In this paper, an adaptive sensor fault-tolerant tracking control scheme is developed for state-constrained uncertain nonlinear systems. Unlike most existing methods that directly rely on fault-contaminated sensor measurements for feedback control, an adaptive fault-compensated estimator (AFCE) is developed. By embedding an adaptive compensation term into the estimator feedback channel, the AFCE dynamically counteracts sensor faults and enables the joint estimation of system states and external disturbances. To accommodate state constraints, a generalized intermittent state constraint (GISC) approach is introduced. By constructing a novel switching function and an auxiliary variable, the proposed GISC achieves smooth transitions between constrained and unconstrained phases, while eliminating the conventional requirement that the upper and lower bounds have opposite signs. Then, building upon the estimated results, a generalized barrier Lyapunov function is incorporated into the fault-tolerant controller design. Rigorous Lyapunov analysis proves that the actual system output can track the desired reference while all states remain within the prescribed bounds under the established controller. Finally, the effectiveness and superiority of the proposed approach are verified through both simulation and experimental results.

Accurate and physically interpretable wind power forecasting (WPF) is crucial for ensuring the reliable operation of power grid systems. Wind turbines have operational characteristics significantly influenced by complex environmental factors, such as wind speed fluctuations and intermittency, posing challenges for precise wind power modeling. Although deep learning models have become a promising data-driven solution in WPF, their common “closed-box” nature makes it difficult to balance forecast accuracy with the rationality of physical mechanisms. Therefore, based on the neural basis expansion analysis (NBEATSx) network architecture, this article proposes a multistep WPF method, named physics-informed adaptive-weight NBEATSx. This method realizes the deep integration of physical prior knowledge and data-driven models, providing a novel technical path for solving the joint optimization problem of accuracy and interpretability in WPF. The operational constraints of wind turbines, such as cut-in, rated, cut-out wind speeds, and rated power, are explicitly embedded into the network structure. A dynamic trainable weighting mechanism is leveraged for stack outputs, instead of the traditional aggregation strategy of direct summation. The experimental results based on a dataset of a 2-MW wind turbine show that the proposed method is significantly superior to the benchmark models and NBEATSx variants in terms of forecast accuracy and robustness.

This article investigates the practical problem of the fault-tolerant tracking control for nonlinear systems subjected to dynamic uncertainties, unknown disturbances, and simultaneous actuator and sensor faults. A novel performance-enhanced intelligent fault-tolerant control (IFTC) framework is developed by integrating the predefined-time stability theory with the optimal control principle. A 1-dimensional convolutional neural network is proposed as a restorer to efficiently detect and isolate sensor faults, upon whose outputs a single radial basis function neural network is constructed to approximate two distinct components of the unknown system dynamics in real time. The proposed fault-tolerant controller ensures the boundedness of all signals within the closed-loop system and guarantees that the tracking errors converge within a user-specified time. Moreover, by incorporating a cost function related to the control effort, the developed controller avoids the excessive control input, achieving an enhanced control performance alongside the predefined-time convergence. Comprehensive simulation results validate the effectiveness and practicality of the proposed performance-enhanced IFTC scheme, highlighting its potential for real-world applications.

This article proposes a novel multicontroller-based fault tolerance method to cope with a class of high-order sub-fully actuated systems (sub-FASs) with nonlinear uncertainties and actuator faults. As a promising control-oriented theory, the FAS approach is a convenient and powerful tool for nonlinear control. However, the stabilization of sub-FASs, whose input matrix function is not globally invertible, is more sophisticated and challenging due to the issue of control singularity. To address the global fault-tolerant stabilization of uncertain sub-FASs, a high-order nonlinear system model with both multiplicative and additive actuator faults is considered. By introducing the concepts of linear singular set and singularity function, the entire state space is analytically divided into several regions. Then, according to the initial states of system, three different control strategies are developed to overcome singularity and achieve global stabilization, including a FAS-based stabilizing control law, a singularity-avoid tracking control strategy, as well as a singularity-free switching control strategy. The closed-loop response of the faulty system is proven to be ultimately uniformly bounded in all cases, and the effectiveness of proposed method is illustrated through a numerical example.

This article investigates the problem of fault-tolerant control for stochastic high-order fully actuated systems (FASs) with actuator faults. Different from the majority of existing studies focusing on deterministic high-order FASs, this work introduces stochastic disturbances into the systems. Employing the generalized martingale technique, a novel fault-tolerant equivalent controller is formulated. Additionally, an adaptive compensation law is constructed to address time-varying faults promptly. The designed preclosed-loop strategy advocates the advantage of the FAS methodology and guarantees performance by ensuring that the tracking error complies with the user-defined probabilistic ultimate bound. Finally, a numerical case and a practical example of a rotary steerable drilling platform are exploited to demonstrate the effectiveness of the proposed method.

This article portrays a class of local faults (LFs), whose basic characteristic is that the fault occurs only when the system state is in a certain domain. To accurately tolerate LFs for stochastic higher-order fully actuated systems, a novel active fault-tolerant control (AFTC) strategy is proposed. The indicators of entering and leaving fault domains are designed to enable the estimation of fault domains. The boundedness in probability of tracking error is guaranteed theoretically through the whole stages of AFTC. Finally, a practical example is presented to demonstrate the effectiveness of the proposed AFTC framework.

This article delves into fault-tolerant control (FTC) for stochastic fully actuated systems (SFASs) affected by component faults. Parallel to most existing studies focusing on deterministic fully actuated systems (FASs), this work considers the impact of stochastic disturbances. To stabilize the system states effectively, an adaptive state feedback FTC law is developed using a novel mixed-order backstepping technique in the sense of expectation. Under the circumstance of limited measurement, an observer-based FTC framework is also established with the necessary and sufficient condition for the distinguishableness of component faults. Both the devised fault compensation strategies ensure performance by constraining all signals within the prescribed probabilistic bounds, underscoring the advantages inherent in the FAS methodology concurrently. Finally, numerical and physical examples validate the efficacy of the proposed method.

In recent years, high-order fully actuated (HOFA) systems, founded by Prof. GR Duan, have recorded rapid progress for deterministic systems. However, the control issue of stochastic fully actuated systems is still an open problem. This study develops a novel stochastic HOFA system model that complements the existing HOFA methodology. Notably, stochastic signals can be considered in the proposed model, different from the case in the deterministic model. By adopting a high-order operator, equivalent control and stabilization control laws are realized to guarantee the global asymptotic stability in probability of the closed-loop system. For the system with sensor gain faults, an observer-based fault-tolerant control law is designed. Finally, the simulation results validate the effectiveness of the proposed control schemes.

This article is concerned with the problem of intermittent fault detection for a class of nonlinear systems through observer-based methods. Different from the permanent fault detection, this article devotes to detecting both the appearance and disappearance of intermittent faults, which has strict requirements on the convergence property of the observation error. A predefined-time observer is designed for sector-bounded nonlinear systems to guarantee that the observation error reaches stability within a preset time. Using the implicit Lyapunov function method and the homogeneity theory, some linear matrix equations and inequalities are derived to solve the observer gain such that the observation error system is input-to-state stable. Then, the residual is generated and evaluated to detect the fault. Finally, the feasibility and effectiveness of the proposed approach are verified by an experiment on the prototype of rotary steerable drilling tool system.

This paper addresses a path-guided formation-containment control problem by virtue of the aperiodic communication for networked heterogeneous multi-vehicle systems suffering from actuator faults and uncertainties. At the leader layer, a novel three-dimensional (3-D) path-following formation controller endowed with spatial-temporal decoupling and path invariance advantages is presented for multiple quadrotors to accomplish a preassigned formation pattern along implicit paths. At the follower layer, in light of neighboring transmission, a containment controller is proposed that actuates several unmanned vehicles to access the convex hull generated by the leaders. Besides, by incorporating an event-triggered criterion with a state predictor, an aperiodic transmission strategy is established to alleviate the communication burden and evade unnecessary resource wastage. Subsequently, to recognize the actuator faults and uncertainties, guided by invariant manifold principle, unknown system dynamics estimators (USDEs) are elaborated on the strength of straightforward filtering operations. Finally, the stability of entire closed-loop system is proved according to Lyapunov analysis and input-to-state stability (ISS), while the effectiveness and superiority are demonstrated by simulations.

In this article, the problem of fault isolation is studied for linear stochastic time-varying systems. Different from the existing results about fault isolation, the system in this article suffers from strong noise, which increases the difficulty of distinguishing different fault components since the difference in measurement distributions caused by different fault components is small. To handle this problem, a novel fault isolation scheme comprising a set of residuals, a set of evaluation functions, and a decision logic is proposed. First, based on the hypothesis testing method, two performance indices reflecting the probability of two classes of false isolation induced by noise are introduced to quantitatively evaluate the influence of strong noise on residuals. Under a given threshold, the existence of the optimal residual with two minimal performance indices is proved in this article, which minimizes the effect of noise on residuals. Subsequently, a suboptimal recursive residual whose parameter gradually tends to the parameter of the optimal residual is designed. Following this, by employing the$\chi ^{2}$test and the predesigned decision logic, fault isolation can be realized. Finally, two illustrative examples are provided to show the effectiveness of the proposed method.

In this article, the problem of intermittent fault (IF) detection is investigated for linear stochastic systems over sensor networks, where the appearing and disappearing times, and magnitude of IF are all nondeterministic. By utilizing the moving-horizon estimator, a novel residual generator is designed to realize the distributed detection of IFs in sensor networks. Different from the traditional moving horizon estimation algorithms, weight matrices of the quadratic cost function in this article are regulated by an unreliability index of the prior estimate to suppress the smearing effect of IFs. In virtue of the matrix transformation method and statistical theory, estimator parameters are obtained and the detectability of a single IF is analyzed by using the residual. In order to avoid the collisions of detection results from different residuals, the global detectability condition is given for all IFs. A cooperative decision-making strategy is proposed such that the only detection result can be guaranteed, which includes the appearing and disappearing times of IFs, and the nodes suffering from IFs. Finally, an illustrative example is provided to show the feasibility and effectiveness of the derived results.

This article is concerned with the problem of state estimation for continuous-time polynomial nonlinear (CTPN) systems with unknown but bounded disturbances. The Taylor polynomial expansion technique is employed to realize the conversion from polynomial nonlinear systems to linear-parameter-varying systems related to the estimate. Moreover, for the purpose of saving the communication resources, an event-triggered sampling scheme is first introduced in the state estimation for CTPN systems, where the event-triggered condition is changed dynamically and the Zeno behavior is excluded. Based on the matrix inequality approach, a sufficient condition is derived in terms of the parameter-dependent linear matrix inequality (LMI) such that the estimation error system is input-to-state stable. Then, the desired estimator parameters can be obtained by solving the parameter-dependent LMI via the sum of squares decomposition technique. Finally, two examples with one concerning the permanent magnet synchronous motor systems are provided to demonstrate the usefulness of proposed method.

In this paper, the event-based finite-horizon H∞state estimation problem is investigated for a class of discrete time-varying stochastic dynamical networks with stateand disturbance-dependent noises [also called (x, v)-dependent noises]. An event-triggered scheme is proposed to decrease the frequency of the data transmission between the sensors and the estimator, where the signal is transmitted only when certain conditions are satisfied. The purpose of the problem addressed is to design a time-varying state estimator in order to estimate the network states through available output measurements. By employing the completing-the-square technique and the stochastic analysis approach, sufficient conditions are established to ensure that the error dynamics of the state estimation satisfies a prescribed H∞performance constraint over a finite horizon. The desired estimator parameters can be designed via solving coupled backward recursive Riccati difference equations. Finally, a numerical example is exploited to demonstrate the effectiveness of the developed state estimation scheme.

In this paper, the dynamic output-feedback control problem is studied for a class of discrete-time nonlinear stochastic systems with successive packet dropouts and uniform quantization effects. The stochastic system under investigation involves state-, control-, and disturbance-dependent noises (also called (x, u, v)-dependent noises) that bring in substantial difficulties in the stability analysis. The phenomenon of successive packet dropouts is governed by a binary switching random sequence. The measurement output is subject to the uniform quantization which results in the norm-bounded disturbances, and the concept of input-to-state stability in probability is introduced to deal with this kind of disturbances. In virtue of intensive stochastic analysis, several sufficient conditions are established to guarantee that the closed-loop system is input-to-state stable in probability under the effects of probabilistic packet dropouts as well as uniform quantizations. As an easy consequence, the design problem with linear output-feedback controllers is discussed for the benefits of practical applications and some simplified conditions are derived. Finally, a numerical example is presented to illustrate the effectiveness of the proposed method.