Time-varying Actuator Faults Research Articles

Observer and fault reconstruction-based control design for interval type-2 fuzzy systems

In this study, the finite-time stabilization and multiple faults reconstruction issues are scrutinized for nonlinear control systems in the presence of time-varying state delay, actuator faults and sensor faults in which the underlying system is approximated by dint of fuzzy approach. To be specific, the interval type-2 (IT2) fuzzy technique is deployed in tandem with a non-parallel distribution compensation scheme to linearize such a faulty system. Further, the existence of unknown inputs in both the system and its output are effectively reconstructed with the aid of IT2 fuzzy-based observer, namely the IT2 fuzzy unknown input (UI) observer. Simultaneously, the designed observer also ensures the reconstruction of system state vectors that are not able to be measured. With the help of this reconstruction information, a fault-tolerant control scheme is established to maintain the performance of the addressed system even in the occurrence of unknown faults. Particularly, in this work, the sampling strategy is incorporated with the developed controller design. Overall, the proposed IT2 fuzzy UI observer-based sampled fault-tolerant controller guarantees the stability of the resulting system within a finite-time duration. Furthermore, the Lyapunov stability theory enables to formulate the delay-dependent adequate constraints in the framework of linear matrix inequalities. On the grounds of established criteria, the relations are also constructed for obtaining the controller and observer gain matrices. Eventually, the simulation study is conducted with two numerical examples to validate the theoretical and practical significance of the developed control procedure.

Hybrid robust fault detection and isolation of satellite reaction wheel actuators

In this paper, a combined robust fault detection and isolation scheme is studied for satellite system subject to actuator faults, external disturbances, and parametric uncertainties. The proposed methodology incorporates a residual generation module, including a bank of filters, into an intelligent residual evaluation module. First, residual filters are designed based on an improved nonlinear differential algebraic approach so that they are not affected by external disturbances. The residual evaluation module is developed based on the suggested series and parallel forms. Further, a new ensemble classification scheme defined as blended learning integrates heterogeneous classifiers to enhance the performance. A wide range of simulations is carried out in a high-fidelity satellite simulator subject to the constant and time-varying actuator faults in the presence of disturbances, manoeuvres, uncertainties, and noises. The obtained results demonstrate the effectiveness of the proposed robust fault detection and isolation method compared to the traditional nonlinear differential algebraic approach.

Robust Self-Learning Fault-Tolerant Control for Hypersonic Flight Vehicle Based on ADHDP

In this article, a robust self-learning fault-tolerant control (FTC) strategy is proposed to deal with the tracking control problem of the hypersonic flight vehicle (HFV) with uncertainties, actuator faults, and external disturbances. First, an adaptive baseline controller is constructed to achieve stable tracking, in which neural networks are introduced to approximate the unknown dynamics, adaptive laws are formulated to compensate the unknown lumped disturbances, and the Nussbaum technique is applied to address the time-varying actuator faults. Then, to improve the command tracking performance of the baseline controller, a data-driven auxiliary controller which can adaptively adjust the action–critic network weights over time along with the tracking deviation to obtain the optimal control signals in the sense of performance index is developed based on action-dependent heuristic dynamic programming technology. Finally, a comprehensive robust self-learning FTC law is constructed by synthesizing the baseline controller and the auxiliary controller, which leads to good robustness and tracking performance of the closed-loop HFV system. The stability and the superiority of the proposed control algorithm are verified by the Lyapunov theory and comparative numerical simulations, respectively.

Adaptive optimal sliding-mode fault-tolerant control for nonlinear systems with disturbances and estimation errors

This paper gives a fault-tolerant control scheme concerning with the optimal and near-optimal system performance for a class of nonlinear systems with time-varying actuator faults, time-varying disturbances, and identification errors of the neural network-based identifier. In this paper, the adaptive dynamic programming method is directly used to design the optimal sliding surface, so that the designer can design the target dynamics of the sliding mode in advance. In addition, the barrier function-based sliding-mode control is used to cope with the bounded but boundary-unknown estimation errors of the identifier, and the method ensures that the sliding-mode variable converges to the designer’s predefined neighborhood of zero in finite time and the control gain is not overestimated. Specifically, first, by adaptive dynamic programming, near-optimal sliding surface is designed in the sense of the quadratic optimal criterion. Second, a neural network identifier is designed to model the lumped uncertainties including actuator faults and disturbances. Third, a barrier function-based adaptive sliding-mode control is used to cope with the identification errors of the neural network identifier and has no control gain overestimation, which can guarantee to confine the sliding variable to a predefined vicinity of the proposed sliding surface. For this system with faults and disturbances, it is shown that near-optimal property is achieved by this scheme, the chattering phenomenon is effectively suppressed, and the reconstruction error of the neural network identifier with unknown boundary is effectively handled by the adaptive sliding-mode control based on the barrier function. The proof of stability is given by Lyapunov’s direct method and the effectiveness of this control scheme is verified by applying it to the spacecraft attitude system.

Distributed Fault-Tolerant Containment Control Protocols for the Discrete-Time Multiagent Systems via Reinforcement Learning Method.

This article investigates the model-free fault-tolerant containment control problem for multiagent systems (MASs) with time-varying actuator faults. Depending on the relative state information of neighbors, a distributed containment control method based on reinforcement learning (RL) is adopted to achieve containment control objective without prior knowledge on the system dynamics. First, based on the information of agent itself and its neighbors, a containment error system is established. Then, the optimal containment control problem is transformed into an optimal regulation problem for the containment error system. Furthermore, the RL-based policy iteration method is employed to deal with the corresponding optimal regulation problem, and the nominal controller is proposed for the original fault-free system. Based on the nominal controller, a fault-tolerant controller is further developed to compensate for the influence of actuator faults on MAS. Meanwhile, the uniform boundedness of the containment errors can be guaranteed by using the presented control scheme. Finally, numerical simulations are given to show the effectiveness and advantages of the proposed method.

Adaptive control for a class of flexible mechanical systems with bending deformation and torsion deformation in the presence of nonlinear time-varying actuator faults and unknown control directions

Vibration control problem of a class of flexible mechanical systems with bending deformation and torsion deformation in the presence of nonlinear time-varying actuator faults and unknown control directions is considered in this paper. The dynamics model of this kind of flexible mechanical system, which includes two kinds of coupled vibration deformation and unknown nonlinear disturbances, is composed of some partial differential equations. In order to overcome the control difficulty brought by unknown control directions and time-varying nonlinear actuator faults, a Nussbaum function based adaptive boundary controller is designed to suppress the bending and twist vibration deformations of the mechanical system. The stability of the system is proved by Lyapunov direct method, and the effectiveness of the proposed control scheme is verified by numerical simulation at the end of this paper.

Predefined-time fault-tolerant attitude control for tailless aircraft considering actuator input saturation

This paper proposes a predefined-time fault-tolerant attitude control methodology to tailless aircraft in the presence of external disturbance, input saturation, and possibly the infinite number of time-varying actuator faults. First, a novel predefined-time performance function is designed, subsequently, a smooth function and bound-based adaptation laws are developed to compensate for the adverse influence caused by actuator fault and input saturation simultaneously. Such a design is less constrained to faults, requires less computation burden, and can be applied to a larger scope of fault scenarios. Besides, a piecewise continuous Lyapunov function is introduced to proceed with the stability analysis of the closed-loop system, and it has been analytically proved that the attitude tracking errors can converge to a predefined residual set within a predefined time while guaranteeing that actuator fault and input saturation are well handled. Finally, comparative simulations are carried out to validate the effectiveness and superiority of the developed control scheme.

Finite-Time Output Feedback Cooperative Formation Control for Marine Surface Vessels With Unknown Actuator Faults

This article studies the finite-time output-feedback cooperative formation problem of multiple marine surface vessels (MSVs) without using velocity information, where each MSV contains unknown time-varying actuator faults, ocean disturbances, and model uncertainties. Considering the performance specifications and requirements in practice, the formation errors and velocities are also required to change in a predefined compact set. Aiming at the challenge of unknown input gain caused by unknown time-varying multiplicative faults, a novel nonlinear extended state observer with an adaptive law is first constructed, which cannot only recover unmeasurable velocities from position-heading information but also simultaneously compensate for ocean disturbances, model uncertainties, and additive faults. Subsequently, combining the barrier Lyapunov function and the nonlinear tracking differentiator technique, a unified finite-time output-feedback cooperative control framework is developed, including both kinematics and kinetics, to prevent constraint deviation and avoid the computational complexity of the traditional backstepping method. Using finite-time stability theory, the designed output feedback cooperative controller can ensure that the desired cooperative performance is achieved within a finite time and tracking errors converge to a small neighborhood of the origin while ensuring that the closed-loop system is semiglobally uniformly ultimately bounded. Simulation examples are shown to demonstrate the effectiveness of the proposed control scheme.

Adaptive contact-force control and vibration suppression for constrained flexible manipulator with unknown control direction and time-varying actuator fault

A contact-force control and vibration suppression problem for a constrained one-link flexible manipulator with unknown control direction and time-varying actuator fault is discussed in this paper. The manipulator is described by a Partial Differential Equation model. The Nussbaum function is used to solve the unknown control direction and time-varying actuator fault problems. A Nussbaum function provides an adaptive boundary controller constructed to control the contact force to a target and simultaneously eliminate the vibration of the flexible manipulator. The Variable Separation Technique is used along with system boundary conditions to prove the necessary existence and uniqueness of the system solution more concisely than using the common C 0 -semigroup contractions method. Because force sensors are not necessary, the noise caused by a force sensor can be avoided. Numerical simulations verify the effectiveness of the proposed controller-based solution.

Finite-time quantized dynamic event-triggered control for cluster synchronization of Markovian jump complex dynamic networks with time-varying delays and actuator faults

In this article, by employing the quantized dynamic event-triggered (ET) and adaptive fault-tolerant control mechanisms, the finite-time cluster synchronization (FTCS) criterion of Markov jump complex dynamic networks (MJCDNs) with actuator faults is established. A kind of novel adaptive controller is designed and appropriate Lyapunov functions are applied to eliminate the effectiveness of actuator faults. Further on historical ET schemes, a more universal dynamic ET control mechanism for MJCDNs is considered in this article, which can reduce the network load of MJCDNs more effectively. Meanwhile, the Zeno behavior is ruled out availably. Finally, the numerical simulation example is presented to verify the validity of the theory.

Synchronization of complex dynamical networks subjected to actuator faults and periodic scaling attacks using probabilistic time-varying delays

In this paper, the synchronization problem of complex dynamical networks (CDNs) subjected to actuator faults and periodic scaling attacks using probabilistic time-varying delays is investigated. In order to handle the fault effects in actuators of proposed complex networks, an actuator fault model is considered. The major objective of this paper is to construct a memory state feedback controller with time-varying actuator faults that are asymptotically synchronized in the mean square sense to defend against periodic scaling attacks. By establishing suitable Lyapunov-Krasovskii functionals and applying Wirtinger-based integral inequality, sufficient conditions are derived in terms of linear matrix inequalities (LMIs) for the proposed system. Moreover, a time-varying delay is supposed to depend on probabilistic distribution conditions. Thus, the resulting criteria that emerge are based on the amount of the delay as well as the probability that the delay will have values at certain intervals. At last, the efficiency of our proposed work is demonstrated by two numerical simulations.

Feasibility Conditions-Free Prescribed Performance Decentralized Fault-Tolerant Neural Control of Constrained Large-Scale Systems

This article investigates the command filter-based decentralized prescribed performance adaptive fault-tolerant compensation control strategy for uncertain nonlinear large-scale systems subject to asymmetric time-varying full-state constraints. Via integrating the performance function with command filter-based backstepping technique, the prescribed performance control problem is addressed, under which the complexity of controller design is reduced. Under the prescribed performance control framework, the nonlinear transformed function is constructed so as to ensure that the asymmetric time-varying full-state constraints free from feasibility conditions imposed on virtual control signals are not violated. Besides, the effect of infinite number of time-varying actuator faults is compensated with the aid of projection adaption design. Furthermore, based on the piecewise Lyapunov function analysis, it is rigorously testified that entire involved signals are bounded, desired constraints are not breached and tracking errors within the predefined domains. Finally, the effective performances of the developed control algorithm are confirmed by some simulation results.

A controller management scheme for active fault-tolerant tracking: Linear discrete-time systems

This paper proposes an active fault-tolerant control (FTC) approach based on the controller management and virtual actuator idea for linear discrete-time systems subject to unknown L2-bounded disturbances, input constraint, and time-varying additive actuator faults. The closed-loop faulty system, which includes the modified nominal controller, the fault and state estimator, and the virtual actuator, suppresses the effects of disturbances and faults, while ensuring input-constraint satisfaction. The management of the nominal controller is performed through an online optimization method – in the form of a standard quadratic programming problem – by manipulating the reference input and intervening in the nominal controller evolution. The proposed method proves the input-to-state stability (ISS) criterion of the overall closed-loop faulty system. The problem of minimizing the ultimate bound of the ISS criterion is formulated in terms of tractable linear matrix inequality (LMI) conditions that allow the fault and state estimation errors to converge to a small neighborhood of the origin. To illustrate the capabilities and advantages of the proposed control strategy, comparative simulation results are presented for a flexible joint robotic system, tracking control of a DC motor’s angular velocity, and the multivariable VTOL aircraft.

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.

Finite-time guaranteed cost control for stochastic nonlinear switched systems with time-varying delays and reaction-diffusion

This study considers the finite-time guaranteed cost control problem for a class of stochastic nonlinear switched systems (SNSSs) with time-varying delays, reaction-diffusion and actuator faults. The reliable control strategy is designed to finite-time stabilization for SNSSs. By virtue of the Lyapunov method, Neumann boundary condition, some famous inequality techniques and average dwell-time approach, sufficient criterion are obtained to ensure finite-time stability of proposed controlled systems. We investigate the finite-time stabilization results by designing the control gain matrices for reliable controller. The sufficient criteria are expressed in terms of linear matrix inequalities (LMIs) that can be verified by MATLAB LMI toolbox. Furthermore, a new sufficient criterion is presented to guarantee the finite-time cost control of proposed controlled systems. Based on this criterion, the effects of control gains, switching signals and time-varying delays on finite-time stability are also analysed. At last, numerical simulations are presented to illustrate the efficiency and superiority of designed reliable controller.

Distributed Cooperative Fault-Tolerant Control of High-Speed Trains With Input Saturation and Actuator Faults

In this work, a novel distributed fault-tolerant control strategy is proposed for cooperative operation of high-speed trains (HSTs) subject to unmodeled dynamics, time-varying external disturbances, input saturation and actuator partial failures. The cooperative control of HSTs is first transformed to the consensus problem of a multi-agent system. Then, the distributed control laws that are equipped with a command filter and an adaptive neural network are developed to achieve the consensus task among the HSTs with predefined displacement and speed profiles. Further, an auxiliary dynamical system is introduced to compensate for the influence of input saturation. Moreover, an adaptive failure compensation strategy is designed to deal with the unknown and time-varying actuator faults. The convergence of the proposed controller is analyzed rigorously by applying the Lyapunov theorem, and the effectiveness is demonstrated by numerical simulations.

Dynamic event-triggered adaptive finite-time consensus control for multi-agent systems with time-varying actuator faults.

In this study, the adaptive finite-time leader-following consensus control for multi-agent systems (MASs) subjected to unknown time-varying actuator faults is reported based on dynamic event-triggering mechanism (DETM). Neural networks (NNs) are used to approximate unknown nonlinear functions. Command filter and compensating signal mechanism are introduced to alleviate the computational burden. Unlike the existing methods, by combining adaptive backstepping method with DETM, a novel finite time control strategy is presented, which can compensate the actuator efficiency successfully, reduce the update frequency of the controller and save resources. At the same time, under the proposed strategy, it is guaranteed that all followers can track the trajectory of the leader in the sense that consensus errors converge to a neighborhood of the origin in finite time, and all signals in the closed-loop system are bounded. Finally, the availability of the designed strategy is validated by two simulation results.

Fault-Tolerant Periodic Event-Triggered Consensus Under Communication Delay and Multiple Attacks

This article proposes a resilient strategy for leaderless and leader–following consensus in general linear multiagent systems under simultaneous presence of false data injection and denial-of-service (DoS) attacks. To save energy, local control updates and communication between the neighboring agents are based on a distributed periodic event-triggered scheme. Sufficient conditions to guarantee the exponential mean square bounded consensus are derived as linear matrix inequality (LMI) conditions obtained from some delay-dependent Lyapunov–Krasovskii functionals. The proposed design framework computes the required consensus control gain based on a desired level of resilience to DoS. The guaranteed level of resilience in our proposed LMI-based method is less conservative (more realistic) compared to the existing analytical solutions. Two physical constraints, namely nonuniform time-varying communication delay and actuator faults, are also included in the problem formulation to enhance the practicability of the proposed solution. Simulations results demonstrate the effectiveness of the proposed framework in the presence of the considered cyberthreats and physical constraints.

Observer-Based Event-Triggered Adaptive Fuzzy Control for Fractional-Order Time-Varying Delayed MIMO Systems Against Actuator Faults

This article presents the observer-based event-triggered adaptive hybrid fuzzy dynamic surface control strategy for a category of uncertain nonstrict-feedback fractional-order nonlinear multi-input multi-output systems, including unknown time-varying delays and actuator faults. First, an adaptive hybrid fuzzy state observer and a serial-parallel estimation system are constructed to estimate the unmeasured system states and incorporate them into the control design scheme, respectively, where some appropriate fuzzy logic systems are introduced to approximate the unknown nonlinear functions. According to the dynamic surface control technique, the designed adaptive fuzzy control approach can surmount the deficiency of “complexity explosion.” Then, an observer-based adaptive event-triggered control algorithm is developed by constructing the Lyapunov–Krasovskii functionals and estimating the compounded disturbances. Furthermore, it is proved that under the drive of the reference signals, all the signals in the closed-loop system are semiglobally uniformly ultimately bounded and Zeno behavior can be successfully excluded. Finally, an example with numerical simulations is utilized to exhibit the applicability of the obtained observer-based event-triggered adaptive fuzzy control approach.

Adaptive Compensation for Infinite Number of Actuator Faults and Time-Varying Delay of a Flexible Manipulator System

This article addresses a novel adaptive compensation strategy for a flexible manipulator in the presence of unmodeled system dynamics and an infinite number of time-varying actuator faults. Differing from the existing results, the unmodeled system dynamics are represented by a nonlinear time-varying delay function, which is tackled with the aid of neural network technique and Lyapunov–Krasovskii function. By introducing the projection technique and a piecewise Lyapunov function, a fault-tolerant control method is proposed, the uniformly ultimate boundedness of the closed-loop system is ensured, and the number of actuator faults is allowed to be infinite. Simulation and physical experiment results show that the manipulator can track the desired angle and the vibration can be suppressed effectively under the proposed scheme for the system with uncertainties and actuator faults.