Faulty Actuators Research Articles

Prescribed‐time fault‐tolerant synchronization control of complex dynamical networks using time‐varying protocols

AbstractThis research delves into the intricacies of the prescribed‐time synchronization control problem of a class of complex dynamical systems, all while factoring in the presence of time‐varying actuator faults. An innovative time‐varying protocol is initially established for the case of additive actuator faults, hinging on the solution to a particular set of parametric Lyapunov equations. Through the astute exploitation of the scalarization properties intrinsic to the solution to the parametric Lyapunov equation, the time‐varying parameter within the protocol is effectively ascertained. Furthermore, to mitigate the impact stemming from multiplicative actuator fault(s), an alternative protocol is also introduced. Through rigorous analysis rooted in the principles of Lyapunov, it is demonstrated that the synchronization errors in both scenarios remain bounded, ultimately converging to zero within a predetermined time frame. The developed protocols are employed in coupled Chua's circuit control systems with faulty actuators, and simulation results validate the efficacy of the proposed approaches.

Model predictive control based integrated fault tolerant controller for autonomous vehicles with four-wheel independent steering, driving and braking systems

This paper presents a fault-tolerant control algorithm for path and velocity tracking of an autonomous vehicle based on four-wheel independent steering, driving, and braking systems. The proposed algorithm is designed to distribute the control effort to normal actuators in the occurrence of faults. The proposed algorithm consists of three modules. The reference velocity and yaw rate to track the reference path are determined by the reference state decision module. The model predictive control (MPC)-based fault-tolerant controller determines the optimal inputs to compensate for the effect of the faulty actuator and maintain the tracking performance. A faulty-factor estimator is proposed based on a recursive covariance estimator to evaluate the performance degradation due to the failure of each actuator. The estimated faulty factors are applied to the objective function of the MPC such that the control input is distributed based on the level of the fault. The performance of the proposed algorithm was verified via performing simulations using MATLAB/Simulink and CarSim. The simulations proved that the proposed controller maintained the control performance of the normal case even in multiple actuator failures.

Data-Driven Fault Detection and Isolation for Multirotor System Using Koopman Operator

This paper presents a data-driven fault detection and isolation (FDI) for a multirotor system using Koopman operator and Luenberger observer. Koopman operator is an infinite-dimensional linear operator that can transform nonlinear dynamical systems into linear ones. Using this transformation, our aim is to apply the linear fault detection method to the nonlinear system. Initially, a Koopman operator-based linear model is derived to represent the multirotor system, considering factors like non-diagonal inertial tensor, center of gravity variations, aerodynamic effects, and actuator dynamics. Various candidate lifting functions are evaluated for prediction performance and compared using the root mean square error to identify the most suitable one. Subsequently, a Koopman operator-based Luenberger observer is proposed using the lifted linear model to generate residuals for identifying faulty actuators. Simulation and experimental results demonstrate the effectiveness of the proposed observer in detecting actuator faults such as bias and loss of effectiveness, without the need for an explicitly defined fault dataset.

Disturbance-observer-based terminal sliding mode control of the quadrotor with thrust deviation fault

This paper presents a novel approach utilizing a sliding mode controller to control a quadrotor effectively in the presence of structural faults. While previous research has extensively explored fault-tolerant control for UAVs, particularly addressing faulty actuators and sensors, there remains a significant gap in addressing control methods for quadrotors experiencing structural faults. We focus specifically on scenarios where a fault in one of the quadrotor’s rotors diverts thrust from the vertical axis, complicating traditional control methods. Our proposed method involves two key steps: firstly, identifying the fault vector using a terminal sliding mode observer, and secondly, calculating appropriate control inputs using a sliding mode method based on the estimated fault vector. Simulation results demonstrate the robustness of this approach, showing accurate control performance even in the presence of multiple faults and disturbances. The system’s reliability hinges on the convergence of the disturbance observer’s estimation error to zero within a finite time frame. In conclusion, the developed controller offers a dependable solution for safely controlling a faulty quadrotor amidst high nonlinearity and uncertainty.

Asymptotic voltage restoration in islanded microgrids via an adaptive asynchronous event-triggered finite-time fault-tolerant control

As the actuators of distributed generators involved in Microgrids (MGs) may experience different faults, it is highly recommended to design an appropriate secondary Fault Tolerant (FT) controller to provide resilience and robustness with respect to these malicious events. Meanwhile, the reduction of communication network workload from controller-to-actuator may be helpful to mitigate actuators stress. By virtue of the above, this article aims at introducing a novel distributed adaptive FT-Event-Triggered Control (FT-ETC) scheme for secondary voltage regulation control in AC islanded MG able to compensate faulty actuators effects while reducing the waste of communication network resources. The proposed control scheme is derived with the aid of the backstepping method and the ETC theory, whose combination ensures resilience with respect to possible faulty signals, while no knowledge of global information is required. Adaptive mechanisms are derived by exploiting Lyapunov theory, while Zeno-freeness property is proven by contradiction. Compared with the existing state-of-the-art on FT-ETC in islanded MG, here the main novelty relies in achieving the asymptotic rather than the practical stability of the voltage tracking errors despite an unhealthy environment, while the remaining adaptive signals involved in the closed-loop systems are bounded in finite-time. Finally, an extensive simulation analysis, also involving real-time Hardware In-the-Loop (HIL) experiments, confirms the theoretical derivation.

Actuator and sensor fault isolation in a class of nonlinear dynamical systems

Fault isolation in dynamical systems is a challenging task due to modeling uncertainty and measurement noise, interactive effects of multiple faults and fault propagation. This paper proposes a unified approach for isolation of multiple actuator or sensor faults in a class of nonlinear uncertain dynamical systems. Actuator and sensor fault isolation are accomplished in two independent modules, that monitor the system and are able to isolate the potential faulty actuator(s) or sensor(s). For the sensor fault isolation (SFI) case, a module is designed which monitors the system and utilizes an adaptive isolation threshold on the output residuals computed via a nonlinear estimation scheme that allows the isolation of single/multiple faulty sensor(s). For the actuator fault isolation (AFI) case, a second module is designed, which utilizes a learning-based scheme for adaptive approximation of faulty actuator(s) and, based on a reasoning decision logic and suitably designed AFI thresholds, the faulty actuator(s) set can be determined. The effectiveness of the proposed fault isolation approach developed in this paper is demonstrated through a simulation example.

Robust fault-tolerant control for dynamic positioning of ships with prescribed performance

This study presents a new fault-tolerant control scheme for ship dynamic positioning. The scheme aims to handle unknown disturbances, imprecise fault estimation, and control input constraints. First, an adaptive sliding mode disturbance observer is introduced. This observer effectively reconstructs disturbances in finite time, even without knowledge of their derivative upper bounds. Next, we design a novel prescribed performance function that sets bounds on tracking errors. This function is combined with an auxiliary intermediate control technique to create a high-level controller. Lastly, a robust fault-tolerant control allocation scheme is proposed to redistribute the generalized forces among faulty actuators. It autonomously finds a feasible force vector in the attainable force set. Simulation results are provided to demonstrate the effectiveness of the proposed control scheme.

Actuator fault detection and isolation in a class of nonlinear interconnected systems

In this paper, the problem of actuator fault detection and isolation is investigated for a class of nonlinear interconnected large-scale systems with modelling uncertainty and measurement noise, where each subsystem can have multiple inputs and multiple outputs (MIMO). The main contribution of this work is the derivation of a scheme that is able to diagnose single or multiple actuator faults in one or multiple subsystems. Each subsystem is monitored by a local diagnosis agent which contains the actuator fault detection module and the isolation module. The detection threshold in the fault detection module is generated through the use of a novel filtering technique, while the fault isolation module is realised by applying a reasoning-based decision logic based on a fault signature matrix. Fault propagation among subsystems is investigated and the results obtained allow for the identification of the subsystems that contain the faulty actuators. Finally, the effectiveness of the proposed actuator fault diagnosis method is demonstrated through a simulation example.

Fault-tolerant attitude control of the satellite in the presence of simultaneous actuator and sensor faults

In this paper, a robust attitude control algorithm is developed based on backstepping sliding mode control for a satellite using reaction wheels and thrusters that can perform its mission despite faulty actuators. In this method, the actuator dynamics have been considered to design the controller and the asymptotic stability of the proposed algorithm has been proven based on the Lyapunov theory. The designed controller can converge the attitude of the system into the desired path in the presence of faulty actuators. Then a fault-tolerant attitude estimation system is designed based on federated unscented Kalman filters that can be effectively employed to detect and isolate sensor faults. Finally, the performance of the designed attitude estimation and controller is investigated by simulation in the presence of both actuator and sensor faults.

Evaluation of Optimized Inner and Outer Loop Controllers for 4X Flyer under Faulty Actuators

Evaluation of Optimized Inner and Outer Loop Controllers for 4X Flyer under Faulty Actuators

A strong secure path planning/following system based on type-3 fuzzy control, multi-switching chaotic systems, and random switching topology

This paper studies the synchronization and control of chaotic systems while proposing a novel chaotic-based path-tracking application for mobile robots (MRs) to ensure their safety and security. In security-based applications that use MRs, such as patrol MRs, the path of the MRs must be complex enough to prevent easy prediction. Multiple chaotic systems with a chaotic switching mechanism are introduced for secure path planning. The main challenges are that the dynamics of MRs are entirely unknown. The modeled dynamics of the MRs are unreliable in practice due to a broad range of uncertainties related to the parameters, operating conditions, environmental impacts, time delays, unmodeled frictions, noisy sensors, and faulty actuators. Also, the chaotic switching of reference signals between chaotic signals imposes a high dynamic perturbation. The main novelties are as follows: (1) a strong secure path is introduced for MRs. (2) A powerful fractional-order predictive controller using type-3 (T3) fuzzy-logic systems (FLSs) is developed. (3) The estimation and prediction errors of T3-FLSs are compensated by a designed parallel compensator. (4) T3-FLSs are tuned online, such that stability is ensured, and prediction accuracy is guaranteed. (5) The suggested scheme is implemented on a real-world MR, and the results demonstrate the feasibility and accuracy of the proposed method. Also, in several simulations, the efficacy of the introduced controller is examined.

Switching-based adaptive fault-tolerant control for uncertain nonlinear systems against actuator and sensor faults

In previous research on fault switching of redundant actuators using monitoring modules, the interference of sensor faults on monitoring results has not received enough attention. In this paper, a new fault-tolerant control (FTC) method combining adaptive compensation and active switching is proposed for a class of uncertain nonlinear systems with backup actuators against actuator and sensor faults. While using command filtered backstepping to reduce computational burden, a direct adaptive compensation scheme is designed to ensure that sensor faults within tolerable bounds do not trigger actuator switching. Based on this, a new lumped monitoring function (MF) containing prescribed performances and tolerable sensor fault bounds is designed to detect and isolate faulty actuators and ensure that the tracking error always satisfies prescribed transient and steady-state performances. Two simulation cases are used for illustrating the proposed method. It is proved that the proposed method can effectively reduce the interference of sensor faults on actuator fault detection and improve the accuracy of actuator switching.

Decentralized Sensor Fault-Tolerant Control of DC Microgrids Using the Attracting Ellipsoid Method.

System stability deterioration in microgrids commonly occurs due to unpredictable faults and equipment malfunctions. Recently, robust control techniques have been used in microgrid systems to address these difficulties. In this paper, for DC-islanded microgrids that have sensors faults, a new passive fault-tolerant control strategy is developed. The suggested approach can be used to maintain system stability in the presence of flaws, such as faulty actuators and sensors, as well as component failures. The suggested control is effective when the fault is never recognized (or when the fault is not being precisely known, and some ambiguity in the fault may be interpreted as uncertainty in the system's dynamics following the fault). The design is built around a derived sufficient condition in the context of linear matrix inequalities (LMIs) and the attractive ellipsoid technique. The ellipsoidal stabilization idea is to bring the state trajectories into a small region including the origin (an ellipsoid with minimum volume) and the trajectories will not leave the ellipsoid for the future time. Finally, computational studies on a DC microgrid system are carried out to assess the effectiveness of the proposed fault-tolerant control approach. When compared with previous studies, the simulation results demonstrate that the proposed control technique can significantly enhance the reliability and efficiency of DC microgrid systems.

Wind Turbine Active Fault Tolerant Control Based on Backstepping Active Disturbance Rejection Control and a Neurofuzzy Detector

Wind energy conversion systems have become an important part of renewable energy history due to their accessibility and cost-effectiveness. Offshore wind farms are seen as the future of wind energy, but they can be very expensive to maintain if faults occur. To achieve a reliable and consistent performance, modern wind turbines require advanced fault detection and diagnosis methods. The current research introduces a proposed active fault-tolerant control (AFTC) system that uses backstepping active disturbance rejection theory (BADRC) and an adaptive neurofuzzy system (ANFIS) detector in combination with principal component analysis (PCA) to compensate for system disturbances and maintain performance even when a generator actuator fault occurs. The simulation outcomes demonstrate that the suggested method successfully addresses the actuator generator torque failure problem by isolating the faulty actuator, providing a reliable and robust solution to prevent further damage. The neurofuzzy detector demonstrates outstanding performance in detecting false data in torque, achieving a precision of 90.20% for real data and 100% for false data. With a recall of 100%, no false negatives were observed. The overall accuracy of 95.10% highlights the detector’s ability to reliably classify data as true or false. These findings underscore the robustness of the detector in detecting false data, ensuring the accuracy and reliability of the application presented. Overall, the study concludes that BADRC and ANFIS detection and isolation can improve the reliability of offshore wind farms and address the issue of actuator generator torque failure.

Deep Learning-Based Robust Actuator Fault Detection and Isolation Scheme for Highly Redundant Multirotor UAVs

This article presents a novel approach for detecting and isolating faulty actuators in highly redundant Multirotor UAVs using cascaded Deep Neural Network (DNN) models. The proposed Fault Detection and Isolation (FDI) framework combines Long Short-Term Memory (LSTM)-based fault detection and faulty actuator locator models to achieve real-time monitoring. The study focuses on a Hexadecarotor multirotor UAV equipped with sixteen rotors. To tackle the complexity of FDI resulting from redundancy, a partitioning technique is introduced based on system dynamics. The proposed FDI scheme is composed of a region classifier model responsible for detecting faults and fault locator models that precisely determine the location of the failed actuator. Extensive training and testing of the models demonstrate high accuracy, with the regional classifier model achieving 98.97% accuracy and the fault locator model achieving 99.107% accuracy. Furthermore, the scheme was integrated into the flight control system of the UAV, before being tested via both real-time monitoring in the simulation environment and analysis of recorded real flight data. The models exhibit remarkable performance in detecting and localizing injected faults. Therefore, using DNN models and the partitioning technique, this research offers a promising method for accurately detecting and isolating faulty actuators, thereby improving the overall performance and dependability of highly redundant Multirotor UAVs in various operational scenarios.

Adaptive Fault-Tolerant Tracking Control for Uncertain Nonlinear Systems With Unknown Control Directions and Limited Resolution

This article addresses the adaptive fault-tolerant tracking control (FTTC) problem for a family of strict-feedback uncertain nonlinear systems subject to limited sensor resolution and unknown control directions. Both partial loss-of-effectiveness (LOE) and lock-in-place (LIP) faults of actuators are investigated. An adaptive control strategy based on a Nussbaum-type function and neural networks is presented by introducing a backstepping approach to make the system output track a desired reference output signal with bounded tracking error in the case of faulty actuators. The effect of the limited resolution is decoupled from the nonlinear system and is approximated using a neural network. The impact of disturbances is effectively compensated by utilizing adaptive parameter estimation terms in the backstepping procedure. It is proven that the proposed FTTC strategy can ensure the boundedness of all signals and guarantee that the output tracking error can converge into a small neighborhood of the origin. Two simulation examples are given to illustrate the effectiveness of the proposed FTTC strategy.

ACTUATOR AND SENSOR FAULT COMPENSATION USING PROPORTIONAL-PROPORTIONAL INTEGRAL OBSERVER FOR FUZZY TRACKING CONTROL OF PENDULUM-CART SYSTEM

The pendulum-cart system is a popular system plant as a case study in nonlinear control design and implementation. The controllability and system performance can be influenced by the effectivity of the actuator and sensor. However, actuator and sensor fault sometimes is inevitable and can be occurred during operation. This paper considers fault-tolerant control (FTC) to minimize the actuator and sensor fault. The control objective is to track the sinusoidal reference position of the cart while the pendulum is maintained upright in which the faulty actuator and sensor occurred. Takagi-Sugeno (T-S) fuzzy tracking control is designed based on a compensator scheme where the Proportional-Proportional Integral Observer (PPIO) is utilized for this scheme. The Linear Matrix Inequalities (LMIs) are used to calculate the controller and observer gains. The performance of the proposed controller is verified through simulation and experimental validation. The effectiveness of FTC in the case of actuator and sensor fault is given. The system responses for the compensated and uncompensated controllers (to track the reference signal) are compared. In the case of a sensor fault, only the compensated controller can converge to the reference signal. However, in the case of actuated fault, both compensated and uncompensated controllers converge to the reference signal but the error of the compensated controller is better than the other one.

Robust Reduced Order Thau Observer With the Adaptive Fault Estimator for the Unmanned Air Vehicles

Developing fault detection and diagnoses algorithms for the unmanned air vehicles such as the quadrotors is challenging since they are intrinsically non-linear, time-varying, unstable, and uncertain. This paper develops a reduced order Thau observer by only considering the uncertain rotational dynamics, which are re-constructed as the dominant linear and non-linear for the design purpose. Therefore, the proposed Thau observer is just third order and can reveal a rotational state estimation error in the presence of the quadrotor faults. This paper also equips the proposed Thau observer with a simple online adaptive fault estimation law, which is able to recognize up to two faulty actuators instantly using the estimated rotational state error. Lyapunov analysis confirms the error convergence in both the Thau observer states and the adaptive fault estimates. In addition, this paper constructs a batch type least-squares projection approach to quantify the magnitude percentages of the actuator failures. Moreover, to show the feasibility of the proposed algorithm, this paper extensively analyses the fault detection and diagnosis results performed in the simulation and real-time environments. Finally, to demonstrate the superiority of the proposed algorithm, it is compared with a recent Kalman filter based quadrotor fault estimation research under the equal conditions.

Modeling and Analysis of the Active Surface System for the Large Single-Dish Sub-mm Telescope

Enlarging antenna diameter while maintaining an accurate reflective surface is important in the development of next-generation single-dish sub-mm telescopes. In this article, we propose an emulation and optimization method of the active surface system (EOMASS) for large single-dish sub-mm telescopes. The proposed method involves three main steps: 1) building an active surface system model (ASSM) based on the whole finite element model (FEM) of the large single-dish sub-mm telescope, which includes the main reflector, realistic adjustment models of actuators, backup structure and so on; 2) emulating the reflective surface deformation compensation under gravity based on the initial distribution of the actuators with the ASSM, in consideration of the manufacturing error of panels; and 3) further optimizing the number and distribution of actuators based on the illumination function, and predicting the performance of the sub-mm antenna due to the limited number of faulty actuators. As an application example, a 60 m single-dish sub-mm telescope is analyzed under gravity with the proposed EOMASS. The results demonstrate that the proposed EOMASS is an effective and advanced method, providing a feasible precision design of the active surface system for a large single-dish sub-mm telescope.

Designing Resilient Linear Systems

Critical systems must be designed resilient to malfunctions and especially to a loss of control authority over actuators. This malfunction considers actuators producing uncontrolled and possibly undesirable outputs. We investigate the design of resilient linear systems capable of reaching their target even after such a malfunction. In contrast with the settings considered by robust control and fault-tolerant control, we consider undesirable but observable inputs of the same magnitude as controls since they are produced by a faulty actuator belonging to the system. The control inputs can then depend on these undesirable inputs. Building on our previous work, we focus on designing resilient systems able to withstand the loss of one or multiple actuators. Since resilience refers to the existence of a control law driving the state to the target, we naturally continue with the synthesis of such a control law. We conclude with the application of our theory to the ADMIRE-fighter-jet model.