Actuator Faults Research Articles

Prescribed Performance Fault-Tolerant Attitude Tracking Control for UAV with Actuator Faults

This paper proposes a prescribed performance fault-tolerant control based on a fixed-time extended state observer (FXTESO) for a carrier-based unmanned aerial vehicle (UAV). First, the attitude motion model of the UAV is introduced. Secondly, the proposed FXTESO is designed to estimate the total disturbances including coupling, actuator faults and external disturbances. By using the barrier Lyapunov function (BLF), it is proved that under prescribed performance control (PPC), the attitude tracking error is stable within the prescribed range. The simulation results for tracking the desired attitude angle show that the average overshoot and stabilization time of PPC-FXTESO is 0.00455rad and 6.2s. Comparatively, the average overshoots of BSC-ESO and BSC-FTESO are 0.035rad and 0.027rad, with stabilization times of 14.97s and 12.56s, respectively. Therefore, the control scheme proposed in this paper outperforms other control schemes.

Adaptive control for nonlinear systems with unknown actuator dynamics based on a novel extended Nussbaum function

AbstractIn this article, we introduced a novel definition for a class of extended Nussbaum functions that have looser requirements than the traditional Nussbaum function. The extended Nussbaum function achieves comparable performance to the traditional Nussbaum function while addressing the challenges of unknown control directions and time‐varying actuator faults. We then provide a specific example function based on the extended Nussbaum function definition. Unlike the traditional Nussbaum function, which oscillates with fixed frequency and constantly increasing amplitude, this example function constrains the amplitude and oscillates with decreasing frequency. Consequently, its amplitude and derivative remain bounded. The oscillation of the system caused by the too large amplitude of the traditional Nussbaum function can be avoided. Furthermore, this example function is constructed using portions of elliptic curve. As a result, it is continuous and differentiable, in contrast to the normal decreasing‐frequency Nussbaum function using trigonometric function, which is only continuous. The overall closed‐loop system's global stability and asymptotic convergence of system states are successfully achieved. Simulation results demonstrate the effectiveness of the proposed function.

Adaptive sliding mode and RBF neural network based fault tolerant attitude control for spacecraft with unknown uncertainties and disturbances

Taking the external disturbance, model uncertainty, actuator failure, and actuator saturation into consideration, this paper investigates the nonlinear fault-tolerant attitude control problem for the spacecraft. First, to deal with the disturbance and uncertainty with unknown boundaries, an adaptive sliding mode control method is proposed to stabilize the state variables of the spacecraft attitude control system. Then the actuator failure and saturation are further considered, and the second spacecraft fault-tolerant attitude controller is derived based on adaptive sliding mode control and radial basis function (RBF) neural networks. The adaptive control gains reduction technique is applied in the design process, which can weaken the chattering phenomenon of the controller to some extent, and the stability of the attitude control system is analyzed through Lyapunov theory. In the proposed controller, model information and external disturbance are not required and only the system states are needed. Furthermore, the boundaries of the model uncertainty, external disturbance, actuator fault and saturation are assumed to be existing but unknown by the proposed controllers. These features make the proposed attitude controllers need few modeling information of the spacecraft, or maintain strong robustness even if the model or external environment occurs significant changes. Finally, numerical simulations demonstrate the great performance of the proposed fault-tolerant attitude control method.

Guaranteed Performance Event-Triggered Adaptive Consensus Control for Multiagent Systems under Time-Varying Actuator Faults

A guaranteed performance event-triggered adaptive consensus control is established for uncertain multiagent systems under time-varying actuator faults. To eliminate the impact caused by actuator faults, an adaptive neural network compensation strategy is developed. Simultaneously, by implementing the asymmetric barrier Lyapunov function and transform function, a prescribed time consensus control with guaranteed performance, is constructed. Furthermore, to reduce the frequency of information transmission, an adjustable switching event-triggered control (ASETC) is proposed by using a modified hyperbolic tangent function. It combines the advantage of the relative threshold strategies and the characteristics of the hyperbolic tangent function, giving better flexibility in saving network resources and guaranteeing system performance. By applying the constructed control method, systems with prescribed performance consensus in a prescribed time are achievable while limited network resources and unknown time-varying faults are present. Some simulation examples implemented in MATLAB (R2022a) are given to demonstrate the above results.

Hidden Markov model–based asynchronous fault-tolerant control for mode-dependent time-delayed Markovian jump systems with uncertain conditional probability

In control engineering, time-delayed systems are quite common. Modern industrial systems often involve delays due to complicated network environments, the accuracy of sensors and actuators, or signal processing. Speaking of Markovian jump systems, such systems undergo discrete mode changes caused by a Markov process. Hence, it is reasonable to consider the mode-dependent time delays. In this paper, the asynchronous fault-tolerant controller is developed for mode-dependent time-delayed Markovian jump systems. First, the estimations of states and actuator fault are provided by designing an intermediate observer. Then, based on these estimations, the asynchronous fault-tolerant controller is designed by virtue of the hidden Markov process with uncertain conditional probability. Finally, the existence conditions of both the designed observer and fault-tolerant controller are given in terms of linear matrix inequalities. In the meantime, a numerical example is illustrated to validate the effectiveness and potential of the proposed method.

Event‐triggered obstacle avoidance control for autonomous surface vehicles with actuator faults

AbstractThe article addresses the event‐triggered obstacle avoidance control problem for autonomous surface vehicles subject to actuator faults. In order to tackle the challenges presented by unknown actuator faults, internal model uncertainties, and external disturbances, we propose a fault‐tolerant control scheme that relies on an extended state observer to estimate the unknown parameters. In addition, considering the occurrence of actuator faults, a novel event‐triggered mechanism is developed to reduce wear of actuator and ensures the performance of system. On this basis, we employ backstepping technique and an improved artificial potential function methods to develop an event‐triggered obstacle avoidance control scheme with the capability of fault tolerance. This proposed control strategy ensures the uniform ultimate boundedness of tracking errors. In contrast to existing results, the presented control strategy simultaneously holds the performance of obstacle avoidance and fault tolerance and reduce the update frequency of actuators. The effectiveness of the provided control scheme has been confirmed through simulation.

Integral terminal sliding mode fault tolerant control of quadcopter UAV systems

The article presents an active fault-tolerant control scheme with an integral terminal sliding mode controller for the UAV systems. This scheme effectively addresses saturation issues, disturbances, and sensor and actuator faults. Initially, the quadcopter UAV's model is represented in state space form. Subsequently, an augmented system incorporating auxiliary states from sensor faults is developed. An adaptive sliding mode observer is proposed for estimating the actuator and sensor faults. The integral terminal sliding mode fault-tolerant control, designed for altitude and attitude regulation, relies on fault estimation data. In contrast, a cascade proportional-integral-derivative (PID) controller is employed for position control. Simulation results demonstrate the superiority of the proposed method over existing control algorithms.

Robust Fixed-Time Fault-Tolerant Control for USV with Prescribed Tracking Performance

The unmanned surface vessel (USV) is an emerging marine tool with its advantages of automation and intelligence in recent years; the good trajectory tracking performance is an important capability. This paper proposes a novel prescribed performance fixed-time fault-tolerant control scheme for an USV with model parameter uncertainties, unknown external disturbances, and actuator faults, based on an improved fixed-time disturbances observer. Firstly, the proposed observer can not only accurately and quickly estimate and compensate the lumped nonlinearity, including actuator faults, but also reduce the chattering phenomenon by introducing the hyperbolic tangent function. Then, under the framework of prescribed performance control, a prescribed performance fault-tolerant controller is designed based on a nonsingular fixed-time sliding mode surface, which guarantees the transient and steady-state performance of an USV under actuator faults and meets the prescribed tracking performance requirements. In addition, it is proved that the closed-loop control system has fixed-time stability according to Lyapunov’s theory. Finally, upon conducting numerical simulations and comparing the proposed control scheme with the SMC and the finite-time NFTSMC scheme, it is evident that the absolute error tracking performance index of the proposed control scheme is significantly lower, thus indicating its superior accuracy.

Finite-time asynchronous control for semi-Markov jump systems with random uncertainties and actuator faults

This article is aimed at studying the finite-time reliable asynchronous control issue for continuous semi-Markov jump systems (S-MJSs) with actuator failures and randomly occurring uncertainties (ROUs). To characterize the asynchronous phenomenon between the system mode and the controller mode, the mode detector approach via a hidden semi-Markov model (HSM) is adopted. The transition probability (TP) matrix of the HSM is nonhomogeneous, which can be provided by a polytopic structure. By resorting the mode-dependent Lyapunov function, sufficient conditions are attained to guarantee the stochastic finite-time boundedness (SFTB) and H∞ performance of the closed-loop system. Then, according to a skillful matrix inequality transforming method, the reliable asynchronous control law is constructed. Finally, a tunnel diode circuit system and a mass-spring-damper mechanical system are proposed to demonstrate the superiority, effectiveness and practicality of the developed design method.

Fault-Tolerant Control of Autonomous Underwater Vehicle Actuators Based on Takagi and Sugeno Fuzzy and Pseudo-Inverse Quadratic Programming under Constraints.

Autonomous Underwater Vehicles (AUVs) play a significant role in ocean-related research fields as tools for human exploration and the development of marine resources. However, the uncertainty of the underwater environment and the complexity of underwater motion pose significant challenges to the fault-tolerant control of AUV actuators. This paper presents a fault-tolerant control strategy for AUV actuators based onTakagi and Sugeno (T-S) fuzzy logic and pseudo-inverse quadratic programming under control constraints, aimed at addressing potential actuator faults. Firstly, considering the steady-state performance and dynamic performance of the control system, a T-S fuzzy controller is designed. Next, based on the redundant configuration of the actuators, the propulsion system is normalized, and the fault-tolerant control of AUV actuators is achieved using the pseudo-inverse method under thrust allocation. When control is constrained, a quadratic programming approach is used to compensate for the input control quantity. Finally, the effectiveness of the fuzzy control and fault-tolerant control allocation methods studied in this paper is validated through mathematical simulation. The experimental results indicate that in various fault scenarios, the pseudo-inverse combined with a nonlinear quadratic programming algorithm can compensate for the missing control inputs due to control constraints, ensuring the normal thrust of AUV actuators and achieving the expected fault-tolerant effect.

Multiple model fault diagnosis and fault tolerant control for the launch vehicle’s attitude control system

Abstract For the launch vehicle attitude control problem, traditional methods can seldom accurately identify the fault types, making the control method lack of pertinence, which largely affects the effect of attitude control. This paper proposes an active fault tolerant control strategy, which mainly includes fault diagnosis and fault tolerant control. In the fault diagnosis part, a small deviation attitude dynamics model of the launch vehicle is established, Kalman filters with different structures are designed to detect and isolate faults through residual changes, and the fault quantity of the actuator is further estimated. In the fault tolerant control part, the following control scheme is adopted according to the above diagnostic information: when the sensor fault is detected, the sensor measurement data is reconstructed; when the actuator fault is identified, the control allocation matrix is reconstructed. Simulation results show that the proposed method can effectively diagnose sensor fault and actuator faults, and significantly improve attitude tracking accuracy and control adjustment time.

Prescribed-time fault-tolerant tracking control for Markov jump nonlinear systems based on saturation dynamics filters

This paper investigates the problem of prescribed-time fault-tolerant tracking control for a class of Markov jump nonlinear systems affected by actuator faults and input saturation. First, a new time-varying constraint function and an error transformation are invoked to transform the original prescribed-time tracking control problem into a tracking control problem with deferred constraint for tracking error. Furthermore, the parameter correction terms are designed, on which a novel Markov jump saturation dynamic filter is constructed to handle the unfavorable effect of input saturation. Meanwhile, the projection operator technique and the norm estimation methodology are integrated to devise adaptive laws to estimate the unknown actuator faults evolving in accordance with the Markov chain. A new prescribed-time fault-tolerant tracking control scheme is put forward that realizes the deferred constraint on the tracking error, which in turn ensures that the tracking error of the system converges to a prescribed accuracy within a prescribed-time. Finally, two simulation examples, one of which implements a prescribed-time fault-tolerant tracking control for rotary steerable drilling tool system, are presented to illustrate the effectiveness and practicality of the developed scheme.

Finite-time fault-tolerant control of a spacecraft using composite learning in the presence of moment of inertia uncertainty and external disturbances

In this paper, a neuro-adaptive finite-time fault-tolerant control for a class of nonlinear systems is proposed based on the backstepping method, considering model uncertainties and external disturbances. To compensate for model uncertainties, a neural network is employed, while external disturbances and the estimation error of the neural network are handled using a disturbance observer. Additionally, actuator faults are estimated and compensated for distinctly using two adaptive elements in the proposed control system. Moreover, to enhance the learning of the neural network, disturbance observer, and other adaptive elements, particularly in the lack of persistently exciting signals, a composite learning approach is utilized. To this end, a state-observer is introduced, and its estimation error along with the tracking error is used in the updating rules of all the adaptive elements in the design. Additionally, to prevent the explosion of terms in the backstepping method, a command filter with practical finite-time stable auxiliary variables is incorporated into the proposed controller. Finite-time convergence of the tracking error to zero is rigorously proved using the Lyapunov method. To demonstrate the effectiveness of the proposed control method, the problem of attitude control of a rigid spacecraft with moment of inertia uncertainty, external disturbances, and multiple actuator faults is considered since precise attitude control of a spacecraft is of great importance in different maneuvers. The results show that using the introduced control system, the spacecraft is able to track the desired trajectory under different types of uncertain dynamics.

Nonlinear adaptive fault-tolerant control for full-car active suspension with velocity measurement errors and full-state constraints

This work concentrates on the vibration suppression problem for full-car active suspension in the presence of multiple actuator faults, velocity measurement errors, full-state constraints and external disturbances, simultaneously. Herein, a novel nonlinear fault-tolerant control (FTC) strategy is developed by the aid of the online estimation of fault parameters, and it can accommodate multiple actuator faults without the information obtained from costly fault detection and isolation mechanism. Then, the violation of full-state constraints can be prevented by introducing the log-type barrier Lyapunov function (BLF) into the adaptive backstepping method, and the problems related to velocity measurement errors of car body and external disturbances are handled under the robust control framework, so the complexity caused by state constraints, actuator faults and velocity measurement errors can thus be taken into consideration. The stability analysis is subsequently conducted with the assistance of Lyapunov theory, the boundness of all signals is confirmed, and the non-violation of the displacement and velocity constraints for car body motions is guaranteed. In the end, the performance of the proposed controller is evaluated under sinusoidal and random road surfaces. Importantly, the simulation results are presented to indicate the validity and advantages of the developed approach despite the existence of multiple actuator faults, velocity measurement errors and external disturbances.

Fault‐tolerant control for multi‐agent systems: Addressing actuator and sensor faults in leaderless/leader‐follower architectures

AbstractThis paper addresses the issue of actuator and sensor faults causing instability in leaderless/follower multi‐agent systems (MASs) and investigates adaptive fault‐tolerant control (FTC) algorithms to secure stability. Sensor faults can disrupt conventional observers by interfering with system state measurements. To address this issue, the descriptor approach is introduced to construct an augmented system from the original system, which converts the faults into components of the augmented system state. Next, by utilizing a sliding‐mode term to compensate for the lumped uncertainty's impact, an observer is designed to realize state estimation for the augmented system. Then, based on the estimations, adaptive FTC protocols are investigated for leaderless/leader‐follower MASs to retain stability against concurrent faults and uncertainties. To enhance control efficiency, the control gains are updated with the estimations from neighbors and proved to be ultimately upper bounded. Finally, the proposed method is validated through numerical simulations of multiple quadrotors.

Distributed consensus and formation control of multi-AUV systems under actuator faults and switching topology

This note presents the distributed consensus and formation control for a group of AUVs, comprising one leader and three followers arranged in a diamond formation. The study addresses significant control challenges, including external disturbances, noise, model uncertainties, actuator faults, stochastic switching topologies, time-varying communication delays, and positional information between agents. Stochastic switching topologies are assumed to follow a Markov chain. To effectively address these challenges and ensuring satisfactory performance levels, two control architectures have been formulated to facilitate the imposition of desired trajectories upon the system states. The initial architecture amalgamates sliding mode control methodology with adaptive algorithms, designed explicitly for tracking missions in the presence of fully functional actuators. In response to potential actuator failures, the second control architecture integrates passive fault-tolerant techniques, significantly enhancing system reliability under such circumstances as well as switching topology. The proposed framework demonstrates its effectiveness in managing the high nonlinearity and coupled dynamics of AUVs. Simulation results validate the efficacy of the developed strategy. It successfully establishes a desired consensus and formation among agents, reduces chattering phenomena, accurately tracks reference trajectories, handles disturbances and uncertainties within an unknown domain, and achieves finite-time convergence.

Vision-Guided Tracking and Emergency Landing for UAVs on Moving Targets

This paper presents a vision-based adaptive tracking and landing method for multirotor Unmanned Aerial Vehicles (UAVs), designed for safe recovery amid propulsion system failures that reduce maneuverability and responsiveness. The method addresses challenges posed by external disturbances such as wind and agile target movements, specifically, by considering maneuverability and control limitations caused by propulsion system failures. Building on our previous research in actuator fault detection and tolerance, our approach employs a modified adaptive pure pursuit guidance technique with an extra adaptation parameter to account for reduced maneuverability, thus ensuring safe tracking of moving objects. Additionally, we present an adaptive landing strategy that adapts to tracking deviations and minimizes off-target landings caused by lateral tracking errors and delayed responses, using a lateral offset-dependent vertical velocity control. Our system employs vision-based tag detection to ascertain the position of the Unmanned Ground Vehicle (UGV) in relation to the UAV. We implemented this system in a mid-mission emergency landing scenario, which includes actuator health monitoring of emergency landings. Extensive testing and simulations demonstrate the effectiveness of our approach, significantly advancing the development of safe tracking and emergency landing methods for UAVs with compromised control authority due to actuator failures.

Model-free control of a magnetically supported plate

Established model-based methods often use a combination of state feedback and observer to control complex systems. They rely on detailed mathematical models that are often hard to derive. Nonetheless, such methods may achieve a high level of accuracy, which justifies the cumbersome modelling. An alternative approach is model-free control, in a form introduced by Fliess and Join, where the system is approximated in a short time interval by a low-order differential equation with unknown parts, a so-called ultra-local model. This control method is a powerful tool, but the parametrisation and the concrete implementation may require time, effort, and experience. The present paper investigates the systematic tuning of a model-free controller for a magnetically supported plate that is modelled as an unstable multiple-input multiple-output system. Furthermore, the incorporation of model information into the model-free controller is investigated. These adaptations ultimately improve results by simplifying parameter tuning and interpretation of estimates. Several experiments are carried out on a test bed to show the capabilities of the proposed algorithms for set point stabilisation and trajectory tracking. The effects of the different parameters in the model-free controllers are addressed, and excellent robustness with respect to actuator faults is demonstrated. Filters for estimating derivatives and unknown quantities are designed using an open-source toolbox.

Adaptive containment fault‐tolerant control for saturated nonlinear multiagent systems with multiple faults and periodic disturbances

AbstractThis paper addresses the problem of adaptive containment fault‐tolerant control for nonlinear multiagent systems with periodic disturbances. Different from most existing fault‐tolerant control schemes, the form of multiple faults is explicitly considered in this paper, including actuator faults and sensor faults. By combining the Fourier series expansion with neural networks, the unknown nonlinear dynamics subject to time‐dependent periodic disturbances are approximated. Then, the “complexity of explosion” issue that exists in traditional backstepping‐based results is avoided by introducing a first‐order sliding‐mode differentiator. It is proved that the developed containment control policies can ensure that all signals of the close‐loop systems are uniformly ultimately bounded, and all followers can converge to a convex area formed by multiple leaders. Simulation results verify the validity of the proposed scheme.

Semi-Global Bipartite Fault-Tolerant Containment Control for Heterogeneous Multiagent Systems With Antagonistic Communication Networks and Input Saturation.

Semi-global bipartite fault-tolerant containment control framework on antagonistic communication networks is proposed in this article for heterogeneous multiagent systems (MASs) under the influence of input saturation and actuator faults. An observer is constructed to estimate the leaders' states on signed digraph, where the communication networks are antagonistic. A fully distributed virtual control approach is developed to acquire the containment trajectory. Based on the observer, a semi-global containment control method is developed to compensate for the detrimental impacts of both input saturation and actuator faults. Besides, the dynamics and state-space dimensions of the agents can be different. The proposed framework overcomes two drawbacks of the conventional containment control: 1) the containment trajectory is obtained under general antagonistic communication networks, which is more general in engineering applications and 2) both actuator faults and input saturation are solved for heterogeneous agents, which relaxes the limitation of homogeneous dynamics. Finally, a simulation example is conducted to test and verify the feasibility of the proposed method framework.