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This article investigates the distributed fault-tolerant time-varying formation control problem for multiple unmanned airships (UAs) against limited communication ranges and input saturation to achieve the safe observation of a smart city. To address the strongly nonlinear functions caused by the time-varying formation flight with limited communication ranges and bias faults, intelligent adaptive learning mechanisms are proposed by incorporating fuzzy neural networks. Moreover, Nussbaum functions are introduced to handle the input saturation and loss-of-effectiveness faults. The distinct features of the proposed control scheme are that time-varying formation flight, actuator faults including bias and loss-of-effectiveness faults, limited communication ranges, and input saturation are simultaneously considered. It is proven by Lyapunov stability analysis that all UAs can achieve a safe formation flight for the smart city observation even in the presence of actuator faults. Hardware-in-the-loop experiments with open-source Pixhawk autopilots are conducted to show the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This article presents an enhanced fault-tolerant synchronization tracking control scheme using fractional-order (FO) calculus and intelligent learning architecture for networked fixed-wing unmanned aerial vehicles (UAVs) against actuator and sensor faults. To increase the flight safety of networked UAVs, a recurrent wavelet fuzzy neural network (RWFNN) learning system with feedback loops is first designed to compensate for the unknown terms induced by the inherent nonlinearities, unexpected actuator, and sensor faults. Then, FO sliding-mode control (FOSMC), involving the adjustable FO operators and the robustness of SMC, are dexterously proposed to further enhance flight safety and reduce synchronization tracking errors. Moreover, the dynamic parameters of the RWFNN learning system embedded in the networked fixed-wing UAVs are updated based on adaptive laws. Furthermore, the Lyapunov analysis ensures that all fixed-wing UAVs can synchronously track their references with bounded tracking errors. Finally, comparative simulations and hardware-in-the-loop experiments are conducted to demonstrate the validity of the proposed control scheme. [ABSTRACT FROM AUTHOR]

• A decentralized fault-tolerant control scheme is developed for multiple UAVs against actuator faults. • Finite-time synchronization tracking control is achieved by utilizing the fractional power of the synchronized errors. • The prescribed performance function is used to constrain the synchronization tracking performance. • The norms of the weighting vectors are used for the estimation to reduce the computational burden. This paper is concerned with the decentralized finite-time fault-tolerant attitude synchronization tracking control problem for multiple unmanned aerial vehicles (multi-UAVs) with prescribed performance. Failure to counteract actuator faults in the formation flight of multi-UAVs in a limited time may lead to catastrophic consequences. By integrating the prescribed performance functions into the synchronization tracking errors, a new set of errors is defined. Based on the transformed errors, a finite-time attitude synchronization tracking control scheme is developed by using neural networks and finite-time differentiator techniques. The neural networks are utilized to identify the unknown nonlinear terms induced by uncertainties and actuator faults. To reduce the computational burden caused by estimating the weight vectors, the norms of weight vectors are used for the estimation, such that the number of adaptive parameters is significantly reduced and independent from the number of neurons. The finite-time differentiators are utilized to estimate the intermediate control signals and their derivatives. Moreover, auxiliary dynamic signals with explicit consideration of differentiator estimation errors are introduced into the control scheme to guarantee the finite-time convergences of the synchronized tracking errors. Furthermore, it is shown that by using the Lyapunov method, all UAVs can track their individual attitude references, while the synchronized tracking errors among UAVs are all bounded in finite time and confined within the prescribed performance bounds. Finally, comparative simulation studies on multi-UAVs are conducted to verify the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Abstract: Advanced fault detection and accommodation schemes are required for ensuring efficient and reliable operation of modern wind turbines. This paper presents a novel approach in designing a fault detection and diagnosis (FDD) and fault-tolerant control (FTC) scheme for a wind turbine using fuzzy modeling, identification and control techniques. First, an improved gain-scheduled proportional-integral (PI) control system based on fuzzy gain scheduling (FGS) technique for multi-input and multi-output wind turbine system is designed. Then, to accommodate sensor faults and based on a signal correction algorithm, an active fault-tolerant control system (AFTCS) is developed as an extension of the gain-scheduled PI control system. The AFTCS exploits the fault information from a model-based FDD scheme developed using fuzzy modeling and identification method. The proposed schemes are evaluated by a series of simulations on a well-known large off-shore wind turbine benchmark in the presence of wind turbulences, measurement noises, and different realistic fault scenarios. All results indicate high effectiveness and robustness of the designed control systems in both fault-free and faulty operations of the wind turbine. [Copyright &y& Elsevier]

This paper investigates the distributed finite-time fault-tolerant containment control problem for multiple unmanned aerial vehicles (multi-UAVs) in the presence of actuator faults and input saturation. The distributed finite-time sliding-mode observer (SMO) is first developed to estimate the reference for each follower UAV. Then, based on the estimated knowledge, the distributed finite-time fault-tolerant controller is recursively designed to guide all follower UAVs into the convex hull spanned by the trajectories of leader UAVs with the help of a new set of error variables. Moreover, the unknown nonlinearities inherent in the multi-UAVs system, computational burden, and input saturation are simultaneously handled by utilizing neural network (NN), minimum parameter learning of NN (MPLNN), first-order sliding-mode differentiator (FOSMD) techniques, and a group of auxiliary systems. Furthermore, the graph theory and Lyapunov stability analysis methods are adopted to guarantee that all follower UAVs can converge to the convex hull spanned by the leader UAVs even in the event of actuator faults. Finally, extensive comparative simulations have been conducted to demonstrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This paper studies the adaptive neural control (ANC)-based tracking problem for discrete-time nonlinear dynamics of an unmanned aerial vehicle subject to system uncertainties, bounded time-varying disturbances, and input saturation by using a discrete-time disturbance observer (DTDO). Based on the approximation approach of neural network, system uncertainties are tackled approximately. To restrain the negative effects of bounded disturbances, a nonlinear DTDO is designed. Then, a backstepping technique-based ANC strategy is proposed by utilizing a constructed auxiliary system and a discrete-time tracking differentiator. The boundness of all signals is proven in the closed-loop system under the discrete-time Lyapunov analysis. Finally, the feasibility of the proposed ANC technique is further specified based on numerical simulation results. [ABSTRACT FROM AUTHOR]

This article finds the minimum cost that is needed to achieve the approximate controllability of switched infinite-dimensional linear systems (IDLSs) by a newly defined Gramian operator. Such a new cost evaluation method helps to establish a fault recoverability condition, which reveals the ability of faulty switched IDLSs to maintain the approximate controllability under given energetic constraints. An example of the swarming system is taken to illustrate the theoretical results. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a new fault tolerant control methodology is proposed for partial loss of control authority in aircraft using piecewise affine (PWA) slab models while minimizing an upper bound on a quadratic cost function. The proposed controller stabilizes and satisfies performance bounds for both the nominal and faulty systems. The controller design criteria are cast as a set of Linear Matrix Inequalities (LMIs) that can be solved efficiently. The new technique is illustrated in a numerical example for the Beechcraft 99 aircraft model. [Copyright &y& Elsevier]

Abstract: This paper presents an average dwell-time approach for reliable stabilization of a class of switched nonlinear systems. Due to the nature of average dwell-time techniques and the representation of actuator faults, this paper has the following features compared with the existing methods in the literature: (1) the proposed method is independent of arbitrary switching policies provided that switching is on-the-average slow enough; (2) the proposed controller exponentially stabilizes this class of nonlinear systems with actuator faults and its nominal (i.e., without actuator faults) systems without necessarily changing any structures and/or parameters of the proposed controllers; and (3) the proposed method treats all actuators in a unified way without necessarily classifying all actuators into faulty actuators and healthy ones. A numerical example is given to show the effectiveness of the proposed method. [Copyright &y& Elsevier]

The refined fault tolerant tracking control (FTTC) scheme is developed for multiple fixed-wing unmanned aerial vehicles (UAVs) against actuator faults and wind effects under event-triggered communication. To decrease the communication overload caused by the continuous information transmissions among neighboring UAVs, an event-triggered communication mechanism is incorporated to dynamically reduce and increase the triggering frequency at the pre-fault and post-fault phases, respectively, thus reducing the communication burden while ensuring the synchronization tracking performance of faulty UAVs. Moreover, refined FTTC performance with event-triggered communications can be achieved by skillfully invoking fractional-order calculus. Furthermore, interval type-2 fuzzy neural networks with fractional-order adaptive modulation factors are constructed to compensate for the unknown nonlinearities. Lyapunov stability analysis has revealed that all attitudes can synchronously track their references. Simulation results are presented to demonstrate that communication resource-saving refined FTTC performance can be guaranteed with different fractional-order operators. • An event-triggered communication mechanism is developed to reduce the signal transmission times. • A fractional-order FTTC method is developed for multiple fixed-wing UAVs with refined performance. • Interval type-2 FNNs are constructed for compensating unknown terms with FO adaptive laws. [ABSTRACT FROM AUTHOR]