Fault-tolerant Control Scheme Research Articles

Adaptive dynamic programming–based fault-tolerant control of multi-UAV formation system

With the increasing utilization of unmanned aerial vehicle (UAV) swarms in civilian and military field applications, it is crucial to eliminate the potential impacts of UAV faults on task completion and efficiency. To address this issue, the present paper investigates a fault-tolerant control scheme based on the adaptive dynamic programming (ADP) method and fault observer. The whole closed-loop system is composed of a position loop and an attitude loop. An ADP-based position-tracking controller is established with an improved cost function that can simultaneously represent actuator faults, control inputs, and tracking errors. In addition, a single-layer critic neural network is constructed to estimate the cost function and approximate control inputs. In the process of adjusting the weights of the neural network, a unique adjustment method combining policy gradient and prioritized experience replay is adopted to improve the data usage efficiency and speed up the weight convergence. Furthermore, a sliding mode–based attitude controller is utilized for the inner-loop attitude subsystem. The significant feature of this scheme is that it has the ability to self-learn and self-adapt. A Lyapunov stability analysis is performed to verify whether the signals are uniformly ultimately bounded. Finally, simulation experiments are conducted to demonstrate the effectiveness of the proposed scheme.

Distributed-observer-based adaptive fault tolerant formation maneuver for autonomous surface vehicles under stochastic cyber-attack

This paper investigates the distributed adaptive fault-tolerant formation maneuver control strategy for multiple autonomous surface vehicles (ASVs) systems subject to stochastic cyber-attack and actuator failures. Existing works have provided solutions against continuous attacks on communication channels while ignoring the increasingly prevalent intermittent attacks. Compared to the former, the latter are more challenging to detect, having been insufficiently described and addressed. Motivated by this observation, this article is concerned with the stochastic intermittent injection attacks, which are modeled as stochastic impulsive sequences, characterized by stochastic injection instant and intensity, leading to unpredictable changes in the state information transmitted by the neighbors. Taking this dilemma into consideration, a novel distributed observer is proposed, capable of effectively observing the target formation even in the presence of stochastic intermittent injection attacks. Meanwhile, a large feedback gain is incorporated into the observer to reduce the impact of attacks on system stability. A linear sliding manifold, together with the adaptive algorithm, is utilized to construct a fault tolerant control architecture for each follower ASV without employing model parameters which can confront the embarrassing scenario of actuator failures. Eventually, theoretical deduction and numerical simulations are conducted to confirm the feasibility of the proposed collaborative control scheme.

ADP-based fault-tolerant consensus control for multiagent systems with irregular state constraints

This paper investigates the consensus control issue for nonlinear multiagent systems (MASs) subject to irregular state constraints and actuator faults using an adaptive dynamic programming (ADP) algorithm. Unlike the regular state constraints considered in previous studies, this paper addresses irregular state constraints that may exhibit asymmetry, time variation, and can emerge or disappear during operation. By developing a system transformation method based on one-to-one state mapping, equivalent unconstrained MASs can be obtained. Subsequently, a finite-time distributed observer is designed to estimate the state information of the leader, and the consensus control problem is transformed into the tracking control problem for each agent to ensure that actuator faults of any agent cannot affect its neighboring agents. Then, a critic-only ADP-based fault tolerant control strategy, which consists of the optimal control policy for nominal system and online fault compensation for time-varying addictive faults, is proposed to achieve optimal tracking control. To enhance the learning efficiency of critic neural networks (NNs), an improved weight learning law utilizing stored historical data is employed, ensuring the convergence of critic NN weights towards ideal values under a finite excitation condition. Finally, a practical example of multiple manipulator systems is presented to demonstrate the effectiveness of the developed control method.

Electro-Mechanical Brake System Architectural Design and Analysis Based on Functional Safety of Vehicles

Electro-mechanical brake (EMB) systems have garnered significant attention due to their distributed architecture. However, their signals from the brake pedal to the wheel-end actuators (WEAs) are transmitted electrically, meaning that any fault in EMB systems can severely impair the braking performance of vehicles. Consequently, the functional safety issues of EMB systems are the primary limitation of their widespread adoption. In response, this study first introduced the typical architectures of EMB and evaluated the automotive safety integrity level (ASIL) that must be achieved. Based on this, an EMB system architecture that satisfies functional safety standards was proposed. To accurately analyze the main factors affecting the probabilistic metric for hardware failures (PMHF) of the architecture, the failure rate of WEAs is further discussed. Specifically, a Markov chain was employed to define the operating states of the WEA matrix. The availability of each operating state was assessed based on the fault-tolerant control strategy. Finally, the failure rates of critical EMB parts, particularly the WEA matrix, were calculated. The results indicate that the unavailability of the WEA matrix is 9.244 × 10−3 FIT. Furthermore, the PMHFs of the EMB system for each safety goal are 6.14 FIT, 5.89 FIT, and 6.03 FIT, respectively, satisfying the ASIL-D requirements.

Fixed-Time Fault-Tolerant Adaptive Neural Network Control for a Twin-Rotor UAV System with Sensor Faults and Disturbances

This paper presents a fixed-time fault-tolerant adaptive neural network control scheme for the Twin-Rotor Multi-Input Multi-Output System (TRMS), which is challenging due to its complex, unstable dynamics and helicopter-like behavior with two degrees of freedom (DOFs). The control objective is to stabilize the TRMS in trajectory tracking in the presence of unknown nonlinear dynamics, external disturbances, and sensor faults. The proposed approach employs the backstepping technique combined with adaptive neural network estimators to achieve fixed-time convergence. The unknown nonlinear functions and disturbances of the system are processed via an adaptive radial basis function neural network (RBFNN), while the sensor faults are actively estimated using robust terms. The developed controller is applied to the TRMS using a decentralized structure where each DOF is controlled independently to simplify the control scheme. Moreover, the parameters of the proposed controller are optimized by the gray-wolf optimization algorithm to ensure high flight performance. The system’s stability analysis is proven using a Lyapunov approach, and simulation results demonstrate the effectiveness of the proposed controller.

Hierarchical Distributed Adaptive Fault-Tolerant Control of Nonlinear Fractional-Order Multiagent Systems With Faults and Periodic Disturbances Using Event-Triggered Communication.

This article presents a distributed fault-tolerant control (FTC) scheme for nonlinear fractional-order (FO) multiagent systems (MASs) with the order lying in (0, 1], such that the proposed control architecture can be directly applied to both FO and integer-order (IO) systems without any modifications. To handle the unexpected actuator faults encountered by the FO MASs, a hierarchical FTC mechanism is developed for each system by constructing an event-triggered distributed FO estimator at the upper layer to estimate the leader system's output via conditionally triggered neighboring information, and an FTC unit at the lower layer to counteract the loss-of-effectiveness faults via Nussbaum function with FO criteria. To further address the unknown nonlinear functions involving bias faults and periodic disturbances, the Fourier series expansion technique is used to construct the input variables of fuzzy neural networks (FNNs), such that the FNNs with dynamically adjusted weight matrices, centers, and widths can be developed for each FO system to act as the learning module. It is shown by FO Lyapunov stability analysis that all follower systems can track the leader system against faults and periodic disturbances. Simulation results on FO systems and hardware-in-the-loop experiment results on IO fixed-wing unmanned aerial vehicles show the extensive feasibility of the developed scheme.

Adaptive dynamic programming-based multi-fault tolerant control of reconfigurable manipulator with input constraint

In this paper, a multi-fault tolerant controller considering actuator saturation is proposed. Based on the adaptive dynamic programming(ADP) algorithm, the fault tolerant control of the reconfigurable manipulator with sensor and actuator faults are carried out. Firstly, combined with the state space expression, the nonlinear transformation of sensor fault is performed by adopting the differential homeomorphism principle. An improved cost function is constructed based on the fault estimation function obtained by the fault observer, and combined with hyperbolic tangent function to deal with input constraint problem. Then, an evaluation neural network (NN) is established and the Hamilton–Jacobi–Bellman (HJB) equation is solved by online strategy iterative algorithm. Furthermore, based on Lyapunov theorem, the stability of reconfigurable manipulator systems with multi-fault are proved. Lastly, the simulation studies are used to certify the effectiveness of the presented fault tolerant control (FTC) scheme.

Trajectory tracking control for unmanned amphibious surface vehicles with actuator faults

In this paper, a fixed-time fault-tolerant control strategy is proposed for the trajectory tracking of aquatic–aerial unmanned amphibious vehicles (AquaUAVs) under multiple actuator faults. A switched error system is introduced to describe the AquaUAV trajectory tracking motion under actuator faults in different modes. A switched fixed-time extended state observer (FESO) is developed to observe the tracking errors of the vehicle and the actuator faults, and a fault-tolerant control strategy. The proposed composite controller not only ensures the stability of the system, but also compensates the faults of the actuator. Simulation and experiment results verify that the proposed control strategy can realize the trajectory tracking control of amphibious vehicle under the condition of multiple actuator faults.

Disturbance-observer-based adaptive neural event-triggered fault-tolerant control for uncertain nonlinear systems against sensor faults

In this article, the adaptive event-triggered fault-tolerant control (FTC) issue of uncertain nonlinear systems suffering from sensor and actuator faults as well as external disturbances is studied. A disturbance observer (DO) is constructed to compensate for external disturbances and approximation errors generated by neural networks (NNs). Then, switching event-triggered control and command filtering techniques are introduced to balance the communication frequency and tracking performance of the controlled systems while avoiding “explosion of complexity” issue caused by iterative derivations of virtual controllers. Furthermore, compensation signals are designed to eliminate filtered errors. Finally, it is proven that the developed FTC scheme can esure that all signals are bounded and free from Zeno phenomenon. Simulation results of a spring damping mechanical system verifies the merits of the designed control algorithm.

Development and fault-tolerant control of a distributed piezoelectric stack actuator

Piezoelectric stack actuator (PSA) has attracted widespread attention in aerospace applications. However, the severe operating conditions would bring certain risks to PSA, leading to decreased performance or even failure. The conventional PSA structure lacks adaptability in the event of a complete failure occurring within the piezoelectric stack layer (PSL) due to its centralized design and driving method. To address the reliability challenges inherent in PSAs, this paper proposes a novel distributed PSA (DPSA) by means of mechanical and electrical dispersion. Additionally, dual-redundant PSLs are integrated into the DPSA as backups, providing hardware redundancy for active fault-tolerant control (FTC). Building upon this foundation, an sliding mode observer (SMO)-based fault detector for DPSA is developed to facilitate fault reconstruction. Subsequently, an active FTC strategy, comprising dual-SMO-based fault detectors and a tracking controller, is introduced to effectively manage faults and reallocate control resources. Comprehensive experiments under various fault scenarios are carried out to assess the performance of the SMO-based fault detector and FTC strategy. The results demonstrate that the proposed fault detector and FTC strategy promptly detect faults and efficiently restore the DPSA to a stable state, thereby ensuring effective trajectory tracking even in the presence of faults.

Prescribed-time active fault-tolerant control of dynamic positioning vessels based on improved iterative learning observer

Considering the dynamic positioning (DP) vessel control system with actuator faults and unknown environment disturbances, a fault-tolerant control scheme based on improved iterative learning observer (ILO) is proposed in this article. Firstly, the kinematics and dynamics models of DP vessels are established containing the actuator faults and environmental disturbances. Secondly, an improved ILO is designed to handle the unknown faults and disturbances concurrently with higher estimation precision by introducing the state information of the previous moment. Further, a prescribed-time active fault-tolerant control scheme is presented on the basis of the improved ILO and prescribed-time stability. And the stability of proposed ILO and DP fault-tolerant control system are analyzed based on Lyapunov theory. Finally, the effectiveness and advantages of the presented ILO and active fault-tolerant control scheme are illustrated by numerical simulations and comparisons.

Inverse compensation mechanism-based adaptive fuzzy-neural fault-tolerant control for an uncertain quadrotor UAV

Actuator faults, system uncertainties, and external disturbances can affect tracking control performance of the quadrotor UAV, and even cause the instability of the system. To address this issue, an adaptive fuzzy-neural fault-tolerant control (AFNFTC) scheme is proposed for the quadrotor UAV integrated with inverse compensation strategy and fuzzy neural networks (FNNs) technology. Firstly, we design an inverse compensation mechanism to address actuator faults and utilize the approximation capabilities of the FNNs to compensate for system uncertainties. Then, based on the Lyapunov direct method, the AFNFTC scheme is designed to ensure the stability of the quadrotor UAV. Under the proposed scheme, the closed-loop system for the quadrotor UAV is proven to be uniformly bounded, and the tracking error of the quadrotor UAV is demonstrated to converge to a small compact set ultimately by appropriately selecting design parameters. Finally, the feasibility and effectiveness of the proposed scheme is validated through simulation results.

Fault‐tolerant control of unmanned marine vehicles with unknown parametric dynamics via integral sliding mode output feedback control

AbstractThis paper focuses on fault‐tolerant control (FTC) for unmanned marine vehicles (UMVs) subject to unknown parametric dynamics, thruster faults, and external disturbances. A novel FTC strategy based on the integral sliding mode output feedback method is proposed. Based on a high gain compensator and output information, a novel integral sliding mode fault‐tolerant controller is constructed to guarantee the dynamic positioning (DP) of UMVs. An attraction region that is related to the known positive constants with respect to dynamic uncertainties has been revealed for the first time. Finally, the closed‐loop stability can be guaranteed from the every initial time in despite of unknown parametric dynamics, thruster faults, and external disturbances. Simulation though a typical floating production ship model has verified the effectiveness of the proposed method.

Neural adaptive performance guaranteed formation control for USVs with event-triggered quantized inputs

ABSTRACT This study presents an adaptive fault-tolerant performance guaranteed formation control strategy with event-triggered quantised inputs (EDQI) for unmanned surface vessels (USVs) in the presence of model uncertainties and actuator faults. Firstly, a prescribed performance formation guidance control law is proposed to allow formation tracking errors to evolve within the performance envelope and convergence to prescribed steady regions at an appointed time. Then, minimal-parameter-learning-based neural networks (MLP-NN) are used to tackle model uncertainties. To reduce the frequency of actuator updates and the communication burden on the channel, a dynamic memory event-triggered quantised input strategy is investigated. In addition, adaptive estimators and auxiliary design signals are built to compensate for the effects caused by input quantisation and actuator faults. It is proved that all closed-loop signals are bounded and formation errors can converge to the prescribed region at an appointed time. Finally, the effectiveness of the proposed controllers is verified by numerical simulations.

Disturbance Observer-Based Adaptive Fault Tolerant Control with Prescribed Performance of a Continuum Robot

This paper studies an adaptive fault tolerant control (AFTC) scheme for a continuum robot subjected to unknown actuator faults, dynamics uncertainties, unknown disturbances, and prescribed performance. Specifically, to deal with uncertainties, a function approximation technique (FAT) is employed to evaluate the unknown actuator faults and uncertain dynamics of the continuum robot. Then, a nonlinear disturbance observer (DO) is developed to estimate the unknown compounded disturbance, which contains the unknown disturbances and approximation errors of the FAT. Furthermore, the prescribed error bound is treated as a time-varying constraint, and the controller design method is based on an asymmetric barrier Lyapunov function (BLF), which is operated to strictly ensure the steady-state and transient performance of the continuum robot. Afterwards, the simulation results validate the effectiveness of the proposed AFTC in dealing with the unknown actuator faults, uncertainties, unknown disturbances, and prescribed performance. Finally, the effectiveness of the proposed AFTC scheme is verified through experiments.

Adaptive neural network fault-tolerant control of hypersonic vehicle with immeasurable state and multiple actuator faults

This paper proposes a fault-tolerant control scheme for tracking and controlling hypersonic vehicles with unknown dynamics, actuator failures, and unmeasurable states. The approach involves using a radial-based neural network to approximate the unknown dynamics and reconstruct the entire system model. Additionally, a neural network state observer is proposed to estimate the unmeasurable state of the system. To address the impact of actuator faults, a nonlinear observer is designed to estimate and compensate for the approximation error of the neural network system and fault values. Furthermore, a prescribed performance function is introduced to ensure both transient and steady-state performance of the system. The bounded stability of the closed-loop system is demonstrated through Lyapunov stability analysis.

L1 Adaptive Fault-Tolerant Control for Nonlinear Systems Subject to Input Constraint and Multiple Faults

This paper investigates an L1 adaptive fault-tolerant control scheme for nonlinear systems with input constraint, external disturbances, and multiple faults, which include actuator faults and sensor faults. Faults and input constraint are important factors that affect the stability and performance of a control system. Actuators and sensors are the most vulnerable components, with the former receiving more attention in comparison. In this paper, sensor faults are first transformed into pseudo-actuator faults through the augmented matrix approach, which facilitates their handling together with actuator faults. Saturation constraints on the control signal are not conducive to the design of the controller. The conversion of an input-saturated function to a time-varying linear system is completed based on function approximation and Lagrange’s mean value theorem. Moreover, a nonlinear system with unknown input gain and uncertainties is constructed using these methods. Next, an L1 adaptive fault-tolerant controller is designed to cope with uncertainties, including system uncertainties, external disturbances, faults, and approximation errors. In the L1 adaptive controller, the online estimation of the time-varying parameters allows for updating of the system state, while the combination of the two is transmitted to the control law such that it can compensate for the effects of the uncertainties. The stability and performance boundaries are further derived using the Lyapunov theory and the L1 reference system. Finally, simulations are carried out to demonstrate the effectiveness of the proposed controller.

Output Feedback Active Fault Tolerant Control for a 3-DOF Laboratory Helicopter With Sensor Fault

In this paper, an output feedback-based active fault tolerant control (FTC) scheme is proposed for an unstable three degree-of-freedom (3-DOF) helicopter equipped only with angular position sensors, of which a single sensor can be faulty. Limited by the available outputs, existing fault estimation (FE) and FTC schemes cannot be applied to this helicopter because when the sensor measuring the elevation or travel angle is faulty, it does not satisfy the minimum-phase and matching conditions required by standard observers. To circumvent this problem, an adaptive interval observer is firstly designed as a fault detection and isolation (FDI) unit to indicate the occurrence and location of a fault, in contrast to the existing FDI methods that require a bank of observers and incur a high computational cost. Then a high-gain observer is combined with a sliding mode observer to form an FE unit that does not require the matching and minimum-phase conditions. Based on the estimate of the fault, an FTC scheme is constructed to ensure an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal{H}_{\infty}$</tex-math> </inline-formula> performance of the faulty system. Finally, physical experiments on the 3-DOF helicopter verify the effectiveness of the proposed scheme. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The 3-DOF lab helicopter serves as an ideal experimental platform for control strategies. In this paper, an active fault tolerant control (FTC) scheme is developed for a 3-DOF lab helicopter when any single sensor can be faulty. The helicopter is nonlinear and subject to external disturbances. There are only encoders measuring the attitude angles and no sensors for angular velocities. In this paper, we firstly design a fault detection and isolation (FDI) unit based on an adaptive interval observer to detect the fault and identify its location; it incurs a lower computational cost compared to existing methods that use a bank of observers. Then a high-gain observer is combined with a sliding mode observer to estimate the fault. Based on the estimate of the fault, an FTC scheme is constructed and designed using Linear Matrix Inequalities to ensure an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal{H}_{\infty}$</tex-math> </inline-formula> performance of the faulty system.

Fault Tolerant Control of Fuzzy Stochastic Distribution Systems With Packet Dropout and Time Delay

The fault diagnosis (FD) and fault tolerant control (FTC) problem of the fuzzy stochastic distribution system (SDS) over packet dropout and time delay is studied. Firstly, the static model of linear fuzzy logic system that approximates the output PDF and the dynamic model with the multiplicative fault in a fuzzy system are established. The random packet dropout and time delay caused by the network are described in a unified framework, in which the lost data is compensated with the latest data received by the buffer. On this basis, an adaptive observer is devised to estimate the unknown multiplicative fault. The design of sliding mode predictive fault tolerant controller is discussed to ensure that the system still has good tracking performance after the fault occurs. Numerical simulations on a molecular weight distribution control system are supplied to prove the effectiveness of the presented scheme. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation of this paper is to improve the reliability of the fuzzy stochastic distribution system with packet dropout and time delay. This kind of stochastic distribution systems is described by the relationship between the input and the output PDF rather than the normal relationship between the input and the output. When the multiplicative fault occurs in the system, the design of fault diagnosis strategy and fault-tolerant controller becomes an important challenge, especially when the system is subject to packet dropout and time delay. To address the above problems, a new fault diagnosis and fault-tolerant control scheme is proposed, which can accurately estimate the time and size of the fault and obtain the satisfactory fault-tolerant control effect.

Neural network-based adaptive fault-tolerant control for nonlinear systems with uncertainties

This paper proposes a novel fault-tolerant control (FTC) scheme for real-time uncertainty estimation in nonlinear systems. It addresses the challenges arising from nonlinear dynamics in system inputs, states, and outputs, along with measurement uncertainties, within an output feedback framework. Our approach leverages two key components: 1) A neural network NN descriptor-based observer: this novel observer concurrently estimates both system states and sensor uncertainties. It is particularly capable of handling unbounded sensor uncertainties in specific situations. It utilizes NNs as universal approximators to capture the system's complex nonlinearities. 2) A robust model reference tracking controller: this controller employs the estimated states from the NN descriptor-based observer to achieve the desired system performance despite the existence of uncertainties. It exhibits robustness, guaranteeing system stability and asymptotic state tracking to a given reference model. The efficacy of the proposed FTC scheme is validated through theoretical analysis and its application to two real-world case studies.