault scenarios involving longitudinal and lateral coupling, calculating FTC boundaries for each fault type. Additionally, a fault tolerance index is introduced for 256 fault scenarios, using singular value decomposition to linearly represent the vehicle attainable force domain. Based on this, an adaptive velocity planning strategy is developed to balance safety and maneuverability under fault conditions. Matlab 2021a/Simulink and Carsim 2019 co-simulation results validate the proposed strategies, demonstrating significant improvements in fault-tolerant performance, particularly in complex and emergency scenarios. [ABSTRACT FROM AUTHOR]

The article addresses the event‐triggered obstacle avoidance control problem for autonomous surface vehicles subject to actuator faults. In order to tackle the challenges presented by unknown actuator faults, internal model uncertainties, and external disturbances, we propose a fault‐tolerant control scheme that relies on an extended state observer to estimate the unknown parameters. In addition, considering the occurrence of actuator faults, a novel event‐triggered mechanism is developed to reduce wear of actuator and ensures the performance of system. On this basis, we employ backstepping technique and an improved artificial potential function methods to develop an event‐triggered obstacle avoidance control scheme with the capability of fault tolerance. This proposed control strategy ensures the uniform ultimate boundedness of tracking errors. In contrast to existing results, the presented control strategy simultaneously holds the performance of obstacle avoidance and fault tolerance and reduce the update frequency of actuators. The effectiveness of the provided control scheme has been confirmed through simulation. [ABSTRACT FROM AUTHOR]

This research focuses on the issue of adaptive fuzzy fault-tolerant position tracking for permanent magnet synchronous motors (PMSMs) subject to finite-time prescribed performance. An improved finite-time prescribed performance control strategy, in which unknown nonlinear functions can be tackled via fuzzy logic systems (FLSs), is presented via incorporating the approach of prescribed performance control with the technique of command filter. In addition, the command filtered method is utilized to conquer the 'explosion of complexity' emerged in the classic backstepping method and the error compensation mechanism is adopted to diminish the error generated by filtering process. Further, the impact of actuator failures is dealt with based on fault-tolerant control. It is proven that the designed controllers not only assure the semi-global boundedness of all the controlled system signals, but also make the output tracking error is preserved in a specified prescribed performance within a finite-time interval. Finally, simulation results are supplied to display the significance and potential of the proposed control technique. [ABSTRACT FROM AUTHOR]

This article designs an exponential convergence tolerant control protocol to solve the stability problem of discrete fuzzy network systems with time-varying communication delays caused by state feedback driven by event-triggered mechanism. In order to reduce the conservatism of the design, firstly, a less conservative bound is obtained by introducing the method of reciprocally convex combination into the bounded treatment of sum inequality. Secondly, a suitable Lyapunov functional, dividing the delays into two intervals, is designed. Then, under the adaptive event-triggered mechanism, a novel exponential convergence control protocol is obtained by applying this boundary and the asynchronous constraint to stability analysis within the interval delay framework. Therefore, this protocol also solve the asynchronous problem of premise variables caused by IT2 T–S fuzzy modeling. Finally, simulation results verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Hypersonic vehicles play a crucial role in various applications and are complex systems that integrate aviation, electronics, computer control, electrical information, and sensing technologies. Owing to this complexity and their harsh working environment, hypersonic vehicles frequently face various faults or failures. Furthermore, these vehicles face a more challenging flight environment and complex dynamic characteristics than traditional aircraft. Building an accurate system model for hypersonic vehicles is considerably difficult. In recent years, extensive research has been conducted on fault diagnosis using deep learning and large datasets. However, the substantial success of deep learning techniques relies on the assumption that sufficient labeled training samples are available. In practical scenarios, problems such as data imbalance, insufficient labeled data, or even the absence of data are frequently encountered. This study investigates zero-shot fault identification for ultra-hypersonic aircraft. In particular, this study focuses on the diagnosis of faults in the flight control system actuators. This study aims to employ deep learning techniques to distinguish whether a specific fault is a loss-of-effectiveness (LoE) or locked-in-place (LiP) fault. In the context of this study, “zero-shot” indicates that no sample data related to the target faults has been included or introduced during the construction of the deep learning model for fault diagnosis. Therefore, the model must rely on alternative methods and features to infer and accurately identify unknown faults for effective fault recognition. To address this problem, artificial descriptions of faults are employed to characterize unknown faults. In particular, a relational network is used to compare the definitions of known fault samples with the descriptions of unknown faults. Furthermore, a deep neural network structure is built by combining convolutional neural networks with long short-term memory networks for feature extraction. Finally, zero-shot fault identification experiments are conducted on a high-hypersonic aircraft with a Winged-cone configuration. Fourteen types of faults, including seven types of LoE faults and seven types of LiP faults, are considered. The highest accuracies range from 81.44% to 89.92% over different types of faults. This demonstrates that it is possible to diagnose and classify different types of faults without training samples, realizing the initial objectives of the fault description-based method. This diagnosis is based on human-defined fault descriptions that allow for fault classification. The proposed zero-shot fault identification method aircraft can mitigate risks, enhance operational reliability, and improve safety in high-hypersonic aircraft operations.

In this paper, the bipartite consensus tracking control problem is investigated for a class of nonlinear fractional-order multi-agent systems (FOMASs) with unknown dynamics, actuator faults, and input nonlinearities. Based on the adaptive backstepping technique, an adaptive bipartite consensus tracking control framework is constructed for FOMASs, where both cooperative and competitive relationships among agents are implemented. Furthermore, a fault compensation mechanism is proposed to relax the restriction on the number of actuators that can fail, and allow the existence of different types of input nonlinearities for each actuator. In addition, an improved adaptive self-triggered control mechanism that can be dynamically adjusted depending on the bipartite consensus error is extended to FOMASs to save network resources. Then, by means of the fractional-order Lyapunov stability criterion, it is theoretically proved that the proposed control scheme ensures that all signals of the closed-loop systems are bounded and drives the bipartite consensus error into a desired neighborhood of the origin. Finally, simulation results are provided to confirm the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

In this paper, we study the control problem of auto-berthing marine surface vessels (MSVs) within a predefined, finite time in the restricted waters of a port, in the face of internal and external uncertain dynamics and actuator faults. We first use radial basis function neural networks to reconstruct the internal uncertainties of the system; then, using the minimum learning parameter method, we transform the weights of the neural networks, the external disturbances of the system, and the bias fault factors into an indirect single-parameter neural learning mode. We also apply a robust depth information adaptation technique to estimate the upper bound on the composite disturbances online. Dynamic surface control technology alleviates the burden of virtual control derivative calculations. Finite-time convergence of the system is guaranteed by a predetermined finite-time function based on a time-based generator (TBG). Based on these methods, we design a finite-time fault-tolerant auto-berthing control scheme based on TBG. The stability of the system is analysed based on Lyapunov stability theory. Finally, we verify the effectiveness of the proposed control scheme through simulation.

Predefined-time control has experienced substantial advancements in recent years. Nevertheless, the current technology has not achieved widespread adoption in nonlinear multi-agent systems (NMASs), and significant issues pertaining to the input quantization and actuator failures remain unaddressed. This paper investigates the problem of predefined-time consensus control for NMASs with input quantization and actuator faults. Notably, the study takes into account scenarios where each actuator may experience an infinite number of faults. In conjunction with practical predefined-time stability theory, an innovative predefined-time adaptive consensus control method has been developed within the framework of the backstepping method, incorporating the approximation technique of the multi-dimensional Taylor network (MTN). Additionally, by utilizing the characteristics of quantized nonlinear sectors and the structural model of actuator faults, novel adaptive estimation techniques are devised to handle the effects caused by actuator faults and quantized inputs. To further alleviate computational burdens and tackle the issue of computational explosion, a finite-time differentiator is employed to estimate the derivative of the virtual control. The proposed control scheme achieves the desired performance of predefined-time convergence. Rigorous theoretical analyses indicate that the proposed control scheme can drive consensus errors to converge within a small range within a predefined-time, and users have the flexibility to choose the settling time. Moreover, all signals in the closed-loop system remain bounded. Finally, simulation results are provided to validate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

• A novel fault-tolerant controller has been developed for the attitude control of rigid spacecraft based on Q-learning. • This controller obviates the necessity for actuator fault data or extensive fault knowledge. • The controller stability analysis and controller implementation are discussed. This paper presents a novel active fault-tolerant control (FTC) scheme based on reinforcement learning (RL) for rigid spacecraft operating in challenging conditions with simultaneous actuator faults and external disturbances. Initially, the paper outlines the dynamics of a rigid spacecraft afflicted by actuator faults and subject to external disturbances. Subsequently, an observer is designed to swiftly detect actuator faults, ensuring a timely response to fault occurrences. An indirect fault estimator is then employed to estimate the total faults affecting the system. Based on the estimated total faults, the proposed decision mechanism switches the controller from the nominal to the fault-tolerant controller. The proposed fault-tolerant controller is model-free and utilizes the Q-learning algorithm. This Q-learning-based fault-tolerant controller can be implemented online without relying on explicit system models or actuator fault details. Notably, this innovative controller operates independently from fault detection and identification (FDI), utilizing data extracted from system trajectories. The stability of the fault-tolerant controller is established using Lyapunov techniques, providing rigorous validation of its effectiveness in maintaining system stability and achieving satisfactory performance. The performance and adaptability of the proposed approach are assessed through comprehensive simulation studies, emphasizing its capacity to enhance spacecraft fault tolerance in demanding operational scenarios. [ABSTRACT FROM AUTHOR]

This paper focuses on the issue of adaptive quantized event-triggered prescribed performance control for a class of uncertain nonlinear systems subject to actuator faults from a new point of view. First, a so-called preassigned finite-time performance function (PFTPF) is embedded to the developed nonlinear system. Then, by applying the PFTPF and embedding this designed barrier function, a barrier Lyapunov function is given for the adaptive controller deign. Subsequently, by coming up with a nonlinear decomposition of quantization, two quantized event-triggered control laws are depicted, which can reduce the communication burden from the controller to the actuator. It is proved that the designed control protocol guarantees that not only all the signals of the closed-loop system are bounded, but also the constrained error variables tend to the corresponding specified regions in finite time. Finally, simulation results are carried out to address the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The prescribed performance event-triggered fault-tolerant control (FTC) problem is considered for a class of uncertain pure-feedback nonlinear systems with actuator failures. Contrary to the existing results, the novel decoupling condition and the universal construction mode of error transformation are first given. Based on this, a new radical constrained function is used to low-complexity controller design without involving the Nussbaum gain scheme or adaptation parameter updated structure, in spite of nonlinear coupling and event triggering input. It is proved that the desired prescribed accuracy of the closed-loop output tracking can be achieved and the number of event-trigger showed a significant reduction. The effectiveness of theory results is verified by simulation on a numerical example. [ABSTRACT FROM AUTHOR]

This paper aims to address the problem of event‐based adaptive robust control for a class of nonaffine nonlinear systems against actuator faults and unknown deception attacks. Firstly, different from the widely used relative threshold triggering mechanism, an improved event‐triggered mechanism with the time‐varying function threshold is devised for effectively saving more communication costs between controller and actuator. Then, based on a class of modified filter‐based coordinate transformations and the improved event‐triggered mechanism, the intermediate virtual control laws are devised to construct actual controllers, which not only guarantees robust stabilization of the system but also relaxes the universal assumptions on control coefficients and attack weights. Besides, the design challenges resulting from the presence of coupling terms in the nonaffine nonlinear system are successfully overcome by virtue of mean value theorem and the property of fuzzy basis function. Theoretical analysis proves that all signals of the closed‐loop system are semiglobally uniformly ultimately bounded (SUUB) and the Zeno behavior can be averted. Finally, the effectiveness of the proposed strategy is verified via comprehensive simulation results. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel spacecraft attitude adaptive tracking control strategy that addresses the challenges posed by unknown inertia parameters, bounded unstructured uncertainties, and various actuator faults. To mitigate the adverse effects of these multiple uncertainties, our proposed control algorithm utilizes a simple tracking‐error feedback and compensation term, which only requires two adaptive parameters. This approach ensures asymptotic tracking convergence by employing an infinitely integrable inequality that incorporates saturation functions. As a result, the complexity of controller design and computational burden are significantly reduced. Finally, several numerical simulations are conducted to validate the feasibility and superiority of our proposed controller. [ABSTRACT FROM AUTHOR]

In this article, an adaptive continuous sliding mode control (SMC) scheme is presented for the trajectory tracking problem of mechanical systems with parameter uncertainties, external disturbances and actuator faults. The hyperbolic tangent function is widely used to replace the signum function in SMC to ensure that the robust term is continuous and to reduce chattering. Since such an approach is difficult for SMC schemes with adaptive gain to induce system stability through Lyapunov functions, we reconstruct the hyperbolic tangent function by taking both the adaptive control gain and sliding variables as inputs. The designed gain dynamics do not require a priori upper bound on lumped uncertainties, including parameter uncertainties, external disturbances and actuator faults, and ensure no overestimated gains. Besides, an adaptive dual-layer super-twisting (ADLST) observer is adopted to accurately estimate unmeasurable velocities, which achieves the synthesis of an adaptive sliding mode observer and the continuous SMC method with adaptive gain. It is proven through the Lyapunov function that all closed-loop signals are ultimately bounded. Comparative simulations are conducted on a two-link rigid manipulator to demonstrate the effectiveness of the adopted observer and the proposed scheme. [ABSTRACT FROM AUTHOR]

This article investigates the fault detection and fault-tolerant control problem for an unmanned surface vehicle exposed to wave-induced disturbances and actuator faults in the physical layer, and delays and deception attacks in the cyber layer. First, a comprehensive vehicle model that includes physical disturbances, faults, time-varying delays, and Bernoulli random variable–based deception attacks is established. Second, an integrated fault detection filter and fault-tolerant controller design are developed to simultaneously provide a high degree of sensitivity to actuator faults and robustness and stability against cyber-physical threats (disturbances, faults, delays, and attacks). Delays and deception attacks are assumed to occur on the channel from fault detection filter to fault-tolerant controller. Finally, the performance and advantages of the integrated fault detection filter and fault-tolerant controller method with the solvability of inequality matrices are evaluated via comparative simulations in the unmanned surface vehicle with both low and high forward speeds. [ABSTRACT FROM AUTHOR]

This paper presents an event-triggered sliding mode fault-tolerant control method for networked control systems with actuator faults and external disturbance. First, an event-triggered scheme in the networked control systems is proposed to reduce the number of data transmission. In addition, considering the event-triggering, a sliding mode surface based on the system structure is constructed. Besides, to obtain the controller parameters that meet the H ∞ performance, the bounded real lemma in the form of linear matrix inequality is obtained using the Lyapunov functional. In addition, a sliding mode fault-tolerant control is designed to guarantee that the system can still run stably under the condition of faults and disturbances. Finally, the simulation results of gas turbine system are given to verify the feasibility of the theoretical method. [ABSTRACT FROM AUTHOR]

This paper investigates the prescribed performance adaptive fault‐tolerant control problem for nonlinear systems with actuator faults and input dead‐zone (IDZ). The partial loss of effectiveness fault and bias fault are considered. First, the asymmetric nonlinear IDZ is transformed into an affine function in the backstepping process. Second, the radial basis function neural network technique is applied to deal with the unknown uncertainty of the studied system. The state observer is designed to estimate the unmeasurable states in the nonlinear systems. Third, a fault‐tolerant controller is developed such that all signals are semi‐globally uniformly ultimately bounded for the closed‐loop system, and the tracking error remains within the specified performance requirements even though the actuator faults and IDZ occur. Finally, an electromechanical system example is provided to exhibit the utilizability of the obtained control technique. [ABSTRACT FROM AUTHOR]

This article addresses the event‐triggered bipartite consensus problem for high‐order nonlinear multi‐agent systems with antagonistic interactions. Intermittent actuator faults covering bias and partial loss of effectiveness are also investigated. To achieve consensus, a relative‐output‐dependent compensator is constructed based on a time‐varying gain. Subsequently, by integrating adaptive compensation mechanisms as well as an event‐triggered mechanism, an output‐feedback fault‐tolerant consensus protocol is proposed for the sake of fault compensation. It is worth noting that a positive ℒp$$ {\mathcal{L}}_p $$ function is embedded in the triggering function to adjust the triggering threshold and exclude the Zeno behavior. Under a signed digraph, it is shown that the designed protocol enables all agents to achieve consensus with opposite signs, even if the total number of unknown faults is infinite. Finally, a simulation is presented to illustrate the validity of the obtained protocol. [ABSTRACT FROM AUTHOR]

Summary: In this paper, the fixed‐time anti‐synchronization and synchronization fault‐tolerant control problem is considered for two identical Liu‐Chen‐Liu chaotic systems with uncertain parameters. With the help of fixed‐time stability theory and adaptive backstepping method, we propose two novel adaptive controllers. In the control scheme proposed in this paper, the adaptive backstepping technique is used to deal with the unknown parameters contained in the chaotic systems. Besides, the piecewise functional method is used to solve the singularity problem in the controller design process. The designed controllers achieve anti‐synchronization and synchronization of two identical Liu‐Chen‐Liu chaotic systems within the preset time interval. Simulation results are conducted to show the effectiveness of two proposed adaptive controllers. [ABSTRACT FROM AUTHOR]

This study addresses the problem of cooperative control design for a group of car-like vehicles encountering fading channels, actuator faults, and external disturbances. It is presumed that certain followers lack direct access to the states of the leader via a directed graph. This arises challenges in maintaining synchronization and coordination within the network. The proposed control strategy utilizes non-singular fast terminal sliding mode control to accelerate consensus tracking and enhance the convergence of the overall system. This controller is designed to mitigate the impact of actuator faults in the presence of fading channels in the communication network. The effects of such issues on team performance are rigorously analyzed. Based on the Lyapunov stability principle, it has been demonstrated that the controller is capable of providing satisfactory performance for the entire system despite these challenges. Moreover, vehicle synchronization can be effectively maintained. Numerical simulations are conducted to verify the theoretical findings. [ABSTRACT FROM AUTHOR]

This paper addresses a class of nonstrict-feedback stochastic nonlinear systems. It addresses the impact of backlash-like hysteresis as well as actuator faults simultaneously. Radial basis function neural networks (RBFNNs) are used specifically to approximate unknown nonlinear functions. Furthermore, a backstepping approach is used to design a neural network-based adaptive fault-tolerant controller for the system. The suggested control methodology compensates effectively for the negative impacts of actuator faults and backlash-like hysteresis. Based on the Lyapunov stability theory, the proposed controller ensures that all closed-loop system signals are semi-globally uniformly ultimately bounded (SGUUB) and the system output tracks the reference signal with bounded tracking error. Furthermore, a numerical example and a real-world example of a single-link manipulator demonstrated the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of adaptive control for nonstrict-feedback nonlinear systems that involve input delay, actuator faults, and external disturbance. To deal with the complexities arising from input delay and unknown functions, we have incorporated Pade approximation and radial basis function neural networks, respectively. An adaptive controller has been developed by utilizing the Lyapunov stability theorem and the backstepping approach. The suggested method guarantees that the tracking error converges to a compact neighborhood that contains the origin and that every signal in the closed-loop system is semi-globally uniformly ultimately bounded. To demonstrate the efficacy of the proposed method, an electromechanical system application example, and a numerical example are provided. Additionally, comparative analysis was conducted between the Pade approximation proposed in this paper and the auxiliary systems in the existing method. Furthermore, error assessment criteria have been employed to substantiate the effectiveness of the proposed method by comparing it with existing results. [ABSTRACT FROM AUTHOR]

This paper is concerned with the transformed parameter-dependent H∞ controller design for semi-Markovian jump linear parameter varying (S-MJLPV) systems under actuator saturation and faults. In the S-MJLPV system, the semi-Markov process transition rate is time-varying during the semi-Markov process and a plant includes time-varying parameters which are bounded and measurable in magnitude. For more practical analysis and synthesis of the S-MJLPV systems, a time-varying actuator fault model and actuator saturation of the controller are considered into account simultaneously. The primary goal of this paper is to develop a transformed parameter-dependent control that makes the closed-loop system stochastically stable with H∞ performance index γ and provides less conservative results against actuator saturation and faults. Based on the mode-dependent Lyapunov function, new sufficient conditions are obtained to ensure that the stochastic stability of S-MJLPV systems. Eventually, an example based on the turbofan-engine model is presented to demonstrate the efficacy of our proposed methods. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of adaptive control for nonstrict-feedback nonlinear systems that involve input delay, actuator faults, and external disturbance. To deal with the complexities arising from input delay and unknown functions, we have incorporated Pade approximation and radial basis function neural networks, respectively. An adaptive controller has been developed by utilizing the Lyapunov stability theorem and the backstepping approach. The suggested method guarantees that the tracking error converges to a compact neighborhood that contains the origin and that every signal in the closed-loop system is semi-globally uniformly ultimately bounded. To demonstrate the efficacy of the proposed method, an electromechanical system application example, and a numerical example are provided. Additionally, comparative analysis was conducted between the Pade approximation proposed in this paper and the auxiliary systems in the existing method. Furthermore, error assessment criteria have been employed to substantiate the effectiveness of the proposed method by comparing it with existing results.

Pendubot has been widely applied as a benchmark platform for control research and education. In this paper, a novel adaptive fuzzy hierarchical sliding mode controller (AFHSMC) is proposed for the pendubot under actuator faults and uncertainties. The proposed controller is designed by combining hierarchical sliding mode control (HSMC), fuzzy logic control (FLC), and balancing composite motion optimization. The proposed controller preserves many advantages such as having a straightforward structure, simple implementation, chattering reduction, and high precision and robustness. The stability of the proposed controller is ensured by using the Lyapunov approach. To verify the control performance, various numerical simulations and experiments are conducted on a pendubot under conditions that involve actuator faults and uncertainties. Compared to the conventional HSMC and FHSMC controllers, the proposed AFHSMC improves by 0.43% and 0.38% for tracking precision of the first link's angle estimate, 3.26% and 0.08% for the second link's angle estimate when influenced by uncertainties, as well as 65.23% and 12.24% for the first link, 83.95% and 16.15% for the second link when influenced by faults.

A guaranteed performance event-triggered adaptive consensus control is established for uncertain multiagent systems under time-varying actuator faults. To eliminate the impact caused by actuator faults, an adaptive neural network compensation strategy is developed. Simultaneously, by implementing the asymmetric barrier Lyapunov function and transform function, a prescribed time consensus control with guaranteed performance, is constructed. Furthermore, to reduce the frequency of information transmission, an adjustable switching event-triggered control (ASETC) is proposed by using a modified hyperbolic tangent function. It combines the advantage of the relative threshold strategies and the characteristics of the hyperbolic tangent function, giving better flexibility in saving network resources and guaranteeing system performance. By applying the constructed control method, systems with prescribed performance consensus in a prescribed time are achievable while limited network resources and unknown time-varying faults are present. Some simulation examples implemented in MATLAB (R2022a) are given to demonstrate the above results. [ABSTRACT FROM AUTHOR]

This article discusses the problem of stabilizing the attitude control of a quadrotor system, which is subject to uncertainty, external disturbances, and sensor and actuator faults. To address these challenges, the control stage employs universal approximators, such as fuzzy systems, which estimate the system's uncertainties and eliminate nonaffine nonlinear actuator faults. In addition, the particle swarm optimization technique is used to adjust the adaptive parameters and fuzzy initial values. The robust control term is carefully designed to handle approximation errors, time-varying sensors, and external disturbances. To solve the issue of the unavoidable algebraic loop during the actuator approximation phase, a Butterworth low-pass filter is integrated. This approach automatically deals with external disturbances, and no further approximation is necessary. Furthermore, the controller can be reconfigured online to enable fast-fault compensation without requiring a fault detection or isolation unit. To prove the global stability and boundedness of all signals in the closed-loop system, Lyapunov theory is used. [ABSTRACT FROM AUTHOR]

This article addresses the issue of event‐triggered security control for nonlinear cyber‐physical systems, characterized by Denial‐of‐Service (DoS) attacks, time‐varying delays, and actuator faults. These complexities are captured using the Takagi–Sugeno fuzzy model. Significantly, this study focuses on DoS attacks targeting the controller‐to‐actuator channel, and subsequently introduces a model for actuator faults that considers the prolonged utilization or degradation of component parts. To optimize communication resource allocation, a model‐independent event‐triggered mechanism is proposed. Utilizing the Lyapunov–Krasovskii function approach, the objective of achieving stochastic stability is transformed into a linear matrix inequalities problem, thus deriving a set of sufficient conditions. The efficiency and validity of the proposed algorithms is demonstrated through simulation results. [ABSTRACT FROM AUTHOR]

This paper addresses the finite-time control problem of a class of uncertain nonlinear systems subject to input saturation and actuator faults. To approximate the unknown system states, a fuzzy state observer is established where fuzzy logic systems are utilized to estimate the unknown nonlinearities. A nonlinear disturbance observer to estimate the external disturbance of the system. A new finite-time adaptive fuzzy controller is constructed together with the proposed disturbance and state observers, which can guarantee the tracking error into a small neighborhood around zero within finite time. To avoid the tedious and arithmetic problems brought by the traditional backstepping control, the dynamic surface technique is applied, which can effectively reduce the computation burden. It can be proved that not only the boundedness of the closed-loop system states can be guaranteed but also the tracking error is regulated into a small range near the equilibrium in finite time, despite unknown actuator faults and input saturation. Finally, the simulations of two-stage chemical reactor nonlinear system are conducted to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Summary: This paper investigates the fuzzy adaptive time‐varying formation control problem for fully‐actuated unmanned surface vehicles (USVs) systems. The studied USVs systems include uncertain dynamics and actuator faults. Fuzzy logic systems (FLSs) and disturbance observer are employed to estimate uncertain nonlinear dynamics in system model and external time‐varying disturbances, respectively. Meanwhile, the prescribed performance control (PPC) technique is applied to construct the constraint requirements of collision avoidance and connectivity preservation in time‐varying formation control. Additionally, a new robust adaptive fault‐tolerant control (FTC) algorithm is presented. Hence, by the aid of adaptive backstepping technique with performance functions, a robust fuzzy adaptive time‐varying formation FTC scheme of time‐varying formation is developed. The proposed formation control scheme not only has the advantages of avoiding collision and preserving connectivity, but also ensures all the signals are uniformly ultimately bounded (UUB). Finally, the simulation results are provided to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of neuroadaptive fixed-time control for full-state constrained strict-feedback nonlinear systems subject to actuator faults. A fixed-time control strategy combined with barrier Lyapunov functions and neuroadaptive backstepping is proposed, and a neural network is employed to approximate the packaged unknown nonlinear terms and nonlinear actuator faults. By constructing barrier Lyapunov functions, it can be ensured that none of the strict-feedback systems' states will transgress their constraint bounds. Additionally, a fixed-time controller is designed such that all the signals in the closed-loop system are bounded, and the output is driven to track the reference signal to a small neighborhood within a fixed time. The benefits and feasibility of the proposed control method are also confirmed by simulations. [ABSTRACT FROM AUTHOR]

Due to the widespread application and significant investment required for a single crane, there is an increased emphasis on crane safety and service life. Fault-tolerant control as an effective solution to unexpected faults has been widely studied recently. However, most fault-tolerant control methods require redundant actuators or a complex design process, which is unsuitable for the tower crane. Following these problems, a fault-tolerant controller based on an adaptive backstepping technique is proposed. Firstly, the system states are reconstructed and written as a cascade system. Secondly, a fixed-time convergence optimized backstepping controller is proposed to achieve smooth control of the tower crane without generating sudden or abrupt values. Then, an adaptive approach has been proposed to update fault parameters for the crane system in case of a sudden fault occurrence. Finally, after conducting comparison tests, it has been determined that the proposed controller not only performs exceptionally well in terms of position accuracy and swing elimination, but also maintains a satisfactory control performance when faced with sudden faults. • The jib and trolley positioning and load swing suppression problems for tower cranes are investigated. • The adaptive backstepping fault-tolerant controller is designed. • The control performance is demonstrated by comparative experiments. [ABSTRACT FROM AUTHOR]

We consider the distributed bipartite consensus problem of general linear multi-agent systems (MASs) with structurally balanced symbolic graphs. The communication topology is fixed topology or Markovian switching topology which follows a continuous time Markov process. Different from the traditional multi-agent fault-tolerant consensus control, a distributed consensus control protocol is designed for multi-agent systems with both competitive and cooperative states. A distributed extended state observer and distributed bipartite consensus based on relative neighbor information are designed with the help of algebraic graph theory and Lyapunov stability theory. The bipartite consensus error and estimation error converge to zero under the action of the bipartite control protocol. In the end, the superiority of the theoretical method is verified by simulation examples.

This paper investigates a fuzzy adaptive fast finite-time control method for nonlinear pure-feedback systems (PFSs) with time-varying actuator faults. Using the mean-value theorem (MVT) to handle the non-affine functions in the system and to effectively decouple the control input signals. By integrating the approximation ability of fuzzy logic systems (FLSs), the fast finite-time control theory, and the backstepping techniques, a fuzzy-based fast finite-time fault-tolerant control method is proposed. Meanwhile, the fault-tolerant controller designed in this paper can effectively compensates the actuator faults. The theoretical analysis verifies that the strategy ensures the convergence of the tracking error and all closed-loop signals are bounded in fast finite-time. Finally, the effectiveness and feasibility of the proposed control strategy are verified by two numerical simulations.

This paper investigates the finite-time attitude consensus tracking problem for multiple spacecraft systems in the presence of input quantization, external disturbances and actuator faults. First, the input quantization is decomposed into a combination of a linear term and a disturbance-like term. Then, by integrating the “adding a power integrator” technique with the adaptive bound estimation approach, a finite-time attitude consensus protocol is proposed under the hypothesis that the time-varying reference attitude is available only to a subset of the follower spacecrafts. It is shown that the proposed attitude consensus control law can guarantee a group of spacecrafts to simultaneously track a common time-varying reference attitude in finite time despite the presence of input quantization and actuator faults. Remarkably, the proposed attitude consensus control scheme does not require the upper bounds of the time varying faults and external disturbances to be known, and also allows the parameters of quantizer to be freely adjusted according to system performance. Finally, simulation studies are carried out to validate the efficiency of the proposed algorithm.

Four-wheel independent drive and four-wheel independent steering (4WID-4WIS) vehicles provide increased redundancy in fault-tolerant control (FTC) schemes, enhancing heterogeneous fault-tolerant capabilities. This paper addresses the challenge of maintaining vehicle safety and maneuverability in the presence of actuator faults in autonomous vehicles, focusing on 4WID-4WIS systems. A novel unified hierarchical active FTC strategy is proposed to handle various actuator failures. The strategy includes an upper-layer motion controller that determines resultant force requirements based on trajectory tracking errors and a middle-layer allocation system that redistributes tire forces to fault-free actuators using fault information. This study, for the first time, considers multi-fault scenarios involving longitudinal and lateral coupling, calculating FTC boundaries for each fault type. Additionally, a fault tolerance index is introduced for 256 fault scenarios, using singular value decomposition to linearly represent the vehicle attainable force domain. Based on this, an adaptive velocity planning strategy is developed to balance safety and maneuverability under fault conditions. Matlab 2021a/Simulink and Carsim 2019 co-simulation results validate the proposed strategies, demonstrating significant improvements in fault-tolerant performance, particularly in complex and emergency scenarios.