Fault-tolerant Control Research Articles

Event-driven intelligent fault-tolerant containment control for nonlinear multiagent systems with unknown disturbances

This paper investigates the event-driven intelligent fault-tolerant containment control problem for nonlinear multiagent systems affected by unknown disturbances, and conducts a comprehensive analysis of the dynamic stability of the system. Reduced-order modelling is employed for system modelling and identification, while radial basis function neural networks are utilised to estimate the nonlinear functions of the system model. An event-driven control scheme is designed based on the backstepping method and Lyapunov functional approach. In contrast to existing consensus control schemes, the proposed event-driven control schemes are specifically tailored to improve the energy efficiency and endurance of nonlinear multiagent systems operating in complex environments. Under the proposed control law, the output of each follower is converge to the convex hull spanned by the outputs of the leaders. The effectiveness of the proposed method is rigorously validated through numerical simulations. Furthermore, a practical example involving the design of a marine surface vehicle using advanced robotics technology is presented to further substantiate the efficacy of the proposed method.

MPC based fault tolerance control of AC–DC power converters for offshore wind turbines

MPC based fault tolerance control of AC–DC power converters for offshore wind turbines

Dynamic event-triggered neuroadaptive fault-tolerant control of quadrotor UAV with a novel cosine kernel

The fault-tolerant control (FTC) for trajectory tracking of a quadrotor unmanned aerial vehicle (UAV) has attracted researchers. The non-linear model of the UAV, coupled with model uncertainties, external disturbances, and actuator failures, requires the function approximation of the lumped non-linearity for controller design. One of the most efficient ways to approximate non-linearity is using radial basis function neural networks (RBFNNs). To date, RBFNNs have been formulated, trained, and used directly for function approximation, which requires considerable computation to derive the control laws. The proposed control and parameter estimation laws approximate the non-linearity by using RBFNNs indirectly. The proposed laws with virtual parameter estimation do not require the actual formulation of RBFNNs and their weights, thus saving on computational resources. For kernel optimization, the Gaussian kernels with exponential terms in RBFNNs are replaced by cosine kernels with algebraic terms, which shows faster convergence as per simulation results. To save on communication bandwidth, static event-triggering communication mechanisms (SECM) and dynamic event-triggering mechanisms (DECM) have been proposed. As DECM works on dynamically changing variables, it saves more communication bandwidth, as tested in simulation. Lyapunov stability analysis proves that errors are uniformly ultimately bounded (UUB). The performance of the proposed algorithm has been tested through numerical simulations, which show superior performance when compared with similar studies. The proposed algorithm has been validated in a real-time Gazebo simulator.

Finite-time output-feedback fault tolerant adaptive fuzzy control framework for a class of MIMO saturated nonlinear systems

This paper presents a finite-time fault-tolerant fuzzy adaptive controller for uncertain interconnected nonlinear systems in strict-feedback form. The controller addresses input saturation, state-dependent actuator faults, external disturbances, and unavailable states for measurement. To avoid any unexpected alteration due to failures, the proposed control scheme design addresses all types of actuator faults that are bias, drift, and loss of accuracy as additive faults, as well as a multiplicative fault that results in loss of effectiveness with nonaffine state-dependency. Restrictions on control gains and unmatched interconnections are eliminated, and the explosion complexity caused by recursive back-stepping designs is avoided. Fuzzy Systems approximate the unknown ideal control laws along side the acutator faults and disturbances. The stability analysis guarantees that tracking errors converge to a small compact set around the origin in finite time. Simulation results on a quad-rotor UAV and a mathematical system confirm the effectiveness of the proposed approach.

ADP-Based Fault-Tolerant Control for Multiagent Systems With Semi-Markovian Jump Parameters.

This article analyzes and validates an approach of integration of adaptive dynamic programming (ADP) and adaptive fault-tolerant control (FTC) technique to address the consensus control problem for semi-Markovian jump multiagent systems having actuator bias faults. A semi-Markovian process, a more versatile stochastic process, is employed to characterize the parameter variations that arise from the intricacies of the environment. The reliance on accurate knowledge of system dynamics is overcome through the utilization of an actor-critic neural network structure within the ADP algorithm. A data-driven FTC scheme is introduced, which enables online adjustment and automatic compensation of actuator bias faults. It has been demonstrated that the signals generated by the controlled system exhibit uniform boundedness. Additionally, the followers' states can achieve and maintain consensus with that of the leader. Ultimately, the simulation results are given to demonstrate the efficacy of the designed theoretical findings.

Distributed Fault-Tolerant Consensus Control of Vehicle Platoon Systems With DoS Attacks

Distributed Fault-Tolerant Consensus Control of Vehicle Platoon Systems With DoS Attacks

Distributed Two-Channel Dynamic Event-Triggered Adaptive Finite-Time Fault-Tolerant Containment Control for Multi-Leader UAV Formations

This paper addresses the distributed adaptive finite-time containment control problem in multi-leader UAV formations with actuator failures, limited communication, and external disturbances. A two-channel dynamic event-triggered strategy based on adaptive and sliding mode control is proposed as a containment control scheme, which solves the contradiction between the need to include containment error in the continuous term of the sliding mode surface in the adaptive law and the discontinuity of the communication between neighboring subsystems (follower UAVs) by constructing intermediate variables. Unlike the traditional event triggering condition that can only be applied to asymptotically convergent systems, the dynamic triggering condition designed in this paper, based on auxiliary variables, hyperbolic tangent function, and adaptive techniques, realizes finite-time convergence during the adjustment of the system and sets a more reasonable lower limit for the triggering thresholds, thus further reducing the communication frequency and ensuring the finite-time convergence of the system. The stability of the closed-loop system can be proved by the Lyapunov theorem. Zeno behavior can be avoided. The simulation results verify the effectiveness of the developed control algorithm.

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 Output Formation Tracking for Nonlinear Multiagent Systems With Double Semi-Markovian Switching Topologies and Time-Varying Actuator Faults.

This article investigates adaptive output formation tracking control of nonlinear multiagent systems with time-varying actuator faults and unknown nonidentical control directions under double semi-Markovian switching topologies. Considering the dynamic changes of communication connections in uncertain environments, a double semi-Markov process is first introduced into the leader-follower structure to describe the random switching of communication topologies. Then, a novel adaptive distributed fault-tolerant output formation tracking control framework is established using the backstepping and Nussbaum gain technique to address matched/mismatched uncertainties and disturbances, time-varying actuator faults, and unknown nonidentical control directions. In this control framework, the independent variable of the Nussbaum function is designed as a non-negative function that monotonically increases with respect to time, thereby overcoming the presence of the absolute value of its derivative in the integration process. Based on the distributed structure, an adaptive fault-tolerant controller is further proposed to achieve the asymptotic output formation tracking in mean-square sense. The stability of the closed-loop nonlinear multiagent systems is analysed through the contradiction argument and Lyapunov theorem. The simulation example verifies the effectiveness of the proposed control strategy.

Quantized Time-Varying Fault-Tolerant Control for a Three-Dimensional Euler–Bernoulli Beam With Unknown Control Directions

Quantized Time-Varying Fault-Tolerant Control for a Three-Dimensional Euler–Bernoulli Beam With Unknown Control Directions

Coupled error super-twisted sliding mode active fault-tolerant control for robotic system with actuator fault.

This paper focuses on a robotic system with actuator fault and presents a sliding mode active fault-tolerant control method based on coupled position error. The actuator fault is first detected by a fault alerter with a predetermined threshold. After the fault is successfully detected, the fault is estimated by a fault observer. Coupling the position error with the weighted position error of different joints, a non-singular fast terminal sliding mode surface is constructed, and a super-twisted algorithm is introduced to design a coupled error super-twisted sliding mode controller (CESSMC). Furthermore, the fault estimation is incorporated with the CESSMC to accomplish the coupled error super-twisted sliding mode active fault-tolerant control. The stability and the finite-time convergence of the system are theoretically proved. Simulations and experiments are conducted to verify the effectiveness of the proposed method. The proposed method can achieve the position error convergence in finite time and reduce chattering in the control input. With the tight coupling between position error and weighted position error, the fault compensation effect and trajectory tracking accuracy can be improved.

Prescribed-Time Fault-Tolerant Control of the FO Decoupled Dual-Mass MEMS Gyro With Deferred Constraints-Design and Implementation

Prescribed-Time Fault-Tolerant Control of the FO Decoupled Dual-Mass MEMS Gyro With Deferred Constraints-Design and Implementation

Neural Network-Based Adaptive Fault-Tolerant Control of Dissolved Oxygen and Nitrate Concentrations in WWTPs

Neural Network-Based Adaptive Fault-Tolerant Control of Dissolved Oxygen and Nitrate Concentrations in WWTPs

AI-enhanced fault-tolerant control and security in transportation and logistics systems: addressing physical and cyber threats

Transportation and logistics systems are becoming increasingly complex and critical to modern infrastructure. This paper proposes a novel AI-enhanced fault-tolerant control framework to address the dual challenges of physical malfunctions and cyber threats. By leveraging advanced machine learning algorithms and real-time data analytics, the proposed methodology aims to enhance the reliability, safety, and security of transportation and logistics systems. This research explores the foundations and practical implementations of AI-driven anomaly detection, predictive maintenance, and autonomous response systems. The findings demonstrate significant improvements in system resilience and robustness, making a substantial contribution to the field of intelligent transportation management.

Distributed adaptive event-triggered finite-time fault-tolerant containment control for multi-UAVs with input constraints and actuator failures

This article investigates the distributed adaptive finite-time containment control problem for multi-UAVs with input constraints, actuator failures, communication limitations, and external disturbances. First, a new smoothing function is used to smooth the asymmetric input constraint signals so that the input constraint and actuator fault control problem can be transformed into a variable gain control problem. Subsequently, a new Nussbaum function is proposed to solve the variable gain control problem. A new adaptive event-triggered strategy is designed to solve the communication limitation problem, and the trigger threshold has the characteristic of adaptive adjustment that can be dynamically decreased. In response to external disturbances, an adaptive law is designed to estimate and compensate for the boundaries of disturbances. It follows from the analysis based on Lyapunov theory that under the proposed controller, the followers will converge to the convex envelope formed by the leaders in a finite time, and Zeno-free is achieved. Simulation results are provided to verify the effectiveness of the developed adaptive event-triggered finite-time fault-tolerant containment control laws.

Event-triggered adaptive fault-tolerant control with pre-specified performance for AUVs trajectory tracking

This paper investigates the problem of fault-tolerant control of the preset performance of autonomous underwater vehicles (AUVs) in case of external disturbances and actuator failures. To begin with, a specific new nonlinear auxiliary function with dual performance constraints is designed to establish the foundation for achieving prescribed performance in the closed-loop system. Subsequently, an event-triggered fault tolerant controller employing Nussbaum gain technique is introduced to accomplish the precision trajectory tracking control of the AUVs by predetermining control index. It is shown that the effect by resulting in actuator faults is on-line compensated by an adaptive estimator. Theoretical analyses demonstrate the boundedness of the signals of the designed closed-loop control system, and the Zeno phenomenon can be effectively avoided. Finally, simulation experiments are conducted to verify the effectiveness of the proposed methods.

Event-triggered adaptive fault estimation and fault-tolerant control for strict-feedback nonlinear systems with full state constraints

Event-triggered adaptive fault estimation and fault-tolerant control for strict-feedback nonlinear systems with full state constraints

Two-dimensional reinforcement learning model-free fault-tolerant control for batch processes against multi- faults

Aiming at the characteristics of batch process changing along with time and batch directions, the existence of unmodeled dynamics, and the partial failure of actuators or/and sensors, we propose a novel 2D reinforcement learning (RL) fault tolerant control strategy without considering model parameters. Firstly, a two-Dimensional (2D) augmented state space model and 2D Q function-based fault tolerant control (FTC) framework is established. The 2D Bellman equation can be acquired by analyzing the relationship between the 2D value function and the 2D Q function. Based on the extended model and Q-learning concept of RL, a data-driven FTTC independent of model parameters is designed, and a 2D data-driven Q-learning algorithm is proposed. Finally, taking the pressure holding phase in the injection process as the experimental object, the control effect is compared with that of the traditional model-based FTC, and better tracking performance and unbiasedness to the probing noise can be proved.

Meta-learning-based fault-tolerant attitude control of hypersonic flight vehicle with input constraints

Meta-learning-based fault-tolerant attitude control of hypersonic flight vehicle with input constraints

Recursive terminal sliding mode approximate optimal control strategy based on reconstructed nonlinear systems

For a class of nonlinear systems with unknown internal states and actuator failures, this paper proposes a recursive terminal sliding-mode fault-tolerant control method based on reconfigurable nonlinear systems and further designs an approximate optimal compensated controller based on adaptive dynamic programming, which employs a neural network (NN) to estimate the optimal value function. The system ensures that the tracking error converges to a small set around zero with faster speed and higher accuracy even when the actuator fails. Finally, the convergence of the expanded state observer and the single-evaluation NN weights is demonstrated based on the Lyapunov theory, and the stability of the whole closed-loop system is given. Simulation results and comparisons verify the effectiveness of the proposed control strategy.