Adaptive Fault-tolerant Control Method Research Articles

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.

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.

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.

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.

Adaptive Fault-Tolerant Tracking Control for Discrete-Time Nonstrict-Feedback Nonlinear Systems With Stochastic Noises

This paper discusses the problem of fault-tolerant tracking control for discrete-time nonstrict-feedback nonlinear systems in the presence of stochastic noises and actuator faults. The system is characterized by a discrete-time nonstrict-feedback structure and multiplicative stochastic noise related to the states, which poses a challenge for the design and analysis of the fault-tolerant controller. Moreover, the considered actuator faults include multiplicative and additive faults without prior information. By integrating the properties of the backstepping framework and neural networks, and by introducing an adaptive fault compensation term, a novel fault-tolerant tracking control strategy is proposed. The effects of faults are compensated and the difficulties caused by the system structure are overcome, avoiding the algebraic loop problem and overcoming the causal contradiction. Given specific parameters, the designed fault-tolerant controller can ensure that the tracking error converges to an adjustable region regarding the origin and that all system signals are uniformly bounded concerning the mean-square sense. The simulation examples illustrate the effectiveness of the designed fault-tolerant control method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —As system complexity has been increasing, practical systems are often affected by faults when performing tracking control. Fault-tolerant control can provide acceptable robustness and improve system safety. On the one hand, many practical systems are nonlinear, difficult to model mathematically and accurately, and affected by stochastic noises. On the other hand, with digital control, the object is a discrete-time system. In practical applications, the upper bounds of the faults and the noises are often unknown. In this paper, a novel adaptive fault-tolerant control method is proposed for the more general uncertain discrete-time nonlinear systems, i.e., nonstrict-feedback nonlinear systems, with stochastic noises and actuator faults. Neural networks and an adaptive fault compensation term are integrated into the fault-tolerant controller. By adjusting the controller parameters properly, it is ensured that the tracking error converges to a small neighborhood of zero in the event of a fault occurring. In future research, sensor faults, input constraints, optimal control design, and applications in multi-agent systems will be considered.

Robust adaptive fault-tolerant control for path maneuvering of autonomous surface vehicles with actuator faults based on the noncooperative game strategy

This paper investigates a parameterized path-guided following/maneuvering control problem of an autonomous surface vehicle (ASV) subject to actuator faults via a noncooperative game strategy. Unlike the existing path maneuvering control methods, the noncooperative game of this paper includes two sub-games. On one hand, the path update is considered to be against the effort of kinematic control. On the other hand, the kinetic control is considered to be against the total disturbances consisting of actuator faults and external disturbances. A robust adaptive fault-tolerant path maneuvering controller is designed based on a noncooperative game approach. Specifically, we design a kinematic control law using an improved dynamic surface control approach to achieve the geometric objective. An improved neural predictor is constructed, in which a concurrent learning-based adaptation is designed to identify unknown fault coefficients. A path update law and a kinetic control law are calculated to cover noncooperative games by using an adaptive dynamic programming approach. Theoretical analysis shows that the closed-loop system is input-to-state stable, and the proposed method can meet the geometric objective, the dynamic objective, and the fault-tolerant objective. Finally, simulation results demonstrate the efficacy of the proposed robust adaptive fault-tolerant control method for path maneuvering.

A Finite-time Adaptive Fault Tolerant Control Method for a Robotic Manipulator in Task-space With Dead Zone, and Actuator Faults

A Finite-time Adaptive Fault Tolerant Control Method for a Robotic Manipulator in Task-space With Dead Zone, and Actuator Faults

Unmodeled dynamics suppressed adaptive fault tolerant control for a class of space robots with actuator saturation and faults

This paper investigates the tracking control issue of a class of complicated space robots subject to actuator saturation and faults, unmodeled dynamics and external disturbances under asymmetric error constraints, and develops a Bias Radial Basis Function Neural Network (BIAS-RBFNN) based intelligent adaptive fault tolerant control scheme. First, considering that the unmodeled dynamics may be coupled with the states and inputs of the system, dynamic auxiliary signal and related mathematical tools are used to decouple and suppress the coupling uncertainties. Moreover, a mapping function based nonlinear error transformation technology is utilized to guarantee the transient performance of the system. Considering the shortcomings of traditional neural networks, the BIAS-RBFNNs have been constructed to improve the approximation and compensation performance. Finally, numerical simulations are carried out to illustrate the effectiveness and advantages of the proposed BIAS-RBFNN based intelligent adaptive fault tolerant control method.

Flight and Vibration Control of Flexible Air-Breathing Hypersonic Vehicles Under Actuator Faults.

The issue of modeling and fault-tolerant control (FTC) design for a class of flexible air-breathing hypersonic vehicles (FAHVs) with actuator faults is investigated in this article. Different from previous research, the shear deformation of the fuselage is considered, and an ordinary differential equations-partial differential equations (ODEs-PDEs) coupled model is established for the FAHVs. A feedback control is proposed to ensure flight stable and an adaptive FTC method is designed to deal with actuator faults while suppressing the system's vibrations. Besides, the stability analysis of the closed-loop system is given via the Lyapunov direct method and an algorithm that transfers the bilinear matrix inequalities (BMIs) feasibility problem to the linear matrix inequalities (LMIs) feasibility problem is provided for determining the control gains. Finally, the numerical simulation results show that the proposed controller can stabilize the flight states and suppresses the vibration of the fuselage efficiently.

FTC Design for Switched Fractional-Order Nonlinear Systems: An Application in a Permanent Magnet Synchronous Motor System.

In this article, an adaptive fault-tolerant control (FTC) method and a fractional-order dynamic surface control (DSC) algorithm are jointly proposed to deal with the stabilization problem for a class of multiple-input-multiple-output (MIMO) switched fractional-order nonlinear systems with actuator faults and arbitrary switching. In each MIMO subsystem and each switched subsystem, the neural networks (NNs) are utilized to identify the complicated unknown nonlinearities. A fractional filter DSC technology is adopted to conquer the issue of "explosion of complexity," which may occur when some functions are repeatedly derived. The common Lyapunov function method is used to restrain arbitrary switching problems in the system, and the actuator compensation technique is introduced to tackle the failure faults and bias faults in the actuators. By combining the backstepping DSC design technique and fractional-order stability theory, a novel NN adaptive switching FTC algorithm is proposed. Under the operation of the proposed algorithm, the stability and control performance of the fractional-order systems can be guaranteed. Finally, a simulation example of a permanent magnet synchronous motor (PMSM) system reveals the feasibility and effectiveness of the developed scheme.

Sliding Mode Disturbance Observer-Based Adaptive Dynamic Inversion Fault-Tolerant Control for Fixed-Wing UAV

Unmanned aerial vehicles (UAVs) have been widely applied over the past decades, especially in the military field. Due to the unpredictability of the flight environment and failures, higher requirements are placed on the design of the control system of the fixed-wing UAV. In this study, a sliding mode disturbance observer-based (SMDO) adaptive dynamic inversion fault-tolerant controller was designed, which includes an outer-loop sliding mode observer-based disturbance suppression dynamic inversion controller and an inner-loop real-time aerodynamic identification-based adaptive fault-tolerant dynamic inversion controller. The sliding mode disturbance observer in the outer-loop controller was designed based on the second-order super-twisting algorithm to alleviate chattering. The aerodynamic identification in the inner-loop controller adopts the recursive least squares algorithm to update the aerodynamic model of the UAV online, thereby realizing the fault-tolerant control for the control surface damage. The effectiveness of the proposed SMDO enhanced adaptive fault-tolerant control method was validated by mathematical simulation.

Impact angle constrained fuzzy adaptive fault tolerant IGC method for Ski-to-Turn missiles with unsteady aerodynamics and multiple disturbances

An impact angle constrained fuzzy adaptive fault tolerant integrated guidance and control method for Ski-to-Turn (STT) missiles subject to unsteady aerodynamics and multiple disturbances is proposed. Unsteady aerodynamics appears when flight vehicles are in a transonic state or confronted with unstable airflow. Meanwhile, actuator failures and multisource model uncertainties are introduced. However, the boundaries of these multisource uncertainties are assumed unknown. The target is assumed to execute high maneuver movement which is unknown to the missile. Furthermore, impact angle constraint puts forward higher requirements for the interception accuracy of the integrated guidance and control (IGC) method. The impact angle constraint and the precise interception are established as the object of the IGC method. Then, the boundaries of the lumped disturbances are estimated, and several fuzzy logic systems are introduced to compensate the unknown nonlinearities and uncertainties. Next, a series of adaptive laws are developed so that the undesirable effects arising from unsteady aerodynamics, actuator failures and unknown uncertainties could be suppressed. Consequently, an impact angle constrained fuzzy adaptive fault tolerant IGC method with three loops is constructed and a perfect hit-to-kill interception with specified impact angle can be implemented. Eventually, the numerical simulations are conducted to verify the effectiveness and superiority of the proposed method.

A Gradient Correction-based Decentralized Optimal Var/Volt Adaptive Fault-Tolerant Control Method for Wind Farms

This paper proposes a gradient correction-based decentralized optimal Var/Volt adaptive fault-tolerant control (GC-AFTC) method for wind farms (WFs). The aim is to ensure the decentralized optimal Var/Volt operation of wind turbines (WTs) inside a radial topology WF even under the fault condition due to several WTs being tripped. When some faults occur and several WTs are tripped, the decentralized optimal Var/Volt operation of WF still can be maintained without the reconstruction of the incidence matrix and any quantitative fault information of the fault WTs. To achieve the fault-tolerant optimal Var/Volt control, the impact of random off-grid events (ROEs) on the WF collection system is analyzed at the farm level. Subsequently, a unified GC method is derived based on the gradient projection (GP) method. The transient Var related to the ROE is incorporated into the GC method to enhance the capability of decentralized online applications of WTs. Additionally, to improve the dynamic performance of WTs, a personalized step coefficient is designed within a stable range, which is utilized in each local WT controller during the GC-AFTC period. Case studies show that the proposed method can drive the WF system towards a better operating point only with local measurements in normal and fault conditions.

Event-Triggered Adaptive Fault-Tolerant Control for Unknown Nonlinear Systems With Applications to Linear Motor

This article presents an event-triggered adaptive asymptotic tracking control method based on the low-complexity design architecture to reduce the communication and computational resources of uncertain nonlinear systems with unknown control directions and actuator faults. By utilizing a systematic codesign of backstepping and an event-triggered mechanism, the proposed adaptive fault-tolerant control method exploits only error transformation variables associated with prescribed performance specifications, without the use of any compensators or approximation structures to handle unknown nonlinearities and actuator and component faults. This design provides a robust method, through which the controller can effectively address external disturbances, unknown system uncertainties, and actuator and component faults, while eliminating the explosion of complexity problem in backstepping. Asymptotic stability analysis of the closed-loop system is conducted, and the Zeno behavior is avoided. The effectiveness of the proposed approach is illustrated through applications to linear motor systems.

Adaptive event-triggered control of Markovian jump complex dynamic networks with actuator faults

The adaptive event-triggered control of Markovian jump complex dynamic networks (MJCDN) with actuator faults is studied in this paper. A new adaptive fault-tolerant control method is introduced to prove that the synchronization error is asymptotically convergent. Compared with traditional event-triggered strategies, a more general adaptive event-triggered control scheme for MJCDN is proposed to reduce network traffic load more effectively. This paper not only considers the synchronization of MJCDN with actuator faults and time-varying delay but also studies the H∞ disturbance attenuation performance of MJCDN with random disturbance and actuator faults under adaptive event-triggered controller with appropriate adaptive laws. The Zeno behavior is ruled out effectively in these two different situations. In the end, two simulation examples are provided to illustrate the validity and rationality of the proposed methods about the adaptive event-triggered control of MJCDN with actuator faults.

Adaptive fault-tolerant control for attitude reorientation under complex attitude constraints

Rigid-body reorientation under attitude constraints has been studied extensively, but few of them take into account the actuator faults which occur frequently in practice. Since the actuator faults may lead to performance degradation or even system instability, a control scheme with fault-tolerant ability is important to enhance safety and reliability. However, it is still an open problem to solve attitude constraints and actuator faults simultaneously in condition of model uncertainties. In this paper, a control algorithm combining the potential function and adaptive fault-tolerant control method is proposed to tackle this challenging problem. We parameterize the kinematics using the singularity-free unit-quaternion representation, and assume perfect measurement of full-state variables. A potential function is first developed based on the geometric properties of constrained zones, which can guide the rigid body to the desired orientation without causing unwinding, while ensuring the satisfaction of complex forbidden and mandatory attitude constraints. Based on the judiciously constructed potential function, an adaptive fault-tolerant control scheme with saturated virtual control is then proposed in the framework of backstepping. Lyapunov stability analysis shows that the derived controller can guarantee asymptotic convergence of both the gradient-related vector and angular rate to zero, despite the presence of actuator faults, unknown inertia, and external disturbances. This indicates that the proposed controller is capable of achieving safe, reliable, and high-precision attitude reorientation maneuvers under complex attitude constraints and adverse operational conditions. Finally, numerical simulations demonstrate the effectiveness of the proposed controller.

Trajectory tracking of tail-sitter aircraft by ℓ1 adaptive fault tolerant control

This paper proposes an &#x2113;<inf>1</inf> adaptive fault tolerant control method for trajectory tracking of tail-sitter aircraft in the state of motor loss fault. The tail-sitter model considers the uncertainties produced by the features of nonlinearities and couplings which cause difficulties in control. An &#x2113;<inf>1</inf> adaptive controller is designed to reduce the position and attitude error when actuators have faults. A reference trajectory containing large maneuver flight transitions is designed, which makes it even harder for the &#x2113;<inf>1</inf> controller to track accurately. Compensators are designed to assist &#x2113;<inf>1</inf> adaptive controller tracking of the reference trajectory. The stability of the &#x2113;<inf>1</inf> adaptive controller including compensators is proved. Finally, the simulation results are used to analyse the effectiveness of the proposed controller. Compared to the H<inf>&#x221E;</inf> controller, the &#x2113;<inf>1</inf> adaptive controller with compensators has better performance in position control and attitude control under fault tolerance state even when the aircraft conducts large maneuver. Besides, as the &#x2113;<inf>1</inf> adaptive control method separates feedback control and adaptive law design, the response speed of the whole system is improved.

Adaptive fault-tolerant flutter control considering related failure

This article proposes a novel adaptive fault-tolerant control method for suppressing flutter and compensating for related failure in a flutter system. Considering cubic nonlinearity, external disturbances, and related failure, the flutter dynamic model was established firstly. Then, an adaptive fault-tolerant control law was proposed on basis of this model to compensate for related failure and suppress flutter. By Lyapunov stability analysis, the stability of proposed control law was proved in detail. On the last, simulation results further proved the effectiveness of the control law which can not only suppress flutter and compensate for related failure successfully but also has good robustness for external disturbances and system perturbation.

Adaptive Fault-Tolerant Control During the Mode Switching for Electric Vehicle Dual-Mode Coupling Drive System

As a new drive system for electric vehicles, the dual-mode coupling drive system can automatically switch between centralized and distributed drive modes and realize two-speed gear shifting. Because the actuator’s displacement signal affects the mode-switching control, when failure occurs at the angle-displacement sensor, the mode-shifting quality is likely to drop greatly, even possibly leading to shift failure. To address the angle-displacement sensor failure and improve the reliability of the shift control, an adaptive fault-tolerant control method is proposed and verified. First, the effect of the output signal of the angle-displacement sensor in the mode-switching control process is analyzed. Then, an adaptive mode-switching fault-tolerant control method is designed based on the Kalman filter and fuzzy theory. Finally, the feasibility of the control effect is verified through simulations and vehicle experiments. The results indicate that the proposed method can effectively eliminate the signal noise of the angle-displacement sensor and successfully switch the modes when the sensor fails. It provides a reference for ensuring the working quality of similar electric drive systems under sensor failures.

Adaptive fault‐tolerant control of variable pitch system of wind power generator based on clustering‐type fuzzy neural network

In view of the faults of the doubly-fed wind power generator system, the health situations of the system are described by the health factor functions. On this basis, the model reference adaptive fault-tolerant control method based on clustering-type fuzzy neural network is proposed. The proposed method can solve the adaptive control issues of wind power generator system with actuator faults, external interference and model uncertainties. The stability and the tracking performance of the system are guaranteed by using Lyapunov stability theorem. Finally, the effectiveness of the proposed method is proved by the fault-tolerant control of the pitch system of the doubly-fed wind power generator.