Fault Tolerant Control Design Research Articles

Observer-based fault-tolerant control design for a class of sampled-data nonlinear systems with bounded disturbances

Observer-based fault-tolerant control design for a class of sampled-data nonlinear systems with bounded disturbances

Adaptive fault-tolerant optimal control for hypersonic vehicles with state constrains based on adaptive dynamic programming

This paper addresses a novel adaptive fault-tolerant control (FTC) design based on adaptive dynamic programming (ADP) technique for hypersonic vehicle (HSV) subject to actuator fault and state constrains. The total control input is constructed by the combination of backstepping-based adaptive FTC and the ADP-based adaptive optimal control to improve the tracking performance and fault-tolerant capacity. By introducing a barrier Lyapunov function (BLF) to deal with the state constraints, a backstepping-based FTC scheme is designed to transform the tracking control problem into an equivalent optimal control problem. Subsequently, an adaptive optimal control strategy is developed by using the ADP technique to provide a supplementary control action. A critic network is constructed to solve the Hamilton–Jacobi–Bellman (HJB) equation online. The convergence properties of the closed-loop system are developed by utilizing the Lyapunov stability theory. Finally, simulation results are carried out to illustrate the effectiveness and efficiency of the proposed adaptive ADP-based FTC strategy.

Fault-Tolerant Control for Aircraft with Structural Damage Using Sparse Online Gaussian Process Regression

AbstractThis paper presents the design of data-driven fault-tolerant control using sparse online Gaussian process regression (SOGPR) to stabilize an aircraft with left-wing damage. The structural damage causes changes in mass, moment of inertia, center of gravity, and aerodynamic coefficients. These parameter variations deteriorate the performance of model-based nonlinear control methods. Hence, Gaussian process-based nonlinear dynamic inversion (GP-NDI) is proposed to compensate for uncertainties in situations of structural damage. Unlike parametric adaptive control approaches, Gaussian process regression is a non-parametric method that does not need prior information about uncertainties. And the proposed method implements SOGPR to reduce computational time and memory by incrementally updating the mean and variance. To compensate for the error in the estimated uncertainty, a robust control input is designed. In addition, a weighted delete score is used to improve the transient response. Numerical simulation results are compared with model reference adaptive control (MRAC) and nonlinear disturbance observer (NDO) to analyze a stabilizing and tracking performance in a structural damage situation.

Adaptive sensor fault tolerant control with prescribed performance for unmanned autonomous helicopter based on neural networks

PurposeThis paper aims to study a neural network-based fault-tolerant controller to improve the tracking control performance of an unmanned autonomous helicopter with system uncertainty, external disturbances and sensor faults, using the prescribed performance method.Design/methodology/approachTo ensure that the tracking error satisfies the prescribed performance, the authors adopt an error transformation function method. A control scheme based on the neural network and high-order disturbance observer is designed to guarantee the boundedness of the closed-loop system. A simulation is performed to prove the validity of the control scheme.FindingsThe developed adaptive fault-tolerant control method makes the system with sensor fault realize tracking control. The error transformation function method can effectively handle the prescribed performance requirements. Sensor fault can be regarded as a type of system uncertainty. The uncertainty can be approximated accurately using neural networks. A high-order disturbance observer can effectively suppress compound disturbances.Originality/valueThe tracking performance requirements of unmanned autonomous helicopter system are considered in the design of sensor fault-tolerant control. The inequality constraint that the output tracking error must satisfy is transformed into an unconstrained problem by introducing an error transformation function. The fault state of the velocity sensor is considered as the system uncertainty, and a neural network is used to approach the total uncertainty. Neural network estimation errors and external disturbances are treated as compound disturbances, and a high-order disturbance observer is constructed to compensate for them.

A Novel Failure-Distribution-Dependent Non-Fragile $H_\infty$ Fault-Tolerant Load Frequency Control for Faulty Multi-Area Power Systems

This paper studies the non-fragile <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_\infty$</tex-math></inline-formula> fault-tolerant load frequency control problem for multi-area power systems with actuator failures. At first, considering the distribution characteristics of partial failures, a stochastic distribution model is established for actuator partial failures. Next, for the perturbations of controller gains, not only the additive uncertainties but also the multiplicative uncertainties are incorporated into the fault-tolerant controller design. The coupling items which hardly directly solved have turned into a W-problem in fault-tolerant load frequency control. Furthermore, a novel failure-distribution-dependent non-fragile <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_\infty$</tex-math></inline-formula> fault-tolerant load frequency control algorithm is given by using linear matrix inequalities technology and Lyapunov-Krasovskii functional method. Finally, a numerical example with a three-area power system are given to show the effectiveness of the proposed methods.

Fault-tolerant controller design based on adaptive backstepping for tower cranes with actuator faults

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.

Deep-learning-based design of active fault-tolerant control for automated manufacturing systems subjected to faulty sensors

This research paper proposes a new implementation of a long short-term memory network for active fault-tolerant control subjected to single and multiple undiagnosable sensor faults. There are two networks used: the first performs as a diagnosis for automated manufacturing systems and can identify faulty sensors, while the second acts as an inverse model of these systems and is used to determine the reconfigured control action to take when sensors are not functioning as expected. The Factory I/O simulator is interfaced with MATLAB to simulate and verify the proposed approach for automated material handling case study with faultless and multiple fault sensors. Four sensors of the case study out of six are tolerated.

Fixed-Time Fault Estimation and Prescribed Performance Fault-Tolerant Control for Interconnected Systems.

This article investigates the problem of fixed-time fault estimation and fault-tolerant control (FTC) for interconnected systems subject to both multiplicative and additive actuator faults. On the basis of the bilimit homogeneous theory, the proposed fault estimation observer can acquire the exact system state and fault information in a specified time, and such a time is determined by a constant upper bound, independent of the initial observation errors. Next, the prescribed performance function (PPF) is employed to impose the anticipant performance criterion on the trajectory tracking errors, for the purpose of preserving both desirable transient and steady-state responses. Afterward, we incorporate the recursive fast terminal sliding-mode technique into the active FTC (AFTC) design procedure to eliminate the influence of faults. In such a way, the fixed-time convergence property of tracking errors can be guaranteed without any restriction on the initial conditions. Finally, comparative simulation results are provided to illustrate the feasibility and superiority of the proposed strategy.

Fuzzy wavelet neural adaptive finite-time self-triggered fault-tolerant control for a quadrotor unmanned aerial vehicle with scheduled performance

This paper focuses on fuzzy wavelet neural networks-based adaptive finite-time self-triggered fault-tolerant control design with assured tracking performance for a quadrotor unmanned aerial vehicle subject to unknown actuator faults. First, an improved finite-time performance function is integrated with the command-filtered backstepping control framework to assure the scheduled tracking performance, which can effectively constrain the transient fluctuations of the tracking error at fault occurrence. Then, two compensation mechanisms are developed to weaken the adverse impact induced by actuator faults and filter errors. Further, a fuzzy wavelet neural adaptive self-triggered fault-tolerant controller with guaranteed performance is designed to improve the fault insensitivity and tracking accuracy of the controlled vehicle, where the fuzzy wavelet neural networks are employed to approximate the unknown nonlinearities and the control signals are allowed to achieve irregular updating only when the pre-specified trigger protocol is violated. Stability analysis proves that the designed controller guarantees the attitude and position subsystems are practical finite-time stable, and the tracking errors are strictly confined to a preassigned region and never cross its allowed bounds. Finally, the validity of the developed control scheme is confirmed by the simulation results.

Integrated Fault-Tolerant Control Design With Sampled-Output Measurements for Interval Type-2 Takagi-Sugeno Fuzzy Systems.

This article is concerned with the integrated design of fault estimation (FE) and fault-tolerant control (FTC) for uncertain nonlinear systems suffering from actuator faults and external disturbance. The uncertain nonlinear systems are characterized as the interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy model, and IT2 membership functions are employed to effectively handle uncertainties. A fuzzy observer, utilizing only sampled-output measurements, is applied to simultaneously estimate actuator faults and system states. Based on the estimation, the fault-tolerant controller is designed to ensure the system stability under a predefined H∞ performance. The sampling behavior complicates the system dynamics and makes the integrated FTC design more challenging. To confront this issue, the discontinuous Lyapunov functional technique is exploited to enhance stability results by considering the sampling characteristic, upon which FE and FTC units are co-designed in the linear matrix inequality (LMI) framework. To further relax stability criteria, the analysis process incorporates the bound information of membership functions through the membership-function-dependent (MFD) method. Additionally, the relationship of mismatched premise variables resulting from the sampling scheme is also taken into account. Moreover, considering the imperfect premise matching (IPM) framework, the proposed fault-tolerant controller provides greater flexibility in selecting the shapes of membership functions and number of fuzzy rules that can vary from the counterpart of the fuzzy system. Finally, the efficacy of the proposed FTC technique is validated through a detailed numerical example.

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.

Fault-Tolerant Safety-Critical Control for Nonlinear Affine System by Using High-Order Control Barrier Function

A class of fault-tolerant safety-critical controller design methods based on high-order control barrier function (HOCBF) is proposed to address the problem of safety and stability of system affected by actuator faults in safety-constrained nonlinear affine system. Firstly, the fault information is incorporated into the conventional HOCBF to obtain a new type of HOCBF with faults. Secondly, in the case of a strictly required range of control inputs, a sufficient condition is proposed to satisfy the existing constraints, where the control inputs are always within the restricted range and the sufficient condition is expressed as feasibility constraints. Next, based on the quadratic programming (QP) method, the control Lyapunov function, fault HOCBF, and feasibility constraints are incorporated together to ensure that the overall feasibility, stabilization, and safety are guaranteed of the considered closed-loop system. Finally, the adaptive cruise control system is taken as an example to verify the effectiveness of the proposed method.

Adaptive fault tolerant control scheme based on observer-controller integrated design for deep-sea vehicles

This paper investigates the adaptive fault tolerant control problem for deep-sea vehicles subject to thruster saturation, dead zone and measurement noise. In order to reduce the conservativeness of the fault tolerant control system, an integrated design of observer estimation and adaptive fault tolerant control is proposed for deep-sea vehicles based on Nussbaum function. Nussbaum functions are used to separate the compensation of the nonlinear factors such as actuator fault and dead zone in the observer and controller from the traditional generalized uncertainty term. Meanwhile, a diagonal matrix with parameters dynamically adjusted based on the tracking error norm is introduced into the control law. The combined stability analysis of the observer and controller is presented in this paper, and the separation principle of the observer and controller is avoided. Finally, the simulation results of the underwater vehicle verify that the proposed method can effectively reduce the energy consumption of the control signals while maintaining the tracking accuracy, and the largest yaw error under the serious thruster fault is reduced by 40% at least compared with other control schemes.

Robust fault-tolerant control of a class of uncertain nonlinear switched systems with actuator saturation

This paper addresses the robust fault-tolerant control problem for a class of uncertain nonlinear switched systems with actuator saturation. Our aim is to design a switching law and a robust fault-tolerant control law for guaranteeing that the closed-loop system is asymptotically stabilizable, while at the same time the attraction domain estimation is as large as possible. By using the multiple Lyapunov functions method, sufficient conditions for robust fault-tolerant stabilisation are proposed for the closed-loop system. Then, when some scalar parameters are given in advance, the problem of fault-tolerant controller design and attraction domain estimation is transformed into a convex optimisation problem with linear matrix inequality (LMI) constraints. Finally the validity of the proposed design method is verified by a numerical example.

Composite fault reconstruction and fault-tolerant control design for cyber-physical systems: An interval type-2 fuzzy approach

This article scrutinizes the stabilization and fault reconstruction issues for interval type-2 fuzzy-based cyber–physical systems with actuator faults, deception attacks and external disturbances. The primary objective of this research is to formulate the learning observer system with the interval type-2 fuzzy technique that reconstructs the actuator faults as well as the immeasurable states of the addressed fuzzy based model. Further, the information of reconstructed actuator faults is incorporated in the developed controller with the imperfect premise variables for ensuring the stabilization of the system under consideration. At the same time, the H∞ technique is employed to reduce the impact of external disturbances in the considered model. In addition to that, the deception attacks are represented as a stochastic variable that satisfies the Bernoulli distributions. On the ground of this, a set of sufficient criteria is deduced in the context of linear matrix inequalities to affirm the stability of the addressed systems. Furthermore, the requisite gain matrices are computed by resolving the obtained linear matrix inequality based stability criteria. At last, two simulation examples, including the mass–spring–damper system are exhibited to demonstrate the usefulness of analytical findings of the developed strategy.

Design of Adaptive Finite-Time Fault-Tolerant Controller for Stochastic Nonlinear Systems With Multiple Faults

In this paper, the adaptive fault-tolerant control (FTC) problem is addressed for the stochastic nonlinear systems with multiple faults. The multiple faults, including the actuator and abrupt system faults, are first discussed in the same theoretical framework. The unknown nonlinearities are approximated by multi-dimensional Taylor networks (MTNs). By taking advantage of backstepping technique and finite-time control, the actual control law and virtual control signals are constructed, and then a novel adaptive FTC scheme based on the finite-time control method is proposed. The proposed controller guarantees that the closed-loop system is semi-global finite-time stable in probability (SGFSP) and the tracking error converges to a small neighborhood around the origin in the finite-time. Lastly, three examples are given to illustrate 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> —This research is motivated by the fact that actuators faults exist widely in real applications, which often degrade the control accuracy of the system and even result in the system instability. So far, the actuator and abrupt system faults of the stochastic nonlinear system have not yet been considered under the same theoretical framework. Therefore, this study designs a new MTN-based adaptive finite-time FTC scheme, which can ensure that the closed-loop system is SGFSP. The proposed control scheme has excellent practical value.

Design of UDE-based finite-time fault-tolerant control for DP vessels with complex disturbances and input constraints

This paper presents a novel uncertainty and disturbance estimator (UDE) – based finite-time fault-tolerant control scheme, applied to the controller design of dynamic positioning (DP) vessels with environmental disturbances, model uncertainty, input saturation, and thruster failures. The proposed method employs a non-singular terminal sliding surface to achieve finite-time stability based on the vessel's position and velocity errors. A UDE is to estimate environmental disturbances and model uncertainties. The auxiliary dynamic system (ADS) and smooth switching function are adopted to compensate for input constraints and avoid singularity caused by ADS. Further, a proposed fault observer is to estimate the thruster's fault state. In partial thruster failure, the utilization of the remaining execution ability of the failed thrusters to achieve vessel control objectives. Theoretical and simulation results show that the proposed method has a fast response, high control accuracy, strong disturbance resistance, and fault tolerance, making it superior to recent and classical trajectory tracking control.

Adaptive Fuzzy Fault Tolerant Control for Robot Manipulators With Fixed-Time Convergence

This paper aims to resolve the three major issues of fault tolerant control (FTC) for robot manipulators: (i) the faster response, lower tracking errors, lower chattering and higher robustness of the FTC, (ii) the requirement of partial or full knowledge of robot dynamics for the design of model-based FTC, and (iii) the global fixed-time convergence of the system. First, a fixed-time controller based on a backstepping control is designed and its disadvantages are analysed. Then, an adaptive fuzzy backstepping control is developed to enhance the tracking performance of the system. The proposed approach does not require the full prior knowledge of robot dynamic model, thus facilitating implementation of the controller in practical applications. In addition, the tracking errors of the system will be practically convergent within a fixed-time, which provides additional system information in advance. The fixed time convergence of the system is mathematically proved and the performance of the system is demonstrated for FTC of a PUMA560 robot.

Fractional Order Fault Tolerant Control - A Survey

In this paper, a comprehensive review of recent advances and trends regarding Fractional Order Fault Tolerant Control (FOFTC) design is presented. This novel robust control approach has been emerging in the last decade and is still gathering great research efforts mainly because of its promising results and outcomes. The purpose of this study is to provide a useful overview for researchers interested in developing this interesting solution for plants that are subject to faults and disturbances with an obligation for a maintained performance level. Throughout the paper, the various works related to FOFTC in literature are categorized first by considering their research objective between fault detection with diagnosis and fault tolerance with accommodation, and second by considering the nature of the studied plants depending on whether they are modelized by integer order or fractional order models. One of the main drawbacks of these approaches lies in the increase in complexity associated with introducing the fractional operators, their approximation and especially during the stability analysis. A discussion on the main disadvantages and challenges that face this novel fractional order robust control research field is given in conjunction with motivations for its future development. This study provides a simulation example for the application of a FOFTC against actuator faults in a Boeing 747 civil transport aircraft is provided to illustrate the efficiency of such robust control strategies.

Distributed neural-based fault-tolerant control of multiple flexible manipulators with input saturations

This paper focuses on a novel fault-tolerant control design for networked flexible manipulators with actuator failures, input saturations, and external disturbances. Unlike the existing methods, the new control strategy has three characteristics. First, the second-order auxiliary system is used to eliminate the influence of input saturation. Second, by designing some proportional–integral (PI) auxiliary variables, the boundary dynamics of manipulators are reconstructed as some new second-order systems, where the nonlinear uncertainties are estimated by the adaptive radial basis neural networks (RBFNNs) and the compound disturbances are compensated by the adaptive method. Third, a boundary local angle interaction protocol is given under a digraph. Then combining the virtual control and Lyapunov design, some novel distributed fault-tolerant control algorithms are obtained to ensure that the vibration states and the angle consensus error are uniformly ultimately bounded. Finally, numerical simulations are provided to show the effectiveness of these control algorithms.