Fault-tolerant Control Method Research Articles

Robust fault detection and adaptive fixed-time fault-tolerant control for quadrotor UAVs

This note scrutinizes an adaptive fault-tolerant control (FTC) approach tailored for unmanned aerial vehicles (UAVs), addressing the critical need for both fault accommodation and disturbance suppression. Departing from traditional reliance on robust discontinuous control strategies prone to chattering and demanding precise uncertainty bounds, our FTC method ensures fixed-time stability, guaranteeing the convergence of attitude tracking errors to zero. Central to our approach is an adaptive algorithm adept at concurrently estimating unknown actuator faults and upper bounds of lumped uncertainties. Moreover, our adaptive schemes accurately estimate the upper bound of the lumped uncertainty term, encompassing model uncertainties, external disturbances, and unmodeled dynamics, thereby eliminating the need for assuming known bounds on uncertainties. Stability analysis under the developed control law is thoroughly performed using the Lyapunov stability theory. Notably, our strategy employs an extended Kalman filter (EKF) observer for state estimation and fault detection, facilitating fault detection through an adaptive threshold technique dynamically adjusted based on real-time mean and variance of the residual signal. Through comprehensive simulation and experimental validations, our proposed methodology demonstrates significant advancements in ensuring safety and reliability in UAVs.

Active Fault Tolerant Nonsingular Terminal Sliding Mode Control for Electromechanical System Based on Support Vector Machine

Effective fault diagnosis and fault-tolerant control method for aeronautics electromechanical actuator is concerned in this paper. By borrowing the advantages of model-driven and data-driven methods, a fault tolerant nonsingular terminal sliding mode control method based on support vector machine (SVM) is proposed. A SVM is designed to estimate the fault by off-line learning from small sample data with solving convex quadratic programming method and is introduced into a high-gain observer, so as to improve the state estimation and fault detection accuracy when the fault occurs. The state estimation value of the observer is used for state reconfiguration. A novel nonsingular terminal sliding mode surface is designed, and Lyapunov theorem is used to derive a parameter adaptation law and a control law. It is guaranteed that the proposed controller can achieve asymptotical stability which is superior to many advanced fault-tolerant controllers. In addition, the parameter estimation also can help to diagnose the system faults because the faults can be reflected by the parameters variation. Extensive comparative simulation and experimental results illustrate the effectiveness and advancement of the proposed controller compared with several other main-stream controllers.

Robust Predictive Fault-Tolerant Control under Actuator Random Failures

A robust predictive fault-tolerant switching control method is presented for a class of industrial processes that are prone to random actuator failures. The primary contribution is the combination of stochastic control theory with robust predictive control to represent the occurrence of system failure in the form of probability, thus significantly enhancing the conventional fault-tolerant control approach. First, a state-space model of the system is established, which is then transformed into an enhanced form that includes state deviations and output tracking errors. This augmented model enables the formulation of fault-tolerant switching control laws with fault probabilities, ensuring system convergence and tracking capabilities. Subsequently, linear matrix inequality constraints are given to find the switching controller gains under recovery and fault probabilities. Under certain probabilities, failure triggers fault-tolerant control, while recovery prompts a shift back to conventional control. Final, we validate the feasibility of our approach with a case study involving pressure regulation.

Adaptive Iterative Learning Constrained Control for Linear Motor-Driven Gantry Stage with Fault-Tolerant Non-Repetitive Trajectory Tracking

This article introduces an adaptive fault-tolerant control method for non-repetitive trajectory tracking of linear motor-driven gantry platforms under state constraints. It provides a comprehensive solution to real-world issues involving state constraints and actuator failures in gantry platforms, alleviating the challenges associated with precise modeling. Through the integration of iterative learning and backstepping cooperative design, this method achieves system stability without requiring a priori knowledge of system dynamic models or parameters. Leveraging a barrier composite energy function, the proposed controller can effectively regulate the stability of the controlled system, even when operating under state constraints. Instability issues caused by actuator failures are properly addressed, thereby enhancing controller robustness. The design of a trajectory correction function further extends applicability. Experimental validation on a linear motor-driven gantry platform serves as empirical evidence of the effectiveness of the proposed method.

Performance-Based Hierarchical Fault-Tolerant Control for Closed-Loop Systems With Multiplicative Faults: A Data-Driven Design Method.

Fault-tolerant control (FTC) is vital for the safety and reliability of automatic systems. Most of the existing FTC methods are developed for open-loop systems subject to additive faults, regardless of the widely present control loops and multiplicative faults within systems. In this article, a performance-based FTC strategy is proposed for the closed-loop systems with multiplicative faults. Considering the high efforts in modeling complex systems, the proposed FTC strategy is realized in the data-driven context. Specifically, a nominal feedback-feedforward controller is first established for the fault-free systems. By selecting the system stability and reference tracking behavior as the key performance indices, two performance evaluators are constructed to detect and classify the occurred multiplicative faults based on the fault-induced effects on the system performance. Then, with the aid of the coprime factorization technique, the multiplicative faults, in the form of additive perturbations to the system coprime factors, are estimated utilizing the closed-loop process data. Furthermore, based on the fault knowledge, a hierarchical fault-tolerant tracking controller is developed according to the levels of system performance degradations, where the functional controller parameters are reconfigured with different priorities. Finally, case studies are provided to validate the effectiveness of the proposed method.

FAULT-TOLERANT CONTROL ALGORITHM FOR AUTOMOTIVE ELECTRONIC MECHANICAL BRAKING RAMP ASSIST

The electronic mechanical braking system directly affects the driving safety of automobiles, but there are auxiliary control methods. Therefore, a fault-tolerant control algorithm for automotive electronic mechanical braking ramp assistance is proposed. The faults of the electronic mechanical braking ramp assistance system and select sensor faults are analyzed as the main research object. By introducing a BP neural network, the longitudinal speed of the vehicle, the wheel speed, the difference between the target slip rate and the current slip rate, and the rate of change of the difference are taken as the network input. Online learning is used to adjust and update the weight coefficients of each layer of the network, output fault diagnosis results and optimized PI controller parameters, and achieve fault-tolerant control. Three different types of sensor fault signals were artificially added in the experimental testing. After the proposed fault-tolerant control method, the controlled signals were used to replace the fault signals, ensuring the normal driving of the car.

Fault-Tolerant Phototaxis of a Modular System Inspired by Gonium pectorale Using Phase-Based Control

In this study, we proposed a model for modular robots in which autonomous decentralized modules adaptively organize their behavior. The phototaxis of Gonium pectorale, a species of volvocine algae, was modeled as a modular system, and a fault-tolerant modular control method of phototaxis was proposed for it. The proposed method was based on the rotation phase of the colony and adaptively adjusted an internal response-related parameter to enhance the fault tolerance of the system. Compared to a constant parameter approach, the simulation results demonstrated a significant improvement in the phototaxis time for positive and negative phototaxis during module failures. This method contributes to achieving autonomous, decentralized, and purposeful mediation of the modules necessary for controlling modular robots.

Adaptive sliding mode and RBF neural network based fault tolerant attitude control for spacecraft with unknown uncertainties and disturbances

Taking the external disturbance, model uncertainty, actuator failure, and actuator saturation into consideration, this paper investigates the nonlinear fault-tolerant attitude control problem for the spacecraft. First, to deal with the disturbance and uncertainty with unknown boundaries, an adaptive sliding mode control method is proposed to stabilize the state variables of the spacecraft attitude control system. Then the actuator failure and saturation are further considered, and the second spacecraft fault-tolerant attitude controller is derived based on adaptive sliding mode control and radial basis function (RBF) neural networks. The adaptive control gains reduction technique is applied in the design process, which can weaken the chattering phenomenon of the controller to some extent, and the stability of the attitude control system is analyzed through Lyapunov theory. In the proposed controller, model information and external disturbance are not required and only the system states are needed. Furthermore, the boundaries of the model uncertainty, external disturbance, actuator fault and saturation are assumed to be existing but unknown by the proposed controllers. These features make the proposed attitude controllers need few modeling information of the spacecraft, or maintain strong robustness even if the model or external environment occurs significant changes. Finally, numerical simulations demonstrate the great performance of the proposed fault-tolerant attitude control method.

Research on fault-tolerant control methods for Bi-rotor vehicles under actuator failure

A PID-self-impedance fault-tolerant controller based on the inner and outer loop structure is developed for a new form of bi-rotor vehicle, taking into account the fault-tolerant control problem when the actuator fails. First, the dynamics model and actuator fault model of the bi-rotor vehicle are established, the PID active fault-tolerant controller with control parameter switching is selected for the outer loop, and the self-immune controller is selected for the inner loop. Second, when a fault occurs, the outer loop PID controller’s settings are switched to the controller parameters of the relevant fault state based on the extent of the fault, and the parameters are matched with the robust controller of the inner loop to achieve fault-tolerant control. Finally, simulation experiments show that the designed fault-tolerant controller can still achieve stable tracking of position and attitude in the presence of actuator faults. This indicates that the flight control system is robust to fault-induced disturbances and effectively improves the vehicle’s fault-tolerant performance.

Neuroadaptive Fault-Tolerant Control Embedded With Diversified Activating Functions With Application to Auto-Driving Vehicles Under Fading Actuation.

This article presents a neuroadaptive fault-tolerant control method for path tracking of multiinput multioutput (MIMO) systems in the presence of modeling uncertainties and external disturbances. In dealing with modeling uncertainties, neural networks (NNs) with diversified activation/basis functions are considered, with which we establish a set of control algorithms that are robust against uncertainties, adaptive to unknown parameters, and tolerant to actuation faults. This is the first work that explicitly takes into account the neural weights uncertainties and activating function uncertainties in multiple layered neural networks in control design. In addition, we apply the developed control algorithms to unmanned ground vehicles (UGVs) with actuator failures. With the aid of Lyapunov stability theory, it is shown that the proposed control is able to drive the vehicle along the desired path with high precision and all the internal signals are uniformly ultimately bounded (UUB) and continuous. Both theoretical analysis and numerical simulation confirm the effectiveness of the designed strategy.

Fault-Tolerant Control Method for Active Steering Control of Rail Vehicles Based on Overdrive Characteristics

Fault-Tolerant Control Method for Active Steering Control of Rail Vehicles Based on Overdrive Characteristics

Progressive Optimal Fault-Tolerant Control Combining Active and Passive Control Manners

This study develops a progressive optimal fault-tolerant control method based on insufficient fault information. By combining passive and active fault-tolerant control manners during the process of fault diagnosis, insufficient fault information is fully used, and optimal fault-tolerant control effect is achieved. In addition, the fault-tolerant control method based on guaranteed robust cost control is introduced. The proposed progressive optimal fault-tolerant control method considers two aspects. First, as the amount of fault information continually increases, the performance index of the progressive optimal fault-tolerant controller improves. Second, at each moment, based on the corresponding insufficient fault information and prior knowledge, optimal fault-tolerant control is achieved according to current fault information. The process of progressive optimal fault-tolerant control converges to active fault-tolerant control when the fault is completely identified, and the optimal fault-tolerant controller is no longer reconfigured until no more useful fault information can be provided. Furthermore, a progressive optimal fault-tolerant control algorithm based on the grid segmentation in the parameter uncertainty domain and the selection of different auxiliary center points is introduced. Simulation results verified the feasibility of the proposed algorithm and the validity of the proposed theory.

Adaptive EKF enhanced fault diagnosis and fault tolerant control for space manipulators with position measurements only

Space manipulators play a critical role in the operation of space robots. However, the failure of these manipulators can affect the control performance and even lead to instability. To improve the safety and robustness of space manipulators, this paper presents an efficient fault diagnosis and fault tolerant control method by integrating adaptive extended Kalman filter (AEKF) and sliding mode control (SMC). The proposed method uses AEKF for system estimation, taking into account uncertainties in the process and measurement noise, as well as sensor limitations. By detecting the failure of angle sensors timely, the trajectory tracking performance can be guaranteed by the robustness of AEKF. In cases where a joint experiences free-swinging, the failure can be identified based on the estimated control effectiveness coefficients from AEKF. Then, once the failure is identified, the joint angle can be regulated by exploiting the motion coupling between the joints. Furthermore, SMC is employed to mitigate the effects of unknown disturbances in the joints. The effectiveness of the proposed method is demonstrated through numerical simulation, which illustrates its ability to diagnose failures and maintain control performance under various failure scenarios. In addition, the robustness of the method is verified through Monte Carlo simulation, demonstrating its reliability in dealing with various uncertainties.

Fault detection and fault-tolerant control of dual-motor autonomous steering system

In order to improve the safety of autonomous ground vehicle (AGV) and the accuracy of trajectory tracking, a fault detection (FD) scheme of steering system and a fault-tolerant control (FTC) method are proposed. First, this paper analyzes the causes of steering motor failure and designs a passive fault-tolerant controller to ensure that the torque motor under coordinated control can still follow the angle when a slight fault occurs in the angle motor. Second, the corresponding FD schemes are designed for different motor faults, and the unknown input observer (UIO) of the steering resistance moment is designed considering the uncertainty of the steering system caused by the fault motor. The active fault-tolerant controller composed of backstepping control and global sliding mode control (SMC) is further combined to control the fault-free motor. Finally, simulation and HiL test results show the effectiveness of the proposed FD scheme and FTC strategy.

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.

Fault-tolerant control of the main drive system of rolling mill based on fault reconfiguration

A mathematical model of the main drive system of rolling mill is developed for the motor armature faults in the main drive system of rolling mill, taking into account the effects of nonlinear damping, friction damping between rolls and external perturbations on the system during operation. The unknown input observer is designed to process perturbations and errors as unknown inputs, and the [Formula: see text] performance index is designed to improve the robustness of the unknown input observer to fault reconfiguration. At the same time, in order to ensure the stability of the main drive system of rolling mill after the occurrence of faults, an active fault-tolerant controller is designed for the main drive system of rolling mill after obtaining the accurate fault reconfiguration value, which realizes the active fault-tolerant control of the main drive system of rolling mill. Through the simulation study on the main drive system of 2030 mm cold rolling mill stand F4, it is shown that the system is restored to the normal state after adding the fault-tolerant controller, and the motor angular speed error and roll angular speed error are 2.12% and 2.43%, respectively. Comparing with the fault-tolerant control method based on fault estimation, the root-mean-square errors of the motor angular speed and roll angular speed estimates of the designed fault-tolerant control method are reduced by 4.24% and 3.28%, respectively. Simulation verifies the effectiveness of the proposed method.

Finite-time state-constrained adaptive fault-tolerant control for heavy launch vehicles with input quantization

This paper proposes the finite-time state-constrained adaptive fault-tolerant control method for a class of heavy launch vehicles subject to quantized input signals and actuator faults. Firstly, the attitude dynamic model of the heavy launch vehicles suffering from the actuator faults and disturbances is formulated. Secondly, to deal with the completely unknown nonlinear functions, radial basis function neural networks (RBFNNs) are introduced for approximation and compensation. Meanwhile, the barrier Lyapunov function (BLF) is introduced to ensure that all the states in the closed-loop system are bounded and the constraints of the system states are satisfied. As a result, the finite-time state-constrained adaptive fault-tolerant control structure is constructed for the heavy launch vehicles and the semi-globally practical finite-time stability (SGPFS) of the closed-loop control system is proved. Finally, numerical simulation results show the effectiveness and the satisfactory performance of the proposed control algorithm.

A Fault-Tolerant Control Method Based on Reconfiguration SPWM Signal for Cascaded Multilevel IGBT-Based Propulsion in Electric Ships

Electric ships have been developed in recent years to reduce greenhouse gas emissions. In this system, inverters are the key equipment for the permanent-magnet synchronous motor (PMSM) drive system. The cascaded insulated-gated bipolar transistor (IGBT)-based H-bridge inverter is one of the most attractive multilevel topologies for modern electric ship applications. Usually, the fault-tolerant control strategy is designed to keep the ship in operation for a certain period. However, the fault-tolerant control strategy with hardware redundancy is expensive and slow in response. In addition, after fault-tolerant control, the ship’s PMSM may experience shock and overheating, and IGBT life is reduced due to uneven switching frequency distribution. Therefore, a stratified reconfiguration carrier disposition Sinusoidal Pulse Width Modulation (SPWM) fault-tolerant control strategy is proposed. The proposed strategy can achieve fault tolerance without any extra hardware. A reconfiguration carrier is applied to improve the fundamental amplitude of inverter output voltage to maintain the operation of the ship’s PMSM. In addition, the available states of faulty H-bridge are fully used to contribute to the output. These can improve the life of IGBTs by reducing and balancing the power loss of each H-bridge. The principles of the proposed strategy are described in detail in this study. Taking a cascaded H-bridge seven-level inverter as an example, simulation and experimental results verify that the proposed strategy, in general, has a potential future application on electric ships.

Fault-tolerant heading control for a bionic gliding robotic dolphin with flipper faults

This paper presents a fault-tolerant heading-control method for a bionic gliding robotic dolphin operating in the presence of flipper faults. To achieve both high manoeuvrability and long endurance, a bionic gliding robotic dolphin that integrates the propulsion modes of dolphins and underwater gliders is introduced. The operational conditions of the flippers are considered meticulously, and common fault types that can arise during the gliding process, such as stuck, failure, and efficiency-loss faults, are analysed. These faults can affect the heading capabilities of the gliding robotic dolphin. To ensure normal navigation in the presence of flipper faults, a fault-tolerant heading control method is devised for the gliding robotic dolphin. This method applies backstepping sliding control to establish control laws based on a dynamic model. Subsequently, a flipper angle solver is designed to convert the controller outputs into deflection angles for the flippers. Simulation results obtained under three typical flipper faults demonstrate the effectiveness of the proposed fault-tolerant control method. This fault-tolerant control method ensures the safety of gliding robotic dolphins under complex deep-sea conditions.

An adaptive fault tolerant control method for electromagnetic formation spacecraft with external disturbances

Fault-tolerant control (FTC) is an essential consideration for the reason that the proper functioning of electromagnetic formation spacecraft (EMFS) actuators are challenged by the harsh space environment. An Integral-Type Sliding Mode Control (ISMC) scheme is proposed for actuator faults and external disturbances (AFED) caused by coil failure and external complex environment of EMFS system. Before the system state reaches the sliding mode surface, we use super-twisting algorithm to accelerate the reaching of the sliding mode surface and reduce the chattering. After reaching the sliding surface, a novel nominal proportional passive controller is proposed that integrates a damping term, thereby enhance system robustness and expediting system stabilization. Moreover, the FTC method proposed incorporates adaptive techniques to handle unknown AFED in real-time while ensuring system stability. This paper proposes a new adaptive FTC method that enhances the actuator’s capability without requiring controller reconfiguration. Simulation results illustrate the effectiveness of this method for suppressing AFED in harsh space environment.