Unknown Sensor Faults Research Articles

Neural‐Network‐Based Finite‐Horizon Estimation for Complex Networks With Probabilistic Quantizations and Sensor Faults

ABSTRACTIn this article, the problem of finite‐horizon state estimation is studied for a class of time‐varying complex networks with sensor faults. The phenomenon of measurement quantization is considered such that the measurements are quantized probabilistically before transmitted to the state estimator. To deal with the unknown sensor fault, a neural network is introduced to appropriate the sensor fault whose weights are updated based on estimation error and the gradient descent method. Our aim is to design state estimators so that the state estimation errors are finite‐time bounded. First, sufficient conditions are established to ensure the existence of the desired state estimators. Then, the gains of the state estimators are derived in terms of the solutions to a set of recursive matrix inequalities. Finally, the usefulness of our estimation approach is confirmed by an illustrative example.

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.

Decentralized adaptive fault‐tolerant control for interconnected nonlinear time‐delay systems with uniform prescribed performance

AbstractIn this article, the prescribed performance tracking control problem is studied for interconnected nonlinear time‐delay systems with sensor and actuator faults. Based on the dynamic gain technique and the backstepping control method, a delay‐independent adaptive state feedback controller is designed. Besides, by embedding a smooth function into the controller, the unknown external disturbances, sensor and actuator faults are successfully compensated. Furthermore, we can deal with the asymmetric prescribed performance control with semi‐global or global requirements uniformly by selecting the initial value of the the scaling function. Based on Lyapunov theory, it is proved that the system output of each subsystem can closely track the desired signal and the boundedness of all signals in the closed‐loop systems can be ensured under the proposed control method. Finally, the effectiveness of the proposed control strategy is proved by two simulation examples.

Event-triggered control for switched systems with sensor faults via adaptive fuzzy observer

This article lays emphasis on a mode-dependent event-triggered controller (MDETC) scheme for a kind of nonlinear switched systems with delay and sensor faults, where the state of the system, sensor faults, and nonlinear term are all unknown. By utilizing a fuzzy logic method and constructing adaptive laws, an adaptive fuzzy observer is designed to simultaneously estimate the states and sensor faults. Meanwhile, a MDETC output feedback is designed for the adaptive fuzzy observer, which is practical and can reduce the conservatism of obtained result since the dwell time (DT) of the switching modes is independent of the triggering interval and the asynchronous phenomenon between system mode and controller mode is avoided. It is discovered that the switching has positive effects on the stability and the designed observer can estimate both the unknown states and sensor faults with arbitrary approximate bound. The effectiveness of the theoretical analysis is demonstrated through numerical simulations.

Mixed-Gain Adaption-Based Fault-Tolerant Funnel Control of Robotic Manipulators With Unknown Dynamics and Sensor Faults

Mixed-Gain Adaption-Based Fault-Tolerant Funnel Control of Robotic Manipulators With Unknown Dynamics and Sensor Faults

Adaptive backstepping fault-tolerant control for hypersonic aircraft with unknown control direction under actuator and sensor faults

Abstract In this paper, an adaptive backstepping controller of hypersonic flight vehicle (HFV) system is investigated in the presence of unknown actuator and sensor faults, unknown control direction and varying external disturbance, which expands traditional fault-tolerant control (FTC) approaches in HFV field. The control-oriented model is established with reasonable and comprehensive analysis of various fault types. Compared with the previous FTC research on HFV system, the time-varying and fixed fault types are simultaneously designed for actuator and sensors, and the control direction is unknown, which can more accurately simulate the real fault situations in the hypersonic flight environment. To avoid singularity problems caused by the unknown control direction, the Nussbaum gain function technique is adopted in the control algorithm, which is also capable of overcoming the influence of unknown time-varying fault parameters. The boundness and stability of system under the designed control scheme are theoretically verified. Finally, numerical simulation results demonstrate the effectiveness and superiority of the proposed control strategy.

Decentralized adaptive fault-tolerant control of interconnected systems with sensor faults

The paper considers a class of interconnected nonlinear systems with unknown sensor faults and uncertain interactions. Each subsystem is subject not only to the local sensor faults but also to the possible effects of faults from other subsystems through uncertain interactions. Both multiplicative and time-varying additive sensor faults are taken into consideration, which are allowed to be unknown. A decentralized adaptive fault-tolerant control (FTC) scheme has been established. To eliminate the effects of faults in the control loop, several auxiliary quantities are constructed wisely and estimated by the designed adaptive mechanism. A smooth function is proposed, by which the uncertain interactions can be compensated even if they are coupled with sensor faults. It is proved that, by using only the corrupted states, all the closed-loop signals are globally uniformly bounded, and the output tracking error converges into an adjustable residual set. Finally, simulation and comparison studies are presented to illustrate the effectiveness of the proposed scheme.

Data‐driven fault‐tolerant control for SISO nonlinear system with unknown sensor fault

SummaryThis paper studies the adaptive fault‐tolerant control problem for a general nonlinear discrete‐time SISO system with unknown system model and sensor fault. First, utilizing the input‐output (I/O) data, an equivalent full‐form dynamic linearization (FFDL) data model is to be constructed by introducing a pseudo‐gradient vector. Then, to estimate the system's actual output from the sensor measurements corrupted by unknown faults, a nonlinear autoregressive with external input neural network (NARXNN) is employed and well‐trained, by which the compensation of the fault signal can hence be derived indirectly. Based on the optimality criterion, an adaptive fault‐tolerant control (FTC) strategy is therefore proposed, which promises the convergence of tracking error and the boundedness of system signals. The effectiveness of the proposed FTC algorithm is illustrated by simulation results.

Rapid Sensor Fault Diagnosis for a Class of Nonlinear Systems via Deterministic Learning.

In this article, a rapid sensor fault diagnosis (SFD) method is presented for a class of nonlinear systems. First, by exploiting the linear adaptive observer technology and the deterministic learning method (DLM), an adaptive neural network (NN) observer is constructed to capture the information of the unknown sensor fault function. Second, when the NN input orbit is a period or recurrent one, the partial persistent excitation (PE) condition of the NNs can be guaranteed through the DLM. Based on the partial PE condition and the uniformly completely observable property of a linear time-varying system, the accurate state estimation and the sensor fault identification can be achieved by properly choosing the observer gain. Third, a bank of dynamical observers utilizing the experiential knowledge is constructed to achieve rapid SFD and data recovery. The attractions of the proposed approach are that accurate approximations of sensor faults can be achieved through the DLM, and the data that are destroyed by the sensor faults can be recovered by using the learning results. Simulation studies of a robot system are utilized to show the effectiveness of the proposed method.

Adaptive resilient containment control for nonlower triangular multiagent systems with time-varying delay and sensor faults

This paper investigates the adaptive resilient containment control for nonlinear multiagent systems (MASs) with time-varying delay, unmodeled dynamics and sensor faults. To solve the coupling problem of unknown state delays and sensor faults in a nonlower triangular structure, we develop an effective method by using a new lemma and the Lyapunov-Krasovskii functional. Then, to reduce the negative impact of unknown sensor faults, a novel adaptive resilient containment control method is designed based on a distributed sliding-mode estimator, which can effectively improve the transient performance of the MASs. Moreover, by using a dynamic signal, the problem of unmodeled dynamics is solved. The proposed control scheme can not only drive all followers suffering from sensor faults to converge to the convex hull formed by the leaders but also relatively reduce the undesired chattering phenomenon. Finally, a comparative simulation example is given to illustrate the effectiveness of the proposed strategy.

Simultaneous actuator and sensor fault reconstruction of singular delayed linear parameter varying systems in the presence of unknown time varying delays and inexact parameters

SummaryIn this article, robust fault diagnosis of a class of singular delayed linear parameter varying systems is considered. The considered system has delayed dynamics with unknown time varying delays and also it is affected by noise, disturbance and faults in both actuators and sensors. Moreover, in addition to the aforementioned unknown inputs and uncertainty, another source of uncertainty related to inexact measures of the scheduling parameters is present in the system. Making use of the descriptor system approach, sensor faults in the system are added as additional states into the original state vector to obtain an augmented system. Then, by designing a suitable proportional double integral unknown input observer (PDIUIO), the states, actuator, and sensor faults are estimated. The uncertainty due to the mismatch between the inexact parameters that schedule the observer and the real parameters that schedule the original system is formulated with an uncertain system approach. In the PDIUIO, the uncertainty induced by unknown inputs (disturbance, noise and actuator, and sensor faults), unknown delays, and inexact parameter measures are attenuated in H∞ sense with different weights. The constraints regarding the existence and the robust stability of the designed PDIUIO are formulated using linear matrix inequalities. The efficiency of the proposed method is verified using an application example based on an electrical circuit.

Adaptive fault‐tolerant containment control for stochastic nonlinear multi‐agent systems with input saturation

AbstractThis article considers the fault‐tolerate containment control problem for stochastic nonlinear multi‐agent systems in the presence of input saturation and sensor faults. In order to solve the problem of input saturation, a smooth function is used to approximate the controller saturation function. Due to the excellent approximation characteristic, neural network (NN) is used to deal with unknown nonlinear functions and unknown sensor faults. Meanwhile, by using the auxiliary system and combining adaptive backstepping technique with adaptive NN control, a fault‐tolerant containment control approach is proposed. By using graph theory and stochastic Lyapunov stability theory, it can be proved that all signals in the closed‐loop system are semi‐globally uniformly ultimately bounded in probability. Finally, simulation results are given to show the effectiveness of the proposed control scheme.

Flight Evaluation of an LPV Sliding Mode Observer for Sensor FTC

This brief develops a sliding mode sensor fault-tolerant control scheme for a class of linear parameter varying (LPV) systems. It incorporates a sliding mode observer that reconstructs the unknown sensor faults based on only the system inputs and outputs. The reconstructed sensor faults are used to compensate for the corrupted sensor measurements before they are used in the feedback controller. Provided accurate fault estimates can be computed; near nominal control performance can be retained without any controller reconfiguration. Furthermore, the closed-loop stability of the fault-tolerant control (FTC) scheme, involving both a sliding mode controller and a sliding mode observer, is rigorously analyzed. The proposed scheme is validated using the Japan Aerospace Exploration Agency&#x2019;s Multipurpose Aviation Laboratory (MuPAL-<inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>) research aircraft. These flight tests represent the first validation tests of a sliding mode sensor FTC scheme on a full-scale aircraft.

Command filtered fixed‐time control for a class of multi‐agent systems with sensor faults

AbstractIn this article, the command filtered fixed‐time fault‐tolerant control (FTC) problem is investigated for multi‐agent systems (MASs). The considered MASs with nonstrict feedback form are affected by unknown nonlinear functions and sensor faults, which are solved by using the properties of neural networks (NNs). In the design process, in order to avoid the “singularity” and “explosion of complexity” problems, an adaptive command filtered fixed‐time NN fault‐tolerant controller is devised. Based on the Lyapunov stability theory, all output signals of leader and followers in the closed‐loop system are synchronized within fixed time. Finally, the effectiveness of the proposed control scheme is verified by simulation experiments.

Adjustable dimension descriptor observer based fault estimation for switched nonlinear systems with partially unknown nonlinear dynamics

This paper focuses on the fault estimation problem for switched systems with partially unknown nonlinear dynamics, actuator and sensor faults, simultaneously. The fault estimation observers are constructed, in which the observer dimension is not fixed and can be selected in a certain range. Both the disturbance decoupling and disturbance attenuation are considered, where the unknown nonlinear dynamics can be decoupled and the effect of modeling error and measurement disturbance is attenuated. Based on the average dwell time and the piecewise Lyapunov function, the observer parameter matrices can be calculated by solving LMIs and matrix equations. Finally, two examples are listed to verify the proposed fault estimation approach.

An Ellipsoidal Set-Membership Approach to Distributed Joint State and Sensor Fault Estimation of Autonomous Ground Vehicles

This paper is concerned with the problem of distributed joint state and sensor fault estimation for autonomous ground vehicles subject to unknown-but-bounded (UBB) external disturbance and measurement noise. In order to improve the estimation reliability and performance in cases of poor data collection and potential communication interruption, a multi-sensor network configuration is presented to cooperatively measure the vehicular yaw rate, and further compute local state and fault estimates. Toward this aim, an augmented descriptor vehicle model is first established, where the unknown sensor fault is modeled as an auxiliary state of the system model. Then, a new distributed ellipsoidal set-membership estimation approach is developed so as to construct an optimized bounding ellipsoidal set which guarantees to contain the vehicle's true state and the sensor fault at each time step despite the existence of UBB disturbance and measurement noises. Furthermore, a convex optimization algorithm is put forward such that the gain matrix of each distributed estimator can be recursively obtained. Finally, simulation results are provided to validate the effectiveness of the proposed approach.

Adaptive fuzzy output feedback fault-tolerant tracking control of switched uncertain nonlinear systems with sensor faults

This paper investigates the adaptive fuzzy output feedback fault-tolerant tracking control problem for a class of switched uncertain nonlinear systems with unknown sensor faults. In this paper, since the sensor may suffer from an unknown constant loss scaling failure, only actual output can be used for feedback design. A failure factor is employed to represent the loss of effectiveness faults. Then, an adaptive estimation coefficient is introduced to estimate the failure factor, and a state observer based on the actual output is constructed to estimate the system states. Fuzzy logic systems are used to approximate the unknown nonlinear functions. Based on the Lyapunov function method and the backstepping technique, the proposed control scheme with average dwell time constraints can guarantee that all states of the closed-loop system are bounded and the tracking error can converge to a small neighborhood of zero. Finally, two simulation examples are given to illustrate the effectiveness of the proposed scheme.

Event-Triggered Adaptive Neural Network Sensor Failure Compensation for Switched Interconnected Nonlinear Systems With Unknown Control Coefficients.

In this article, a decentralized adaptive neural network (NN) event-triggered sensor failure compensation control issue is investigated for nonlinear switched large-scale systems. Due to the presence of unknown control coefficients, output interactions, sensor faults, and arbitrary switchings, previous works cannot solve the investigated issue. First, to estimate unmeasured states, a novel observer is designed. Then, NNs are utilized for identifying both interconnected terms and unstructured uncertainties. A novel fault compensation mechanism is proposed to circumvent the obstacle caused by sensor faults, and a Nussbaum-type function is introduced to tackle unknown control coefficients. A novel switching threshold strategy is developed to balance communication constraints and system performance. Based on the common Lyapunov function (CLF) method, an event-triggered decentralized control scheme is proposed to guarantee that all closed-loop signals are bounded even if sensors undergo failures. It is shown that the Zeno behavior is avoided. Finally, simulation results are presented to show the validity of the proposed strategy.

A new result of terminal sliding mode finite-time state and fault estimation for a class of descriptor switched systems

The paper investigates the finite-time state and fault estimation problem for continuous-time descriptor switched systems. A more representative faulty plant is considered, where unknown additive sensor and process faults, the Lipschitzian disturbance in state dynamics, and the external composite disturbance in the measured output exist simultaneously. Compared to some existing actuator models, a more representative actuator fault model is given, where some bounded conditions of the degradation and the additive fault are canceled. The actuator empirical efficiency is also introduced to estimate the time-varying actuator degradation. An important highlight is the extension of the Lyapunov terminal sliding mode (TSM) condition of finite-time stability into descriptor switched systems, where the method of average dwell time (ADT) is considered. A novel adaptive sliding mode observer is developed by constructing an augmented system model, and the relevant rank conditions are proved. To validate the effectiveness of the proposed observer, a descriptor switched circuit system is simulated under different control inputs and various actuator fault models. It can be seen that the TSM finite-time state and fault observer acquires the fast and accurate estimation of the targeted vector.

Adaptive fuzzy output feedback FTC for nonstrict-feedback systems with sensor faults and dead zone input

This paper solves the fault-tolerant control (FTC) problem of uncertain nonlinear systems in nonstrict-feedback form. The controlled plants considered in this paper are more complicated than existing ones, which is composed of the unknown nonlinearities, unmeasured states, and sensor faults and unknown dead zone input nonlinearity. Fuzzy logic systems and a fault-estimation-based state observer are used for identifying the unknown nonlinearities and obtaining the unmeasured states, respectively. By designing the sensor faults compensation and the dead zone inverse compensation methods, an observer-based fault-tolerant compensation control scheme is developed under backstepping recursive design frame. And it is testified that the closed-loop signals are bounded, and the tracking performance is satisfied even when the controlled systems are not free of sensor faults and unknown dead zone input nonlinearity. An electromechanical system is used to test the effectiveness of the developed control strategy.