Actuator Faults Research Articles

Distributed adaptive finite-time fault-tolerant cooperative control of heterogeneous multi-agent systems with saturation and disturbances

In this paper, the issue of distributed adaptive finite-time fault-tolerant cooperative control (FT-FTCC) problem is investigated for multiple unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) with unknown parameter uncertainties, actuator faults, input saturation and external disturbances. Starting from the dynamic models of the UAVs and UGVs, an unified control model is presented. Then, a sliding-mode estimator is presented to estimate the position of the leader for the followers which only uses the information from neighbours. Next, a distributed adaptive FT-FTCC scheme, which can also deal with the uncertainties, actuator faults, input saturation and disturbances, is proposed by utilising disturbance observers and neural networks. Based on Lyapunov function approach, the tracking errors of all followers subject to the pre-defined desired positions are uniformly ultimately bounded. Finally, simulations are given to validate the efficiency of the developed FT-FTCC scheme.

Fault-Tolerant Control for Quadcopters Under Actuator and Sensor Faults

Fault detection and diagnosis (FDD) methods and fault-tolerant control (FTC) have been the focus of intensive research across various fields to ensure safe operation, reduce costs, and optimize maintenance tasks. Unmanned aerial vehicles (UAVs), particularly quadcopters or quadrotors, are often prone to faults in sensors and actuators due to their complex dynamics and exposure to various external uncertainties. In this context, this work implements different FDD approaches based on the Kalman filter (KF) for fault estimation to achieve FTC of the quadcopter, considering different faults with nonlinear behaviors and the possibility of simultaneous occurrences in actuators and sensors. Three KF approaches are considered in the analysis: linear KF, extended KF (EKF), and unscented KF (UKF), along with three-stage and adaptive variations of the KF. FDD methods, especially the adaptive filter, could enhance fault estimation performance in the scenarios considered. This led to a significant improvement in the safety and reliability of the quadcopter through the FTC architecture, as the system, which previously became unstable in the presence of faults, could maintain stable operation when subjected to uncertainties.

Error governor for active fault tolerance in PID control of MIMO systems

Input saturation and actuator faults are common issues in control system engineering. This paper proposes an Error Governor (EG) policy that dynamically manages the feedback error to enhance the tracking error in Multiple Input-Multiple Output (MIMO) closed-loop system controlled by Proportional-Integral-Derivative (PID) regulators. By integrating an Adaptive Kalman Filter (AKF) for optimal fault estimation with the EG scheme, the solution enables fault-tolerant control without requiring modifications to the baseline controller, which can be impractical or unsuitable in certain applications. Furthermore, the proposed control scheme completely avoids windup, thereby replacing conventional Anti-Windup (AW) schemes for PIDs, and is computationally cheap and easy to implement, needing only the same inputs as conventional AW algorithms and the actuator fault estimation. Simulation results on the MIMO model of the Zagi flying-wing aerial vehicle show that the EG reduces the tracking error in presence of actuator faults by − 49.92 % in terms of Integral Absolute Error (IAE), outperforming conventional AW methods.

Real-time Actuator Fault Detection and Isolation for Quadrotor UAV via Directional Residuals

Abstract Fault diagnosis is critical for the safe and reliable operation of quadrotor unmanned aerial vehicles (UAVs), so that timely remedial measures can be taken to reduce its negative impact. This paper presents a real-time actuator fault detection and isolation (FDI) method for quadrotor UAV flight process. First, a linear parameter-varying (LPV) model is established to describe quadrotor UAV dynamics, which considers measurement noise and external disturbance. Then, an FDI strategy is developed based on directional residuals, in which only one observer-based residual generator is required and Η_/L∞ indices are incorporated to balance fault sensitivity and disturbance robustness. A threshold is derived from the L∞ performance condition, and actuator fault is detected through residual evaluation. Furthermore, by introducing fault feature vectors associated with the system model, the faulty actuator is isolated promptly by analyzing directional correlations between generated residuals and the defined fault feature vectors. Finally, based on a quadrotor UAV platform, the effectiveness and superiority of the proposed FDI method are verified through online trajectory tracking processes.

Intelligent sliding mode fault-tolerant control for aircraft engines with actuator dynamics and faults based on adaptive dynamic programming

Abstract The fault-tolerant control issue of aircraft engines with actuator dynamics and faults is investigated in this paper. By proposing a novel intelligent sliding mode fault-tolerant control (ISMFTC) method, which combines an adaptive dynamic programming (ADP) sliding surface with Grey Wolf Optimizer (GWO) for controller parameter optimisation, the goal is to achieve quality steady-state and dynamic performance in aircraft engines while maintaining strong fault-tolerance properties. Firstly, by considering not only actuator dynamics but also actuator faults, an uncertain nonlinear cascaded model of aircraft engines is developed according to characteristic of aircraft engines and their actuators. Secondly, an ADP-based sliding surface is proposed for considered aircraft engine uncertain nonlinear cascaded system. It can obtain a certain sense of optimised performance, and could be solved by ADP strategy off-line as well. Thirdly, fault-tolerant controller is obtained on the basis of sliding mode theory and adaptive fault estimation law, namely, ADP-based ISMFTC controller. Meanwhile, GWO is integrated into the investigation of ADP-based ISMFTC controller, optimised designable control parameters are obtained subsequently. Besides, robustness analysis is elaborated according to Lyapunov theory, fault estimation error is bounded and states of closed-loop system are uniformly ultimately bounded. Simulation on a twin-shaft turbofan aircraft engine, indicates the effect of proposed ADP-based ISMFTC method.

Adaptive Integral Sliding Mode Control with Chattering Elimination Considering the Actuator Faults and External Disturbances for Trajectory Tracking of 4Y Octocopter Aircraft

This paper presents a control strategy for a 4Y octocopter aircraft that is influenced by multiple actuator faults and external disturbances. The approach relies on a disturbance observer, adaptive type-2 fuzzy sliding mode control scheme, and type-1 fuzzy inference system. The proposed control approach is distinct from other tactics for controlling unmanned aerial vehicles because it can simultaneously compensate for actuator faults and external disturbances. The suggested control technique incorporates adaptive control parameters in both continuous and discontinuous control components. This enables the production of appropriate control signals to manage actuator faults and parametric uncertainties without relying only on the robust discontinuous control approach of sliding mode control. Additionally, a type-1 fuzzy logic system is used to build a fuzzy hitting control law to eliminate the occurrence of chattering phenomena on the integral sliding mode control. In addition, in order to keep the discontinuous control gain in sliding mode control at a small value, a nonlinear disturbance observer is constructed and integrated to mitigate the influence of external disturbances. Moreover, stability analysis of the proposed control method using Lyapunov theory showcases its potential to uphold system tracking performance and minimize tracking errors under specified conditions. The simulation results demonstrate that the proposed control strategy can significantly reduce the chattering effect and provide accurate trajectory tracking in the presence of actuator faults. Furthermore, the efficacy of the recommended control strategy is shown by comparative simulation results of 4Y octocopter under different failing and uncertain settings.

Nonfragile Prescribed Performance Control of Robot Manipulators With Actuator Faults

Nonfragile Prescribed Performance Control of Robot Manipulators With Actuator Faults

Adaptive quantized finite-time fault-tolerant control for uncertain multi-input multi-output systems and its application

The article proposes a novel state-feedback control method for a multiple-input multiple-output (MIMO) nonlinear system with actuator faults and input quantization. The innovation of the design approach lies in the utilization of fuzzy logic systems (FLSs) to approximate the uncertain intermediate virtual control laws, thereby achieving a simplified virtual control design form. Additionally, finite-time control is employed to enhance the system’s response speed. Different from the existing literatures, the adaptive control scheme of partial loss fault gain is integrated with input quantization, which completes the unknown gain estimation and avoids the assumption condition of unknown control gain. The theoretical analysis combined with Lyapunov stability analysis shows that the tracking error can converge regardless of whether the system experiences a fault, while the closed-loop signal remains stably bounded for a finite time. Finally, the simulation results of the quadrotor unmanned aerial vehicle (UAV) attitude system indicate that this control scheme is effective.

Output Feedback Consensus Control of Nonlinear Multi-agent System Under Communication Faults and Actuator Faults

Output Feedback Consensus Control of Nonlinear Multi-agent System Under Communication Faults and Actuator Faults

Off-policy reinforcement-learning-based fault-tolerant [formula omitted] control for topside separation systems with time-varying uncertainties

The topside separation system plays a pivotal role in the treatment of produced water within offshore oil and gas production operations. Due to high-humidity and salt-infested marine environments, topside separation systems are susceptible to dynamic model variations and valve faults. In this work, fault-tolerant control (FTC) of topside separation systems subject to structural uncertainties and slugging disturbances is studied. The system is configured as a cascade structure, comprising a water level control subsystem and a pressure-drop-ratio (PDR) control subsystem. A fault-tolerant H∞ control framework is developed to cope with actuator faults and slugging disturbances. To enhance control performance in the presence of actuator faults and model uncertainties while reducing sensitivity to slugging disturbances, the fault-tolerant H∞ control problem for the topside separation system is established as the two-player differential game problem. In addition, a Nash equilibrium solution for the fault-tolerant H∞ control problem is achieved through the solution of the game algebraic Riccati equation (GARE). A model-free approach is presented to implement the proposed fault-tolerant H∞ control method using off-policy reinforcement learning (RL). Simulation studies demonstrate the effectiveness of the solution.

Adaptive fault-tolerant containment control for heterogeneous uncertain nonlinear multi-agent systems under actuator faults

As modern control systems involve a growing number of actuators, the consequent number of faults occurrence, which may deteriorate the closed-loop system performance, is increasing. Focusing on the containment control problem of generic nonlinear high-order heterogeneous uncertain Multi-Agent Systems, this study proposes a novel distributed adaptive fault-tolerant Proportional-Integral-Derivative control protocol to improve the reliability and the resilience of the whole networked systems against actuator failures. The Lyapunov theory, combined with the Barbalat lemma, provides the design of suitable adaptive mechanisms that adjust the PID control actions to ensure the asymptotic convergence of the closed-loop containment errors for the overall network, hence implying the convergence of each agent state toward the convex region shaped by the multiple leaders, despite the occurrence of actuators faults. The fulfillment of the fault-tolerant containment objective is ensured by requiring a lower computational burden with respect to alternative solutions, hence resulting in more attractive for wider practical engineering applications. Numerical simulation results, also including a comparison analysis with a state-of-the-art controller, corroborate the efficiency and the advantages of the proposed distributed PID controller.

Event-triggered explorized IRL-based decentralized fault-tolerant guaranteed cost control for interconnected systems against actuator failures

This paper presents a novel data-based decentralized guaranteed cost (DGC) fault tolerant control (FTC) scheme for the large-scale systems subject to actuator faults and mismatched interconnection. First, the FTC issues of interconnected systems are converted into a series of near optimal event-triggered control (ETC) methods for isolated subsystems via constructing a modified performance index function of each subsystem. By means of adaptive dynamic programming (ADP) algorithm, the upper bound of performance index function of large-scale systems can be obtained by solving the Hamilton–Jacobi-Bellman (HJB) equation of each auxiliary subsystem. Second, according to the proposed ADP-based decentralized approach and utilizing the event-based synchronous integral reinforcement learning (IRL) algorithm, a model-free guaranteed cost (GC) FTC approach is developed for interconnected large-scale system which can relax the restriction on the condition that system functions must be known. Further, the ultimate uniformly bounded (UUB) stability of auxiliary subsystems can be proved according to the Lyapunov principle. Finally, the effectiveness of the proposed control method is verified by presenting the simulation results.

H∞ adaptive ETF control for degenerate jump systems under actuator faults and impulsive deception attacks with application to DCMIP device

This article considers the issue of H∞ adaptive event-triggered feedback control for degenerate jump systems under actuator faults and impulsive deception attacks. Based on periodic sampling strategy, a mode-dependent adaptive event-triggered scheme is developed, which can save communication resources, avoid the Zeno phenomenon and take full advantage of Markovian mode information. By constructing the neoteric Lyapunov-Krasovskii (L-K) functional containing piecewise linear impulsive auxiliary functions and applying singular value decomposition (SVD) technique, the fresh stochastic admissibility conditions for degenerate Markovian jump systems under actuator faults and impulsive deception attacks are obtained under the framework of linear matrix inequalities (LMIs), and then the adaptive event-triggered scheme with weighted matrices and feedback controller are designed simultaneously. By applying freedom weight matrix method and the property that the network-induced delay derivative is one, the non-strict LMI conditions are transformed into strict ones, such that the stochastic admissibility of the degenerate jump systems and state feedback controller gains are realized adequately. Finally, a direct current motor-controlled inverted pendulum (DCMIP) device is used to confirm the feasibility of the presented approach.

Comprehensive Analysis and Reconstruction of Sensor Faults in Interleaved Buck Converters Using Sliding Mode Observers

This paper presents a fault signal reconstruction method for current sensors in an interleaved buck DC–DC converter, utilizing a sliding mode observer (SMO). A filter bank is used to design the observer within an extended-order system, effectively treating sensor faults as actuator faults, which enables precise estimation of the fault signal. Thus, the proposed approach allows for the identification of the faulty sensor and supports the implementation of fault-tolerant strategies. The paper provides an in-depth analysis of current sensor faults, verifies their impact on current balancing control, and demonstrates the challenge of achieving error-free current estimation in one phase using observers. A comprehensive set of simulation results is carried out, validating the method’s effectiveness and showing a strong correlation with theoretical principles.

Sliding Mode Fault-Tolerant Control for Nonlinear LPV Systems with Variable Time-Delay

This paper presents a robust sliding mode fault-tolerant control (FTC) strategy for a class of linear parameter variant (LPV) systems with variable time-delays and uncertainties. First fault estimation (FE) is conducted using a robust sliding mode observer, synthesized to simultaneously estimate the states and actuator faults of LPV polytopic delayed systems. Second, a sliding mode FTC is developed, ensuring all states of the closed-loop system converge to the origin. This paper presents an integrated sliding mode FTC strategy to achieve optimal robustness between the observer and controller models. The integrated design approach offers several advantages over traditional separated FTC methods. Our novel approach is based on incorporating adaptive law into the design of the Lyapunov–Krasovskii functional to improve both robustness and performance. This is achieved by combining the concept of sliding mode control (SMC) with the Lyapunov–Krasovskii function under the H∞ criteria, which plays a key role in guaranteeing the stability of this class of system. The effectiveness of the proposed method is demonstrated through a diesel engine example, which highlights the validity and benefits of the integrated and separated FTC strategy for uncertain nonlinear systems with time delays and the sliding mode control.

Improved FPTPPF-based predefined-time tracking control of a UVMS with actuator faults

In this paper, a new predefined-time control scheme is proposed for the trajectory tracking problem of an underwater vehicle-mechanic system (UVMS) with external disturbances, model uncertainties and actuator faults. First, a predefined-time controller is proposed to achieve convergence in a predefined time so that the convergence time does not depend on the initial value of the system. Second, considering the possible grasping and transportation tasks for the UVMS, an improved flexible predefined-time prescribed performance function (FPTPPF) with self-adjustment capability is proposed to avoid the vulnerabilities of the existing prescribed performance functions. A control framework is constructed for the integral barrier Lyapunov function and FPTPPF, which can achieve good tracking performance. Third, a predefined-time extended state observer (ESO) is constructed to address the problems caused by external disturbances, model uncertainties and actuator faults. For strong sudden disturbances, the H∞ control strategy is designed via the backstepping method, which effectively improves the robustness. Finally, the predefined-time stability of the system is proven via Lyapunov stability theory, where the tracking errors can converge to a small region of the null domain in a predefined time. The performance and superiority of the proposed predefined-time control method are verified via simulation comparisons.

Coordinated torque vectoring and direct yaw control based on Kalman filter control allocation for a four in-wheel electric vehicle

This paper presents a novel hierarchical control architecture designed for a four in-wheel electric vehicle (EV), integrating longitudinal and lateral control strategies to optimize performance and lateral stability. On one hand, a high-level longitudinal controller is synthesized to track a desired speed profile and generate a total torque. On the other hand, a direct yaw control (DYC) strategy is employed for steerability and stability control to generate a yaw moment. To manage the total torque as well as the yaw moment, a control allocation layer is employed. Subsequently, the formulation of the control allocation problem is presented, incorporating a gain-scheduling Daisy chaining Kalman filter (DCKF) control allocation to deal with actuator dynamics and fault tolerance. Results demonstrate enhanced performance in normal and emergency driving. Quick adaptability in case of emergency is achieved without overshooting or undershooting, with a smooth response.

Adaptive Fault-Tolerant Control of Mobile Robots with Fractional-Order Exponential Super-Twisting Sliding Mode

Industrial mobile robots easily experience actuator loss of some effectiveness and additive bias faults due to the working scenarios, resulting in unexpected performance degradation. This article proposes a novel adaptive fault-tolerant control (FTC) strategy for nonholonomic mobile robot systems subject to simultaneous actuator lock-in-place (LIP) and partial loss-of-effectiveness (LOE) faults. First, a nominal fractional-order sliding mode controller based on the designed exponential super-twisting reaching law is investigated to reduce the reaching phase time and eliminate the chattering. To address the time-varying LIP faults and uncertainties, a novel barrier function (BF)-based gain is explored to assist the super-twisting law. An estimator is designed to estimate the lower bound of the time-varying partial LOE fault coefficients, thus without requiring the boundary information of faults that is commonly requested in traditional FTC schemes. Combined with the nominal controller clubbed with BF and estimator-based LOE fault compensation term, the fault-tolerant controller is finally constructed. The proposed FTC scheme achieves fast convergence and the sliding variables can be confined in a predetermined neighborhood of the sliding manifold under actuator faults. The results show that the proposed controller has superior tracking performance under faulty conditions compared with other state-of-the-art adaptive FTC approaches.

Non-fragile reliable control for multi-agent systems with actuator faults using an improved L-K functional

Non-fragile reliable control for multi-agent systems with actuator faults using an improved L-K functional

UAV Trajectory Tracking Using Proportional-Integral-Derivative-Type-2 Fuzzy Logic Controller with Genetic Algorithm Parameter Tuning.

Unmanned Aerial Vehicle (UAV)-type Quadrotors are highly nonlinear systems that are difficult to control and stabilize outdoors, especially in a windy environment. Many algorithms have been proposed to solve the problem of trajectory tracking using UAVs. However, current control systems face significant hurdles, such as parameter uncertainties, modeling errors, and challenges in windy environments. Sensitivity to parameter variations may lead to performance degradation or instability. Modeling errors arise from simplifications, causing disparities between assumed and actual behavior. Classical controls may lack adaptability to dynamic changes, necessitating adaptive strategies. Limited robustness in handling uncertainties can result in suboptimal performance. Windy environments introduce disturbances, impacting system dynamics and precision. The complexity of wind modeling demands advanced estimation and compensation strategies. Tuning challenges may necessitate frequent adjustments, posing practical limitations. Researchers have explored advanced control paradigms, including robust, adaptive, and predictive control, aiming to enhance system performance amidst uncertainties in a scientifically rigorous manner. Our approach does not require knowledge of UAVs and noise models. Furthermore, the use of the Type-2 controller makes our approach robust in the face of uncertainties. The effectiveness of the proposed approach is clear from the obtained results. In this paper, robust and optimal controllers are proposed, validated, and compared on a quadrotor navigating an outdoor environment. First, a Type-2 Fuzzy Logic Controller (FLC) combined with a PID is compared to a Type-1 FLC and Backstepping controller. Second, a Genetic Algorithm (GA) is proposed to provide the optimal PID-Type-2 FLC tuning. The Backstepping, PID-Type-1 FLC, and PID-Type-2 FLC with GA optimization are validated and evaluated with real scenarios in a windy environment. Deep robustness analysis, including error modeling, parameter uncertainties, and actuator faults, is considered. The obtained results clearly show the robustness of the optimal PID-Type-2 FLC compared to the Backstepping and PID-Type-1 FLC controllers. These results are confirmed by the numerical index of each controller compared to the PID-type-2 FLC, with 12% for the Backstepping controller and 51% for the PID-Type-1 FLC.