Fuzzy Fault-tolerant Control Research Articles

Observer-based adaptive fuzzy quantized fault-tolerant control of nonstrict-feedback nonlinear systems with actuator and sensor faults

This article emphasises the adaptive quantised fault-tolerant output feedback control problem for one type of nonstrict-feedback nonlinear systems suffering from input and output quantisation as well as actuator and sensor faults. By introducing a bounded time-varying function (BTVF), a generalised intermediate-variable estimator (IVE) is designed to estimate actuator bias fault. For the scaling measurement sensor fault, by applying a projection operator containing the measured output quantised signal, a novel gain-compensated state observer is constructed. In addition, relying on fuzzy logic system (FLS) and intelligent adaptive algorithm, a fuzzy fault-tolerant controller is devised to handle dual faults and quantisation. After theoretical proof, the presented control framework can realise that the closed-loop system is semi-globally uniform ultimately bounded (SGUUB), while the tracking error is within an adjustable area. Lastly, the validity and superiority of the suggested scheme is confirmed by a relevant simulation example.

Fixed-Time Adaptive Fuzzy Fault-Tolerant Control of Flapping Wing MAVs With Wing Damage

Fixed-Time Adaptive Fuzzy Fault-Tolerant Control of Flapping Wing MAVs With Wing Damage

Adaptive Fuzzy Fault-Tolerant Attitude Control for a Hypersonic Gliding Vehicle: A Policy-Iteration Approach

In this paper, adaptive fuzzy fault-tolerant control (AFFTC) for the attitude control system of a hypersonic gliding vehicle (HGV) experiencing an actuator fault is proposed. Actuator faults of the HGV are considered with respect to its actual structure and actuator characteristics. The HGV’s attitude system is firstly represented by a T–S fuzzy model, and then a normal T–S fuzzy controller is designed. A reinforcement learning (RL)-based policy iterative solution algorithm is proposed for the solving of the T-S fuzzy controller. Then, based on the normal T–S controller, a fuzzy FTC controller is proposed in which the control matrices can improve themselves according to the special fault. An integral reinforcement learning (IRL)-based solving algorithm is proposed to reduce the dependence of the design methods on the HGV model. Simulations on three different kinds of actuator faults show that the designed IRL-based FTC can ensure a reliable flight by the HGV.

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.

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.

Integral reinforcement learning–based adaptive fuzzy fault tolerant control of hypersonic flight vehicle

The fault tolerant control (FTC) of hypersonic flight vehicle (HFV) with actuator fault is proposed in this paper. The fault model considered in this paper is a general model which HFV may encounter in practice. For designing the FTC, the nonlinear model of HFV is represented by T-S fuzzy models, and policy iteration (PI) strategy is utilized to solve the design problem of T-S controller for the built T-S model of HFV without actuator fault. Then, based on the normal T-S controller, an adaptive fuzzy FTC controller is proposed, in which the feedback gain matrices can improve themselves according to the special fault. The stability of the proposed adaptive fuzzy FTC is proved by Lyapunov theory, and an integral reinforcement learning (IRL)–based solving algorithm is proposed. Simulations on three different kinds of actuator faults are proposed, and the simulation results show that, under three different faults, the designed adaptive FTC can ensure the reliable flight of HFV.

Event-Triggered Adaptive Fuzzy Fault-Tolerant Attitude Control for Tailless Flying-Wing UAV With Fixed-Time Convergence

Event-Triggered Adaptive Fuzzy Fault-Tolerant Attitude Control for Tailless Flying-Wing UAV With Fixed-Time Convergence

Adaptive fuzzy fault‐tolerant control of stochastic nonlinear multiagent systems with input quantization and state constraints

SummaryThis paper is dedicated to the problem of adaptive fuzzy fault‐tolerant control (FTC) for a class of stochastic nonstrict feedback nonlinear multi‐agent systems (MASs) with input quantization and full state constraints. Firstly, based on the backstepping technique and the bounded estimation method, the effect of actuator faults can be eliminated by the proposed adaptive updating laws. Secondly, the stochastic barrier Lyapunov functions (BLFs) are constructed to ensure that all state constraints are not violated. Meanwhile, the introduction of input quantization can effectively save network resources and reduce computational burden. It is further shown that all the closed‐loop signals are uniformly bounded, and the consensus tracking errors asymptotically tend to zero. The effectiveness of the proposed control scheme is illustrated by a numerical example.

Event-Triggered Prescribed Performance Adaptive Fuzzy Fault-Tolerant Control for Quadrotor UAV With Actuator Saturation and Failures

Event-Triggered Prescribed Performance Adaptive Fuzzy Fault-Tolerant Control for Quadrotor UAV With Actuator Saturation and Failures

Fault diagnosis and fault tolerant control for distributed drive electric vehicles through integration of active front steering and direct yaw moment control

For the purpose of improving the lateral stability in the path following process for the distributed drive electric vehicles subject to multiple actuator/sensor faults, a new fault diagnosis and fault tolerant control method is proposed in this paper through the integration of the active front steering (AFS) and direct yaw-moment control (DYC). Firstly, considering the actuator/sensor faults and parameter uncertainties together, a Takagi–Sugeno fuzzy model is established to describe the integrated AFS/DYC system with nonlinear dynamics. Secondly, because the fault information plays a key role in the fault tolerant control, a fuzzy estimation observer is presented to diagnose actuator/sensor faults online. Thirdly, considering the high cost of the measurement sensors for lateral speed, it is hard to measure the complete state of the vehicle. Thus, in the frame of dynamic output feedback structure, a fuzzy fault tolerant control method is proposed to improve the path following performance and guarantee the dynamic stability. Finally, the hardware-in-the-loop experimental results of two typical driving maneuvers show that the proposed control method has a superior performance in the improvement of the driving stability and active safety.

Relaxed Stabilization of Event-Triggered Interval Type-2 T–S Fuzzy Positive Systems With Stochastic Actuator Failure

This paper studies the membership-function-dependent (MFD) stability and positivity analysis of positive Interval Type-2 (IT2) Takagi-Sugeno (T-S) fuzzy fault-tolerant control systems under the event-triggered control framework. Considering the fact that parameter uncertainties may exist in nonlinear plants, the IT2 T-S fuzzy model as an effective mathematical tool is employed to approximately express a complex positive system with nonlinearities and uncertainties. In order to save communication resources and improve the security of system operation, an event-triggered fault-tolerant control strategy is then designed based on the Imperfectly Premise Matching (IPM) concept. In addition, for capturing positivity characteristic and matching with the linear co-positive Lyapunov function, a novel event-triggered condition in the 1-norm rather than in the 2-norm is developed to control the positive IT2 T-S fuzzy systems. Thereinto, the non-convex problem as the first obstacle is required to be cleared since feasible solutions usually cannot be obtained based on the non-convex conditions. For dealing with this problem, a valid approach is presented to conduct convexification with the help of a user-chosen constant vector. Besides, because of event-triggered action, the inherent mismatched issue of the premise variables of the fuzzy model and the ones of the fuzzy controller is encountered. Fortunately, the advanced MFD analysis, which is capable of bringing the information contained in the membership functions (MFs) into the stability and positivity conditions, provides an effective strategy to remove this barrier. Consequently, relaxed results which can accomplish the stability and positivity for wider positive systems are developed and displayed in sum-of-squares (SOS) form. Detailed simulation results are given to clarify the effectiveness of the designed event-triggered fault-tolerant control method.

Optimal adaptive fuzzy fault-tolerant control applied on a quadrotor attitude stabilization based on particle swarm optimization

This article discusses the problem of stabilizing the attitude control of a quadrotor system, which is subject to uncertainty, external disturbances, and sensor and actuator faults. To address these challenges, the control stage employs universal approximators, such as fuzzy systems, which estimate the system’s uncertainties and eliminate nonaffine nonlinear actuator faults. In addition, the particle swarm optimization technique is used to adjust the adaptive parameters and fuzzy initial values. The robust control term is carefully designed to handle approximation errors, time-varying sensors, and external disturbances. To solve the issue of the unavoidable algebraic loop during the actuator approximation phase, a Butterworth low-pass filter is integrated. This approach automatically deals with external disturbances, and no further approximation is necessary. Furthermore, the controller can be reconfigured online to enable fast-fault compensation without requiring a fault detection or isolation unit. To prove the global stability and boundedness of all signals in the closed-loop system, Lyapunov theory is used.

Robust Adaptive Fuzzy Fault Tolerant Control of Robot Manipulators With Unknown Parameters

This paper resolves the safety tracking problem of the robot manipulator with the actuator faults, process faults, uncertain dynamics, and external disturbances. A novel robust adaptive fuzzy fault tolerant control (FTC) framework is presented. In this approach, a constraint mechanism of the zero overshoot error is proposed to keep the filter variable within an adjustable range, and the compact set can be available. An adaptive fuzzy logic system (FLS) is, then, employed to approximate the time-varying, nonlinear, and unknown robot dynamics. Furthermore, a robust adaptive term is designed to compensate the actuator faults and approximation errors, and ensure the convergence and stability of the whole robot control system. The uniform ultimate boundedness of the closed-loop dynamics for the robot manipulator is proved by the Lyapunov function. Afterwards, the simulation results demonstrate that the proposed robust adaptive fuzzy FTC (RAFFTC) approach of the robot manipulator has good control performance. Finally, the effectiveness of the proposed RAFFTC approach is verified by experiments.

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

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

Disturbance observer-based fuzzy fault-tolerant control for high-speed trains with multiple disturbances

Abstract The fault-tolerant control problem is investigated for high-speed trains with actuator faults and multiple disturbances in this paper. Based on the novel train model resulting from Takagi and Sugeno fuzzy theory, a sliding-mode fault-tolerant control strategy is proposed. The norm bounded disturbances which are composed of interactive forces among adjacent carriages and basis running resistances are rearranged by the fuzzy linearity technique. The modeled disturbances described as an exogenous system are compensated by a disturbance observer. Moreover, a sliding mode surface is constructed, which can transform the stabilization problem of position and velocity into the stabilization problem of position errors and velocity errors, i.e., the tracking problem of position and velocity. Based on the parallel distributed compensation method and the disturbance observer, the fault-tolerant controller is solved. Lyapunov theory is used to prove the stability of the closed-loop system. The feasibility and effectiveness of the proposed fault-tolerant control strategy are illustrated by simulation results.

Observer-Based Adaptive Fuzzy Quantized Fault-Tolerant Control of Nonstrict-Feedback Nonlinear Systems With Sensor Fault

This paper focuses on the problem of adaptive quantized fault tolerant tracking control for a class of uncertain nonstrict-feedback nonlinear systems in the presence of input and output quantization and sensor fault. The main technical challenges come from how to deal with the input and output quantization errors and the partial loss of sensors' effectiveness. Firstly, a new observer including the quantized input and output signals and the estimation of fault parameter is designed to reconstruct the unavailable states, and some special techniques are developed to deal with the sector-bounded quantization errors. Furthermore, based on the cubic absolute-value Lyapunov function, the adaptive fault compensation strategy is presented to make up for the influence from sensor fault upon the control performance. Then, with the aid of fuzzy logic system (FLS) approximation method and backstepping technique, a novel output feedback adaptive fuzzy fault tolerant control (FTC) scheme is proposed for this class of systems, such that all signals in the closed-loop are semi-globally uniformly ultimately bounded (SGUUB) while the tracking error remains in a small vicinity of the origin. Finally, the effectiveness and applicability of the proposed control scheme are validated by conducting two simulation examples.

Event-Triggered Prescribed Performance Fuzzy Fault-Tolerant Control for Unknown Euler–Lagrange Systems With Any Bounded Initial Values

This paper investigates the tracking problem of event-triggered prescribed performance fuzzy fault-tolerant control (FTC) for unknown Euler-Lagrange systems with actuator faults and external disturbances. Firstly, the barrier Lyapunov functions (BLFs) and prescribed performance functions are synthesized to guarantee that the tracking errors satisfy the preset transient performance. Different from existing prescribed performance control methods, which require the initial values of the tracking errors to be within the prescribed performance functions, an error transformation method is introduced to ensure that the tracking errors with any bounded initial values can enter the preset boundaries within a preset time. Then, considering the unavailability of system parameters, the fuzzy logic systems (FLSs) are used to approximate unknown parameters of the system. What's more, to solve the problem of limited communication and computing resources in practical systems, an improved event-triggered control (ETC) scheme is proposed, which can reduce the communication and computation burden without satisfying the input-to-state stability (ISS) assumption. Meanwhile, the Zeno phenomenon can be avoided. Furthermore, the effects of actuator faults and the event-triggered mechanism are handled by Nussbaum gain technology. Finally, the superiority of the proposed control algorithm is verified by simulation results.

Observer-based [formula omitted] fuzzy fault-tolerant switching control for ship course tracking with steering machine fault detection

To enhance the robustness of ship autopilot (SA) system with nonlinear dynamics, unmeasured states, and unknown steering machine fault, an observer-based H∞ fuzzy fault-tolerant switching control for ship course tracking is proposed. Firstly, a global Takagi–Sugeno (T-S) fuzzy nonlinear ship autopilot (NSA) is developed with full consideration of ship steering characteristics. And the actual navigation data collected from a real ship are used to verify the reasonableness and feasibility of NSA model. Then, virtual fuzzy observers (VFOs) for both fault-free and faulty systems are proposed to estimate the unmeasured states and unknown fault simultaneously, and compensate for the faulty system by using the fault estimates. Accordingly, the VFO-based H∞ robust controller (VFO-HRC) and fault-tolerant controller (VFO-HFTC) are designed. Subsequently, a smoothed Z-score-based fault detection and alarm (FDA) is developed to provide switching signals for which the controller and its corresponding observer should be invoked. Finally, simulation results on the “Yulong” ship demonstrate the effectiveness of the developed control method.

Interval Type-II Fuzzy Fault-Tolerant Control for Constrained Uncertain 2-DOF Robotic Multi-Agent Systems with Active Fault Detection

This study proposed a novel adaptive interval Type-II fuzzy fault-tolerant control for constrained uncertain 2-DOF robotic multi-agent systems with an active fault-detection algorithm. This control method can realize the predefined-accuracy stability of multi-agent systems under input saturation constraint, complex actuator failure and high-order uncertainties. Firstly, a novel active fault-detection algorithm based on pulse-wave function was proposed to detect the failure time of multi-agent systems. To the best of our knowledge, this was the first time that an active fault-detection strategy had been used in multi-agent systems. Then, a switching strategy based on active fault detection was presented to design the active fault-tolerant control algorithm of the multi-agent system. In the end, based on the interval type-II fuzzy approximated system, a novel adaptive fuzzy fault-tolerant controller was proposed for multi-agent systems to deal with system uncertainties and redundant control inputs. Compared with other relevant fault-detection and fault-tolerant control methods, the proposed method can achieve predefinition of stable accuracy with smoother control input. The theoretical result was verified by simulation.

Finite-Time Adaptive Fuzzy Control for Unmodeled Dynamical Systems with Actuator Faults

This article concentrates upon the issue of finite-time tracking control for a category of nonlinear systems in pure-feedback form with actuator faults and unmodeled dynamics, where the loss of effectiveness and bias fault are considered. Meanwhile, the function approximation method utilizing fuzzy logic systems and dynamic surface control approach with first-order filter are implemented to model the unknown nonlinear terms induced from the proposed controller procedure and tackle the “explosion of complexity” issue of the classic backstepping method. The use of the maximal norm of the weight vector estimation method and adaptive approach reduces the computation load induced by fuzzy logic systems. Within the framework of backstepping control, a finite-time adaptive fuzzy fault-tolerant control protocol is derived to guarantee the boundedness of all signals and tracking error of the controlled system within a finite-time. Simulation studies are offered to show the validity of the derived theoretical results of the finite-time control protocol.