Actuator Faults Research Articles

Encryption–decryption-based distributed fault-tolerant consensus tracking control for multi-agent systems

This study investigates fault-tolerant consensus tracking for discrete-time multi-agent systems (MASs) subject to external eavesdropping threats and additive actuator faults. First, actuator faults are modeled by difference equations, and decentralized observers are constructed to estimate actuator faults as well as system states. To offset fault-induced effects, ensure secure communication, and alleviate communication congestion, neighboring encrypted state information based on the encryption–decryption strategy (EDS) and estimated fault are integrated into a distributed active fault-tolerant consensus tracking control (FCTC) protocol. Through the properties of compatible norms, criteria for the controller, observer, and dynamic encryption key in EDS are derived to achieve leader-following consensus (LFC) of MASs with bias and drift actuator faults. Simulation results confirm the validity of the encryption–decryption-based distributed FCTC strategy.

Neuroadaptive Cooperative Fault-Tolerant Control of Heterogeneous Multiagent Systems Based on Fully Actuated System Approaches.

The leader-following cooperative problem in heterogeneous multiagent systems (HMASs) with unmodeled dynamics and actuator faults is investigated in this article. The HMASs, which include unmanned ground vehicles and unmanned aerial vehicles, are first described using a fully actuated system model (FASM). The FASM, as opposed to the first-order state-space model, preserves the physical significance of original systems and makes it feasible to apply the control rule entirely. In order to approximate unknown system dynamics, novel neuroadaptive laws with few learning parameters are then suggested. To counteract the negative effects of actuator faults, the Nussbaum function and adaptive approach are utilized. In addition, a cooperative fault-tolerant protocol is suggested, wherein consensus errors are uniformly ultimately bounded. The lack of virtual control variables in the proposed protocol reduces its complexity. The theoretical results are then validated by numerical simulations.

Decentralized Control for Large-Scale Systems With Actuator Faults and External Disturbances: A Data-Driven Method.

This article investigates optimal control for a class of large-scale systems using a data-driven method. The existing control methods for large-scale systems in this context separately consider disturbances, actuator faults, and uncertainties. In this article, we build on such methods by proposing an architecture that accommodates simultaneous consideration of all of these effects, and an optimization index is designed for the control problem. This diversifies the class of large-scale systems amenable to optimal control. We first establish a min-max optimization index based on the zero-sum differential game theory. Then, by integrating all the Nash equilibrium solutions of the isolated subsystems, the decentralized zero-sum differential game strategy is obtained to stabilize the large-scale system. Meanwhile, by designing adaptive parameters, the impact of actuator failure on the system performance is eliminated. Afterward, an adaptive dynamic programming (ADP) method is utilized to learn the solution of the Hamilton-Jacobi-Isaac (HJI) equation, which does not need the prior knowledge of system dynamics. A rigorous stability analysis shows that the proposed controller asymptotically stabilizes the large-scale system. Finally, a multipower system example is adopted to illustrate the effectiveness of the proposed protocols.

Active Adaptive Composite Fault Tolerant Controller Design For Nonlinear Systems

Abstract It can be seen from the public opinion monitoring that there are many cases of death and injury caused by quality problems of mechanical products, which poses a serious threat to people’s lives. Therefore, it is particularly important to analyze the reliability of mechanical products. This study focuses on active adaptive composite fault tolerant controller (ACFTC) design for nonlinear system with actuator fault. Firstly, a novel iterative learning observer (ILO) is proposed to estimate the early potential fault. And then, the ACFTC approach is proposed in order to obtain desired performance in the faulty case based on the fault estimation information from ILO. Finally, simulation of the flexible joint robot system verifies the validity and applicability of the algorithm.

Prescribed-time active fault-tolerant control of dynamic positioning vessels based on improved iterative learning observer

Considering the dynamic positioning (DP) vessel control system with actuator faults and unknown environment disturbances, a fault-tolerant control scheme based on improved iterative learning observer (ILO) is proposed in this article. Firstly, the kinematics and dynamics models of DP vessels are established containing the actuator faults and environmental disturbances. Secondly, an improved ILO is designed to handle the unknown faults and disturbances concurrently with higher estimation precision by introducing the state information of the previous moment. Further, a prescribed-time active fault-tolerant control scheme is presented on the basis of the improved ILO and prescribed-time stability. And the stability of proposed ILO and DP fault-tolerant control system are analyzed based on Lyapunov theory. Finally, the effectiveness and advantages of the presented ILO and active fault-tolerant control scheme are illustrated by numerical simulations and comparisons.

Adaptive fixed‐time fault‐tolerant fuzzy control of AUVs with asymmetric output constraints

AbstractThe fast and safe formation tracking problem for multiple autonomous underwater vehicle (AUV) systems (MAUVS) with asymmetric output constraints and actuator faults is studied in this article. Actuator faults are composed of loss of effectiveness and time‐varying unknown bias faults, an adaptive fixed‐time fault‐tolerant controller (AFFTC) is designed by employing fixed‐time stable theory and fuzzy logic systems. Under the designed control algorithm, the MAUVS can be practically fixed‐time stable, the tracking errors among the follower AUVs and the virtual leader AUV can converge to a small area near the origin within the fixed time, and the settling time is unaffected by the initial state of the system. To enhance the safety of MAUVS, a novel asymmetric barrier function is utilized to constrain the trajectory tracking errors within the prescribed range. Finally, the simulation results demonstrate the effectiveness of the proposed control algorithm.

Modelling and Diagnosis of Hybrid Dynamic Systems by a Multi-Model Approach

Fault diagnosis is an essential task in ensuring the smooth operation of complex dynamic systems. The consequences of faults can be serious, leading to loss of life, harmful emissions to the environment, high repair costs and economic losses caused by unplanned production line stoppages. The work developed in this paper concerns the modeling and diagnosis of faults (sensor faults, system faults, actuator faults) in hybrid dynamic systems using our multi-model approach (which combines two sub-models, one continuous and the other discrete). The aim is to integrate three well-known tools in the literature: the Bond Graph, the Observer and the Timed Automata, to design a global diagnostic model. The hybrid dynamic system is modeled by connecting the tools for the continuous part, i.e. the bond graph and the observer, to the timed automata for the discrete part. The resulting model is used for fault diagnosis in two stages: The first is fault detection by analyzing the residuals generated by the system output and that of the observer. The second step involves fault localization, which results from analysis of the signature matrix and temporal identification of the system. The proposed method combines the advantages of these tools to obtain the best performance, particularly in the fault location phase. The simulation results prove the effectiveness of the proposed model for the hybrid dynamic system. Moreover, these results also evaluate the performance of the proposed diagnostic approach while reducing non-detections, detection delays and false alarms.

Inverse compensation mechanism-based adaptive fuzzy-neural fault-tolerant control for an uncertain quadrotor UAV

Actuator faults, system uncertainties, and external disturbances can affect tracking control performance of the quadrotor UAV, and even cause the instability of the system. To address this issue, an adaptive fuzzy-neural fault-tolerant control (AFNFTC) scheme is proposed for the quadrotor UAV integrated with inverse compensation strategy and fuzzy neural networks (FNNs) technology. Firstly, we design an inverse compensation mechanism to address actuator faults and utilize the approximation capabilities of the FNNs to compensate for system uncertainties. Then, based on the Lyapunov direct method, the AFNFTC scheme is designed to ensure the stability of the quadrotor UAV. Under the proposed scheme, the closed-loop system for the quadrotor UAV is proven to be uniformly bounded, and the tracking error of the quadrotor UAV is demonstrated to converge to a small compact set ultimately by appropriately selecting design parameters. Finally, the feasibility and effectiveness of the proposed scheme is validated through simulation results.

A unified sensor and actuator fault diagnosis in digital twins for remote operations

This paper explores the development of a unified hybrid approach for sensor and actuator fault diagnosis in digital twins for remote operations. Central to this approach is the implementation of a robust adaptive Kalman filter algorithm, which forms the backbone of the proposed unified algorithm. The essence of this unified algorithm lies in its capability to effectively filter the sensor measurements. The algorithm is enriched with tuning parameters, offering flexibility in adjusting the convergence rate to suit operational requirements. Noteworthy for its robustness, our approach excels in handling uncertainties and diverse types of faults, including drift, bias, noise, and freeze fault scenarios. Through comprehensive simulation and experimental evaluations conducted on a small surface vessel, the method demonstrates remarkable proficiency in accurately identifying sensor and actuator faults. This precision enables early detection and prompt mitigation of anomalies, contributing to heightened operational resilience.

Modeling and vibration suppression for overhead crane in planar space with nonlinear time-varying actuator faults and uncertain control directions

Modeling and vibration suppression for overhead crane in planar space with nonlinear time-varying actuator faults and uncertain control directions

Asynchronous output feedback control for fuzzy Markov jump systems with actuator faults and deception attacks

Asynchronous output feedback control for fuzzy Markov jump systems with actuator faults and deception attacks

Intermediate parameter based distributed sensor fault-tolerant estimation for a class of nonlinear systems

This paper investigates the estimation problem of a class of nonlinear systems with actuator and sensor faults. The primary objective is to design a distributed fault-tolerant observer which can estimate system states and actuator faults. Firstly, a distributed observer network with intermediate parameters is constructed to compensate the missing information of unobservable nodes of the system. Next, a class of redundant sensors is set up for each distributed observer node to obtain more output measurement samples. More importantly, when some of the redundant sensors occur faults, all sensor signals will be further processed and classified by a new algorithm. An index of sensor health level is constructed to characterize the quality of the fault-free or faulty sensor signals. By using the proposed algorithm, unhealthy sensor signals will be automatically filtered out, while healthy ones will be retained. Based on the healthy sensor signals, the system states and actuator faults are estimated. Finally, an example demonstrates that the proposed method is effective.

Neural adaptive performance guaranteed formation control for USVs with event-triggered quantized inputs

ABSTRACT This study presents an adaptive fault-tolerant performance guaranteed formation control strategy with event-triggered quantised inputs (EDQI) for unmanned surface vessels (USVs) in the presence of model uncertainties and actuator faults. Firstly, a prescribed performance formation guidance control law is proposed to allow formation tracking errors to evolve within the performance envelope and convergence to prescribed steady regions at an appointed time. Then, minimal-parameter-learning-based neural networks (MLP-NN) are used to tackle model uncertainties. To reduce the frequency of actuator updates and the communication burden on the channel, a dynamic memory event-triggered quantised input strategy is investigated. In addition, adaptive estimators and auxiliary design signals are built to compensate for the effects caused by input quantisation and actuator faults. It is proved that all closed-loop signals are bounded and formation errors can converge to the prescribed region at an appointed time. Finally, the effectiveness of the proposed controllers is verified by numerical simulations.

Adaptive fuzzy quantized prescribed performance synchronization of uncertain non-strict feedback chaotic systems with time-varying actuator failure

This paper addresses an adaptive fuzzy prescribed performance synchronization for a class of uncertain non-strict feedback chaotic systems subject to input quantization and time-varying actuator faults. The proposed approach utilizes fuzzy logic systems to estimate uncertainties. In addition, multiplicative and additive faults are considered simultaneously, which may occur either separately or simultaneously. To address the challenge, based on the backstepping scheme and a filter, an intermediate controller is conducted to compensate for the interference between actuator faults and quantification, in which a damping term and a positive time-varying function are introduced. Moreover, treating the error system as a constrained system, a simplified nonlinear mapping is employed to achieve asymmetric prescribed performance synchronization. Unlike traditional methods that rely on barrier Lyapunov functions and switch functions, the proposed approach eliminates the need for a switch function and extends the action scope to cover all synchronization errors. The analysis demonstrates that even if actuator faults and input quantization coexist, all signals in the closed-loop system remain bounded, and synchronization errors remain within the prescribed performance range. Finally, synchronization simulations are conducted on a nonlinear gyroscope system and a Non-autonomous chaotic Micro-Electro-Mechanical-System to reveal the validity of the proposed scheme.

Self-modified flexible prescribed performance control for a class of strict feedback systems with input saturation and actuator faults

Self-modified flexible prescribed performance control for a class of strict feedback systems with input saturation and actuator faults

Finite‐time consensus for variable‐order fractional non‐linear multi‐agent systems under actuator faults and external disturbances

AbstractThe present investigation aims to address the leader‐following consensus issue for variable‐order fractional multi‐agent systems (VOFMASs) under actuator faults and unknown external disturbances. The Caputo definition for the variable‐order fractional (VOF) derivative is used to model the non‐linear dynamics of the leader and follower agents. Consequently, two lemmas are developed for the Caputo VOF derivative of the Lyapunov function. In the first case, it is assumed that the multi‐agent system (MAS) operates without actuator faults and an adaptive controller is proposed. With the aid of the developed lemmas, assurance is provided for the finite‐time bounded cooperative tracking of the VOFMAS despite the presence of unknown external disturbances. In the second case, a novel fault‐tolerant controller is designed for the finite‐time consensus of the MAS under two common kinds of actuator faults: loss of effectiveness fault and bias fault. Finally, the efficacy of the proposed controller is demonstrated through the presentation of results from three simulation examples.

Safe tracking control for unmanned autonomous helicopter under actuator faults and outside disturbances

This paper considers the problem on tracking control for the medium-scale unmanned autonomous helicopter (UAH) system under external disturbances, actuator faults, and full state constraints. In comparison with existing results, this paper does not only consider asymmetric state constraints but also jointly take the faults and disturbances into account on the UAH system, estimating and compensating for them separately, which can effectively enhance the tracking performance. First, the dynamics of nonlinear UAH system is divided into the position subsystem and the attitude one. Second, an improved barrier Lyapunov function (BLF) is constructed to handle the problem of asymmetric state constraints. Third, in order to estimate the external disturbances and actuator faults, two coupled generalized proportional integral observers (GPIOs) and two fault estimators are designed, respectively. Fourth, based on above estimations, improved BLFs, and backstepping method, two tracking control laws for the position and attitude loops are presented and an overall closed-loop system is established. Then, a sufficient condition ensuring uniform boundedness of tracking and estimation errors is derived. Finally, some simulating results demonstrate the effectiveness and advantage of the proposed control scheme.

Adaptive fuzzy ABLF function fixed‐time tracking control method for nonlinear fault‐tolerant control systems with event triggering mechanism

SummaryAs the nonlinearities of many real physical controlled systems become stronger and stronger, they are difficult to be described by accurate mathematical models. For a class of nonlinear single‐input single‐output systems with all‐state constraints and actuator faults, an event‐triggered adaptive fuzzy ABLF function fixed‐time tracking control method is proposed. The asymmetric barrier Lyapunov function (ABLF) combined with backward step technique is used to ensure the boundedness of the closed‐loop system output. In order to solve the problem of limited bandwidth resources, this paper adopts the event trigger mechanism and fuzzy control technology to approximate the unknown function to ensure the convergence of the system in a fixed time. After theoretical analysis, it is proved that the tracking error of the system converges in the small neighborhood of the origin, and it still has good tracking performance when the actuator faults. Finally, by comparing the proposed method with the asymmetric barrier Lyapunov function, which verifies the effectiveness of the proposed method.

Observer‐based optimal fault‐tolerant tracking control for input‐constrained interconnected nonlinear systems with mismatched disturbances

SummaryThis paper investigates an observer‐based optimal fault‐tolerant tracking control problem for interconnected nonlinear systems with input constraints and mismatched disturbances via adaptive dynamic programming (ADP). Firstly, an augmented system consisting of tracking error and the reference trajectory is constructed, and then the original optimal tracking control problem is transformed into the optimal regulation control problem of the augmented system. An integral sliding‐mode‐based optimal fault‐tolerant control scheme is developed to eliminate the effect of actuator faults and guarantee the optimal control performance. Furthermore, a neural network‐based observer is designed to identify the completely unknown system dynamics based on the input‐output data, thereby relaxing the restriction on system dynamics. Subsequently, the modified Hamilton‐Jacobi‐Bellman equations are solved by using the ADP algorithm under a critic network framework. According to the Lyapunov approach, all signals in the closed‐loop augmented system are uniformly ultimately bounded. Finally, the effectiveness of the developed control scheme is demonstrated via simulation results.

Model predictive path tracking control of intelligent vehicles based on dual-stage disturbance observer under multi-channel disturbances

Abstract Parameter fluctuations, unmodeled dynamics, speed variation, steering actuator faults, and other multi-channel uncertain disturbances are the key challenges faced by the path tracking control of intelligent vehicles, which will affect the accuracy and stability of the path tracking. Therefore, a model predictive control (MPC) method based on a dual-stage disturbance observer (DDOB) is proposed in this paper. First, a tracking error dynamics model considering multi-channel uncertain disturbances is constructed, based on which a model predictive controller is designed to obtain the nominal front wheel steering angle by the Karush–Kuhn–Tucker (KKT) condition. Furthermore, the DDOB is designed to enable real-time estimation of the system disturbances, and then the estimated disturbances are used as the compensation for the nominal front wheel steering angle, which establishes the MPC control law with parallel compensation of the DDOB. Finally, the error boundedness of the DDOB and the global stability of the model predictive controller are analyzed. The effectiveness and superiority of the proposed algorithm are verified through Carsim–Simulink simulation and hardware-in-the-loop experiments.