Fault-tolerant Control Research Articles

Fault Tolerance Analysis and Optimization of Centralized Control Platform Based on Artificial Intelligence and Optimization Algorithm

To enhance the reliability and self-healing of the system, the research on fault tolerance of the reconfigurable modular centralized control center is its development trend. Most of the previous research has focused on hardware redundancy. Improving fault tolerance performance is an essential topic in the research of centralized control platforms. Firstly, the problem of centralized fault tolerance in the working configuration of a reconfigurable manipulator is studied. The effect of each hinge on fault tolerance in the existing configuration is studied with the criterion of manoeuvrability and tolerable space. The fault module was first modelled to represent the system architecture information. A modular motion rule based on autonomous recombination technology is proposed. A self-organizing deformation algorithm with fault tolerance is studied. The fault tolerance of the motion pairs is compensated by adding a small number of motion pairs to ensure the configuration characteristics. With the addition of a failure compensation device, the joint’s range of motion was reduced, and the fault tolerance rate was enhanced. After the failure of the robot arm, the fault tolerant control method can still ensure that the robot arm can perform work in its tolerable working space. The test results show that the fault tolerance analysis method is practical and feasible. It lays a theoretical foundation for the application in aerospace, industry and other fields.

Adaptive fixed-time fuzzy fault-tolerant control for robotic manipulator with unknown friction and composite actuator faults

This paper exploits the issue of fixed-time (FT) tracking control (TC) for uncertain robotic manipulator systems (RMS) with composite actuator faults (CAFs), unknown disturbances and joint friction. The fuzzy logic system (FLS) algorithm is introduced to fit the unknown uncertainties including unmodeled parameters, CAFs and joint friction of the RMS, after which a relevant updating algorithm is employed to generate the unidentified boundary of the plant disturbance and FLS approximation error. Then a novel adaptive fuzzy sliding mode (SM) fault-tolerant controller is synthesized, which not only guarantees the boundedness of estimation errors of the involved adaptive parameters, but also actualizes the reachability of the predevised sliding surface. Furthermore, the proof that the system trajectories can achieve the FT convergence to the expected position signals, is then provided on account of the FT stability theory. Ultimately, simulation experiments exhibit the rationality based upon the current control algorithm.

Adaptive fault-tolerant control for ascent HSV with wing damage and function constraints on states

In this article, a fault-tolerant tracking control technique is developed for the ascent of hypersonic vehicles (HSV) experiencing wing damage and time-varying state constraints. The difficulty of control problem lies in handling state constraints associated with both state and time, while determining the unknown control directions of the multi-input multi-output (MIMO) nonlinear system. The presence of constraints in both state and time complicates the design of the control algorithm. This paper proposes the construction of a novel state transition function to establish a new unconstrained system, thereby eliminating the conservative feasibility conditions of Barrier Lyapunov Function (BLF) and integral BLF methods when dealing with time-varying constraints. The fusion of time-varying scaling functions and Nussbaum-type functions not only resolves the issue of unknown control directions in the MIMO nonlinear system but also enhances the transient and steady-state performance of the closed-loop system. Neural Networks are employed to approximate the unknown nonlinear functions. Using Lyapunov stability theory, it is proven that state constraints are not violated, and all signals in the closed-loop system are bounded. Simulation results demonstrate the merits of the designed fault-tolerant control (FTC) algorithm.

Robust Fault-tolerant Control with Dynamic Event-triggered Mechanism Based on Observer for Nonlinear Switched Systems

Robust Fault-tolerant Control with Dynamic Event-triggered Mechanism Based on Observer for Nonlinear Switched Systems

Analysis of Closed Loop Current Controlled BLDC Motor Drive

Brushless direct current (BLDC) motors are mostly preferred for dynamic applications such as automotive industries, pumping industries, and rolling industries. It is predicted that by 2030, BLDC motors will become mainstream of power transmission in industries replacing traditional induction motors. Though the BLDC motors are gaining interest in industrial and commercial applications, the future of BLDC motors faces indispensable concerns and open research challenges. Considering the case of reliability and durability, the BLDC motor fails to yield improved fault tolerance capability, reduced electromagnetic interference, reduced acoustic noise, reduced flux ripple, and reduced torque ripple. To address these issues, closed-loop vector control is a promising methodology for BLDC motors. In the literature survey of the past five years, limited surveys were conducted on BLDC motor controllers and designing. Moreover, vital problems such as comparison between existing vector control schemes, fault tolerance control improvement, reduction in electromagnetic interference in BLDC motor controller, and other issues are not addressed. This encourages the author in conducting this survey of addressing the critical challenges of BLDC motors. Furthermore, comprehensive study on various advanced controls of BLDC motors such as fault tolerance control, Electromagnetic interference reduction, field orientation control (FOC), direct torque control (DTC), current shaping, input voltage control, intelligent control, drive-inverter topology, and its principle of operation in reducing torque ripples are discussed in detail. This paper also discusses BLDC motor history, types of BLDC motor, BLDC motor structure, Mathematical modeling of BLDC and BLDC motor standards for various applications. Key Words: Brushless direct current, industries, direct torque control.

Neural network adaptive switched fault-tolerant control of uncertain nonlinear systems with full state constraints

This paper studies the neural network adaptive fault-tolerant control (NNAFTC) for a class of uncertain nonlinear systems with full state constraints. It is the first time to introduce switched adaptive fault compensation strategy for the controller design process. On the one hand, the proposed method overcomes the limitation needed to be given a prescribed local restriction region of the traditional state constraint schemes, such as log-type, tan-type and integral-type. On the other hand, the full-state constraint and switched fault-tolerant problem is effectively solved even if the existence of actuator failures. Concomitantly, the novel switched tuning functions, the NN approximation technique and the integral barrier Lyapunov functions (IBLF) are combined to construct the backstepping controllers and adaptation laws. It can be proved that all the closed-loop states are restricted in the proper compact sets for any given initial values. Finally, A simulation is shown to demonstrate the validity and advantages of the presented control approach.

Adaptive containment control for heterogeneous MASs with unknown leaders and actuator failures

This paper investigates the containment control problem for heterogeneous multi-agent systems with unknown leaders and actuator failures. The dynamics of the leaders are unavailable to all followers and the measurable information of leaders is available to some followers. A model-free adaptive observer for each follower is proposed to estimate the convex hull of the leaders states and the dynamic knowledge of the unknown leaders. The followers may be affected by the actuator failures. Based on the adaptive observer and the adaptive solution of the output regulation equation, an adaptive fault-tolerant control protocol is designed to mitigate the impact of actuator failure on containment performance such that the output containment errors are ultimately uniformly bounded. Two simulation examples are provided to demonstrate the feasibility of the presented control scheme.

Dynamic output feedback based fault tolerant control for continuous-time switched affine systems via reduced-order observer

In this study, the issue on dynamic output feedback fault-tolerant controller design is addressed for a class of continuous-time switched affine systems in the presence of actuator faults. The existence of affine terms gives rise to the lack of a common equilibrium point and further makes it more difficult to propose the desired output-feedback-based fault-tolerant control scheme. Firstly, the actuator fault estimation is achieved through a reduced-order observer design approach. Then, a kind of dynamic output feedback controller is constructed to perform the fault tolerance goal by utilizing fault estimation information. Moreover, the mode-dependent average dwell time switching constraint is borrowed to avoid the chattering owing to the high-frequency switching. The sufficient conditions with less conservative are derived to guarantee the practical exponential stability of closed-loop system with a prescribed L∞ gain. Finally, both the effectiveness and validity of the designed fault-tolerant control strategy are illustrated via a case study of a buck-boost DC-DC converter.

A comprehensive investigation of brushless direct current (bldc) motors: current state, advanced control strategies, and utilization systems

In the field of dynamic applications, specifically within automotive, pumping, and rolling sectors, there exists a noteworthy preference for the use of Brushless Direct Current (BLDC) motors. Projections show that, by the year 2030, BLDC motors are poised to supersede classic induction motors as the dominating force in industrial power transmission. This transformation, however, is accompanied by crucial issues and unresolved research challenges in the environment of BLDC motors.The core concern revolves around the dependability and endurance of BLDC motors. These motors presently encounter obstacles in achieving advanced fault tolerance, reduced electromagnetic interference, lowered acoustic noise, as well as mitigated flux and torque fluctuations. The path of closed-loop vector control emerges as a possible technique to address these challenges.In recent literature studies spanning the previous five years, a striking scarcity of exploration in the domain of BLDC motor controllers and design becomes clear. Furthermore, key areas such as the comparative study of existing vector control schemes, the increase of fault tolerance control, the attenuation of electromagnetic interference inside BLDC motor controllers, and other pivotal elements remain undiscovered. These research lacunae serve as a motivator for the undertaking of an intensive investigation to face the fundamental issues related with BLDC motors. BLDC motors have quickly become the motor of choice for electric vehicle (EV) applications due to the fact that they are reliable, simple, and energy efficient.This detailed survey goes deep into numerous sophisticated control strategies for BLDC motors. These encompass fault tolerance control, electromagnetic interference reduction, field orientation control (FOC), direct torque control (DTC), current shaping, input voltage control, intelligent control, drive-inverter topology, and the underlying operational principles aimed at the minimization of torque irregularities. Additionally, the study goes through the historical narrative of BLDC motors, the categorization of BLDC motor kinds, their structural complexity, mathematical modeling, and the standards that govern BLDC motor uses in varied industries.

Optimal fuzzy event-triggered fault-tolerant control of fractional-order nonlinear stochastic systems

This paper considers the optimal fuzzy fault-tolerant control issue for fractional-order nonlinear stochastic systems (FONSS) using event-triggered mechanisms with fractional-order derivative α∈(0,1). First, in order to weaken the impacts of actuator and sensor faults, a fuzzy observer and augmented system are developed. Since stochastic disturbance can adversely affect stability, based on fractional Barbalat lemmas and Lyapunov theory, an adaptive fault-tolerant controller and the corresponding adaptive update law are designed to ensure the stability of FONSS. Then, the architecture of identifier-critic-actor NN, which is more concise than the traditional one, is described. This NN architecture recognizes unknown dynamics and completes control actions after evaluating the control performance, driving control toward optimization. Furthermore, the event-triggered mechanism is devised to save control resources and avoid Zeno behavior. Finally, simulation results demonstrate that under equivalent conditions, the proposed optimal event-triggered control reduces the number of information transmissions compared to traditional time-triggered control, saving communication resources and enhancing the overall control performance.

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.

Integral sliding mode-based event-triggered optimal fault tolerant tracking control of continuous-time nonlinear systems

In this paper, the integral sliding mode-based event-triggered optimal fault tolerant tracking control of continuous-time nonlinear systems is investigated via adaptive dynamic programming. The developed control scheme consists of two parts, i.e., integral sliding mode control and event-triggered optimal tracking control. For the first part, an integral sliding mode controller is designed to eliminate the affect of actuator fault and the dynamics of nominal nonlinear systems is obtained. For the second part, a novel quadratic cost function with respect to the tracking error and its dynamics is developed such that the feedforward control law or the discount factor is not required, which reduces the complexity of the control method and guarantees the tracking performance. Moreover, a critic-only structure is established to obtain the solution of tracking Hamilton–Jacobi–Bellman equation. It should be noted that the optimal tracking control law is updated only at triggering moments in order to preserve computing and communication resources. Finally, the effectiveness of the present approach is demonstrated through simulation examples of a robotic arm system and a Van der Pol circuit system.

Fixed-Time Collision-Free Fault-Tolerant Formation Control of Multi-UAVs Under Actuator Faults.

When cooperating through an intensive formation, the safe distancing of unmanned aerial vehicles (UAVs) is a delicate issue, especially if UAVs are subjected to actuator faults that cause rapid maneuvers. This article investigates the fixed-time fault-tolerant formation control of multiple quadrotor UAVs under actuator faults, which considers the collision avoidance among UAVs when faults occur, and the convenience of engineering application. First, an augmented fixed-time observer with measurement noise oppression is adopted to estimate and compensate actuator faults and disturbance in rotational and translational dynamics. Then, a baseline attitude controller, a command filter, and a velocity controller are proposed for each quadrotor UAV to track the desired velocity within a fixed time. Next, a distributed fixed-time sliding-mode controller that integrates the gradient of repulsive potential function into the sliding manifold is designed to achieve leader-follower formation control and collision avoidance simultaneously. The control scheme is proven to be fixed-time convergent via Lyapunov stability analysis and is normalized in accordance with the compatibility of hardware implementation. Finally, the designed algorithm is embedded into PX4 architecture to illustrate the effectiveness and practicality of the control strategy.

Observer-Based Fault-Tolerant Control for Uncertain Robot Manipulators without Velocity Measurements

In recent years, robot manipulator arms have become increasingly prevalent and are playing pivotal roles across various industries. Their ability to replace human labor in arduous and hazardous tasks has positioned them as indispensable assets. Consequently, there has been a surge in research efforts aimed at enhancing their operational performance. The imperative to improve their efficiency and effectiveness has garnered significant attention within the research community. In this study, a novel fault-tolerant control (FTC) scheme for robot manipulators to handle the effects of the unknown input is proposed to aid robots in achieving good tracking performance. In the first step, an extended state observer (ESO) is constructed to approximate both velocities and the unknown input in the robot system. The observer offers estimation information with good accuracy and quick convergence. The estimated signals are then combined with computed torque control (CTC), which is a useful control technique for trajectory tracking of robot manipulator systems, to construct an active FTC to decrease the influences of the unknown input. The proposed algorithm does not require velocity measurement in the design process. In addition, with a novel design approach, the combination of controller and observer provides a novel control signal that delivers higher tracking performance compared to the traditional design approach. The global and asymptotic stability of the suggested technique is proved through the Lyapunov theory. Finally, simulations are implemented on a 2-degree-of-freedom (DOF) robot manipulator to validate the efficiency of the proposed controller–observer method.

Fault-tolerant control of bullet train based on fuzzy adaptive control

Abstract For the trajectory tracking problem of a single-particle train dynamics model under the condition of partial loss of power in the train power unit due to actuator failure during the operation of the EMU, considering the situation where the system exists with uncertain dynamics from the model and unknown external disturbers, a control method based on fuzzy adaptive fault-tolerant control method for EMU is proposed. Under the framework of the backstepping method, the fuzzy system is used to approach the system function, and a fuzzy adaptive controller is designed. It is proved that the designed control law can enable the train system to track the given speed and displacement trajectory under partial power loss. Using the CRH3 train as the research object, the results show that the designed controller can complete the trajectory tracking control task.

Fault-tolerant control against actuator faults based on extended state observer for quadrotor UAV

Abstract A model predictive control (MPC) strategy is devised to handle the issue of multi-actuators partial failure faults of quadrotor vehicles. Firstly, considering a quadrotor system with actuator partial failure faults and unknown disturbances, the quadrotor dynamics model is analyzed and established. Then, the MPC attitude fault-tolerant controller is devised to transform the issue of controlling the attitude angle of a quadrotor into a quadratic programming solution problem, and the extended state observer (ESO) is devised to evaluate the faults and perturbations, which highly and greatly improves the system’s suppression capability against disturbances. Finally, numerical simulation experiments demonstrate the scheme’s fault-tolerance to actuator faults and its ability to suppress unknown perturbations, while the results of semi-physical simulation experiments also prove that the method is effective and feasible.

Fault-Tolerant Adaptive Control for Trajectory Tracking of a Quadrotor Considering State Constraints and Input Saturation

Fault-Tolerant Adaptive Control for Trajectory Tracking of a Quadrotor Considering State Constraints and Input Saturation

Learning-Based Minimally-Sensed Fault-Tolerant Adaptive Flight Control

Learning-Based Minimally-Sensed Fault-Tolerant Adaptive Flight Control

Auto-berthing Control for MSVs with a Time-based Generator under Actuator Faults: A Concise Neural Single-Parameter Approach

Abstract In this paper, we study the control problem of auto-berthing marine surface vessels (MSVs) within a predefined, finite time in the restricted waters of a port, in the face of internal and external uncertain dynamics and actuator faults. We first use radial basis function neural networks to reconstruct the internal uncertainties of the system; then, using the minimum learning parameter method, we transform the weights of the neural networks, the external disturbances of the system, and the bias fault factors into an indirect single-parameter neural learning mode. We also apply a robust depth information adaptation technique to estimate the upper bound on the composite disturbances online. Dynamic surface control technology alleviates the burden of virtual control derivative calculations. Finite-time convergence of the system is guaranteed by a predetermined finite-time function based on a time-based generator (TBG). Based on these methods, we design a finite-time fault-tolerant auto-berthing control scheme based on TBG. The stability of the system is analysed based on Lyapunov stability theory. Finally, we verify the effectiveness of the proposed control scheme through simulation.

Dynamic Event-Triggered and Self-Triggered Fault-Tolerant Attitude Control for Multiple Spacecraft Systems With Uncertainties and Input Saturation

Dynamic Event-Triggered and Self-Triggered Fault-Tolerant Attitude Control for Multiple Spacecraft Systems With Uncertainties and Input Saturation