Achieving a rapid dynamic response is of critical importance in nonlinear system control. This paper develops a generalized adjustable predefined-time (GAPT) control and observation scheme for second-order nonlinear dynamical systems, aiming to overcome the limitation of non-adjustable actual convergence time in traditional predefined-time methods. Firstly, a novel GAPT system framework is proposed. This framework promotes the concept of traditional predefined time systems and introduces an adjustment parameter to flexibly adjust the actual convergence time of the system. Secondly, an adaptive generalized adjustable predefined-time observer (AGAPTO) is designed, which combines adaptive laws to ensure that the observation error converges to the specified boundary within a predefined time, while the disturbance estimation error achieves exponential convergence. Thirdly, a nonsingular generalized adjustable predefined-time sliding mode controller (NGAPTSMC) is proposed. This controller not only addresses the singularity issue of traditional predefined-time sliding modes, but also utilizes the GAPT characteristics to achieve adjustability of the actual convergence time for the arrival and switching stages. The effectiveness and superiority of the proposed method are validated through numerical simulations and real-time experiments.

This paper investigates the asynchronous filtering problem for single-machine infinite bus (SMIB) power systems subject to stochastic transmission line faults. The system is modeled as a discrete-time Markov jump system (MJS) to capture the random switching behavior induced by transmission line faults. To address the asynchrony between the system modes and the filter operation, a hidden Markov model (HMM) is adopted. The filtering problem is reformulated as a regulation problem by introducing a quadratic performance index based on output estimation errors, offering a filtering-based alternative to control strategies. To solve the associated coupled algebraic Riccati equations (CAREs), an inverse reinforcement learning (IRL)–based algorithm is developed, which enables model-free filtering without requiring prior knowledge of the system dynamics or transition probabilities. The convergence of the proposed algorithm is rigorously analyzed, and a numerical example based on an SMIB power system with stochastic faults is provided to validate its effectiveness.

This paper investigates the fixed-time sliding mode control (FTSMC) problem for a quadcopter unmanned aerial vehicle (QUAV), which is driven by a lip-reading recognition module. The lip-reading recognition module is consisted of a trained deep neural network with the structure of 2D-Conv+GhostNet+TCN. In order to reduce the communication burden between the remote controller and the QUAV as well as reduce the computation burden in running the lip-reading recognition module, the event-triggered mechanism is introduced to the position controller design. The low-bound of the triggering interval is derived explicitly so that the Zeno phenomenon can be excluded. Furthermore, in order to overcome the main obstacle in high-accuracy control of QUAV, this paper launches a novel wind disturbance rejection approach by using wind field model, which is motivated by the physical dynamic characteristics of the practical wind. Specifically, the wind disturbance is estimated in the designed FTSMC by applying a specific wind field equation with preassigned physical parameters. To further reduce the chattering in the controller, a fitting technique is introduced via a local multivariate linear regression. Finally, both simulation and human-in-the-loop experiment results verify the applicability of the proposed control approach for the lip-reading-driven QUAV system. Note to Practitioners—This research is motivated by the need to design lip-reading-driven QUAV. In noisy environments or when silence is required, the efficiency of traditional human-computer interaction methods such as speech recognition is greatly reduced. Especially for people with damaged vocal cords, speech recognition is not achievable. In addition, it is difficulty to realize high-precision anti-interference control of QUAV with lower computational and communication burdens. In order to solve these problems, this research designs a lip-reading recognition module for QUAV control to cope with various complex application scenarios and realizes high-performance control by FTSMC algorithm. The key of this work to save system resources is to introduce the event-triggered mechanism into the position controller of the QUAV. In addition, this paper introduces the wind field model into the QUAV model to realize the wind disturbance suppression. The lip-reading-driven QUAV proposed in this paper have a wide range of applications, such as controlling QUAV in hazardous environments and improving the efficiency of interaction between human and QUAV.

In this paper, a non-force-sensing variable admittance control approach is proposed for lower limb rehabilitation exoskeleton robots. This approach aims to provide satisfactory assistance and rehabilitation-training performance for users of such exoskeletons. Our method initiates with a novel fixed-time sliding mode observer that estimates human-machine interaction torques according to generalized momentum. This observer-based torque estimation strategy can estimate the interaction torque precisely, thereby enabling a non-force-sensing effect. Next, a variable admittance control framework is formulated for lower limb exoskeleton robots to ensure superior compliance. This framework comprises two essential components. First, a newly designed adaptive fixed-time sliding mode controller based on a class$\kappa _{\infty } $function for the inner loop, which operates without requiring prior knowledge of the upper bound of lumped perturbations and guarantees precise gait trajectory-tracking performance. Second, a variable-parameter admittance model for the outer loop, which utilizes an exponential function to dynamically adjust the admittance parameters, thereby achieving a balance between the exoskeleton’s compliance and gait-correction efficacy. Finally, both simulation and experimental results are presented and analyzed to validate the effectiveness and superiority of the proposed non-force-sensing variable admittance control approach. Specifically, simulation results demonstrate that the root-mean-square (RMS) values of the inner-loop tracking errors for the proposed method are reduced by 26.1% and 20.4% at the hip and knee joints, respectively, compared with the top-performing benchmark algorithm. Meanwhile, the precision of the outer-loop observation is improved by 21% and 18% at these joints. Experimental validation further shows reductions of 14.2% and 20.1% in the RMS inner-loop tracking errors at the hip and knee joints, respectively, versus this benchmark algorithm.Note to Practitioners—Motivated by the problem of how to realize effective control of lower limb exoskeleton robots to provide the wearers with appropriate comfort and gait-correction effect, this paper formulates an adaptive variable admittance control framework. In the inner loop of this framework, a novel gait trajectory-tracking controller using adaptive sliding mode is designed to ensure high tracking accuracy. In the outer loop, a new adaptive observer is designed to estimate the human-robot interaction torque, and an adaptive admittance model is formulated to provide appropriate compliance for the robot. The proposed strategies are experimentally validated and can be utilized in real-word applications. The work of this paper has reference significance for the development of lower limb exoskeleton technologies.

Path following is a fundamental capability for autonomous surface vessels (ASVs). A typical path-following algorithm comprises two main modules: guidance and control. In the guidance domain, the vector field (VF) approach has been widely adopted due to its demonstrated superiority over alternative guidance strategies. Consequently, it has been successfully applied to a range of unmanned systems, including ASVs, airships, and quadrotors. However, most existing VF guidance laws are constrained to simple geometric paths, such as straight lines and circular orbits. Therefore, their practical applicability is limited in real-world engineering scenarios. To address this limitation, this paper introduces a novel, continuously differentiable VF capable of handling general curved paths, thereby significantly broadening the application scope of the VF methodology. In terms of control, a major challenge for ASVs lies in achieving long-endurance operation while maintaining robustness against uncertainties. The finite-time uncertainty observer (FTUO)-based adaptive dynamic programming (ADP) approach has been shown to be effective in addressing this challenge. Nevertheless, common FTUO-based ADP methods often suffer from drawbacks such as system chattering, observer peaking, and asymptotic convergence. In response to this situation, this paper proposes a modified FTUO-based ADP method for ASVs. This approach eliminates undesirable effects, i.e., chattering and peaking, and further saves control energy. Both simulation and experimental results verify the effectiveness and advantages of the proposed path-following algorithm.

This article introduces an innovative approach to simultaneously estimate time-domain and spatiotemporal faults in nonlinear distributed parameter systems (NDPSs)nonlinear distributed parameter systems (NDPSs) under external disturbances. First, the establishment of an iterative learning observer that accounts for both temporal and spatial changes is presented. Next, a fault estimation law is devised utilizing a distinct full iterative learning (FIL)full iterative learning (FIL) technique, facilitating rapid and precise estimation of fault signals while mitigating the impact of external disturbances. Furthermore, the adoption of the $\lambda $ -norm method aids in simplifying the determination of convergence conditions and gain matrix calculations. Lastly, comprehensive simulation results validate the efficacy of the developed approach, underscoring its adeptness in efficiently and precisely estimating faults across both time and spatiotemporal domains.

Although existing 3D super-resolution methods for magnetic resonance imaging (MRI) volumetric data can provide better visual images than some traditional 2D methods, they should face challenge of increasing network's parameters and computing cost for getting higher reconstruction accuracy. To address this issue, a lightweight 3D multi scale distillation volumetric Transformer, named Transformer-based dual-attention feature distillation (TDAFD) network, is proposed for 3D MRI by utilizing 3D information hiding in images sufficiently. Our TDAFD network contains several proposed dual-attention feature distillation (DAFD) modules and two designed recursive volumetric Transformers (RVT). Concretely, the proposed DAFD module contains a multi-scale feature distillation (MSFD) block for extracting global features under different scales and a feature enhancement dual attention block (FEDAB) for concentrating on the key features better. In addition, our RVT develops 2D Transformer to 3D and save network's parameters via recursion operations for capturing long-term dependencies in volumetric images effectively. Therefore, our proposed TDAFD network can not only extract deeper features via multi scale feature distillation and Transformer, but also realize the balance of performances and network's parameters. Extensive experiments illustrate that our proposed method achieves superior reconstruction performances than some popular 3D MRI SR methods, and saves number of weights and FLOPs.

Underwater imagery often suffers from light attenuation and color distortion, resulting in images with low contrast and blurriness. Enhancing these images is crucial yet challenging due to the complex degradation and noise inherent in underwater environments. In this study, we introduce a novel diffusion model, termed Underwater Image Enhancement(UIE) Diffusion, which leverages a global feature prior for effective underwater image enhancement. To our knowledge, this is the inaugural application of a diffusion model to the task of underwater image enhancement, setting a new benchmark in performance. Our approach begins with the introduction of a global feature prior to augment the diffusion model, mitigating the impact of noise and distortion during training. We then incorporate an underwater image degradation model to facilitate the learning of mappings between high-quality and degraded underwater images. To address over-enhancement caused by high-frequency components, we employ scaling factors to modulate the influence of frequency features during diffusion. Additionally, we enhance the model's stability during inference by integrating a backward diffusion process into its training. Comprehensive evaluations on multiple public datasets demonstrate that UIE Diffusion surpasses existing state-of-the-art methods in both subjective outcomes and objective assessments.

In this article, a distributed fault estimation (DFE) approach for switched interconnected nonlinear systems (SINSs) with time delays and external disturbances is proposed using a novel segmented iterative learning scheme (SILS). First, through the utilization of interrelated information among subsystems, a distributed iterative learning observer is developed to enhance the accuracy of fault estimation results, which can realize the fault estimation of all subsystems under time delays and external disturbances. Simultaneously, to facilitate rapid fault information tracking and significantly reduce sensitivity to interference, a new SILS-based fault estimation law is constructed by combining the idea of segmented design with the method of variable gain. Then, an assessment of the convergence of the established fault estimation methodology is conducted, and the configurations of observer gain matrices and iterative learning gain matrices are duly accomplished. Finally, simulation results are showcased to demonstrate the superiority and feasibility of the developed fault estimation approach.

For the input-delayed stochastic systems with the states and input quantization, the adaptive stabilization problem is investigated in this article. The whole control scheme design process can be divided into three steps. First, the traditional adaptive neural control scheme is developed for the controlled system. Next, the effective control scheme is proposed for the system with the quantized states. Finally, the adaptive neural control method is developed for the considered system with the states and input quantization. The radial basis function neural network (RBFNN) is applied to approximate the unknown terms online, and the Pade approximation method is introduced to deal with the input-delayed problems. The adaptive neural fault control strategy is presented to address sensor faults and the discontinuity due to the quantized states. Under the constructed controllers, all the closed-loop signals remain semi-globally uniformly ultimately bounded (SGUUB) in mean square. The effectiveness and superiority of the presented control schemes are verified by some simulation results.

Sample clustering techniques play a crucial role in the data‐driven state evaluation of electromechanical equipment, and selecting an appropriate similarity measurement method for sample sets helps improve the clustering performance. The Jaccard coefficient is a commonly employed indicator of similarity for scalar set‐type samples. In this paper, we propose an incremental clustering algorithm for matrix‐type samples by defining an improved Jaccard coefficient. First, a new binary relation is formulated to derive a relationship matrix between samples. Second, an undirected graph is given by using the relationship matrix, and an improved pruning operation is provided to simplify the graph by eliminating redundant edges. Then, a new relationship matrix is generated according to the modified graph, which enables the calculation of the improved Jaccard coefficient. By using the improved Jaccard coefficient, the improved incremental clustering algorithm updates cluster centers by selecting a particular sample to maximize the sum of similarities between the selected sample and other samples within the same cluster. Finally, the effectiveness of the proposed incremental clustering algorithm is demonstrated in fault diagnosis and remaining useful life estimation application scenarios, respectively. The experimental results indicate that the improved algorithm outperforms traditional clustering methods.

Abstract In this paper, a learning-based nearly optimal control framework with fault-tolerant capability is designed to tackle the tracking control problem of a flexible-link manipulator in the presence of actuator fault and model uncertainties. Initially, the optimal control law is obtained by adopting the dynamic programming and a critic structure as the solution of Hamilton–Jacobi–Bellman equation for the nominal model. Then, by implementing an integral sliding mode control, the robustness against actuator fault and model uncertainty is guaranteed. The adaptive laws are constructed based on radial basis functions neural networks to estimate the upper bound of uncertainty and the actuator bias fault, satisfying both optimal performance and chattering reduction of the sliding surface. Furthermore, the actuator effectiveness loss is handled. The stability of the closed-loop system is analytically proven, and the performance of the proposed framework is investigated against several practical operating conditions. This incorporates the fidelity assessment of tracking precision and trackability of control signal using performance indices such as the integral absolute error and root-mean-square error. The results of extensive simulation studies confirm the effectiveness and robustness of the proposed control framework.

Accurate fault detection and localization are essential for single-phase three-level (SPTL) rectifier systems with high reliability requirements. However, power switch faults, grid-side current sensor (CS) faults, and DC-link voltage sensor (VS) faults can all contribute to distorted output in the rectifier system, posing challenges for existing diagnostic methods tailored for single-type faults, as they struggle to distinguish between these various faults. Therefore, this study proposes a comprehensive diagnosis technology for open-circuit (OC) faults, CS faults, and VS faults of SPTL rectifiers on the basis of a reduced-order observer. To achieve this, the method begins by expanding and transforming the state equation of the rectifier with faults, ensuring complete decoupling of the OC fault vector from the initial system states and sensor faults. Subsequently, an assessment of the initial system state, CS faults, and VS faults is achieved via the design of a reduced-order observer. Using these estimation results, fault detection variable and its adaptive thresholds is designed, along with fault-distinguishing variables to differentiate between sensor faults and OC faults. Simultaneously, sensor fault identification method and OC fault location method are introduced. Finally, the validity and resilience of the comprehensive diagnostic approach are confirmed through hardware-in-the-loop (HIL) test results under diverse scenarios.

In this article, we investigate the optimal control problem for semicar active suspension systems (SCASSs). First, we model the SCASSs by Newtonian dynamics as well as considering the uncertainties and nonlinear dynamics of the actuator. Second, in order to solve the complexity brought by uncertainties, we apply the Takagi–Sugeno (T-S) fuzzy approach to transform the SCASSs as multilinear systems, as well as solving the optimal control problem as a zero-sum problem to find the solution of Nash-equilibrium. Third, we construct a novel self-learning method based on the reinforcement learning framework, and propose two algorithms to solve the fuzzy game algebraic Riccati equation. Especially, in the second algorithm, without using any model information of the SCASSs, we only use the state and input information in control design by a self-learning manner removing the traditional dependence problem, which is more preferable for practical applications. Finally, we give a simulation result of the SCASSs to demonstrate the effectiveness and practicability for the designed self-learning algorithms.

This paper investigates the issue of active fault tolerant control (AFTC) for manipulator with actuator faults. First, a higher-order sliding mode (HOSM) observer for fault diagnosis is constructed to estimate unmeasured states and compensate for the actuator faults simultaneously. Then, a feedback fault tolerant control (FTC) strategy is proposed to guarantee system's convergence in finite time and to reduce chattering phenomena based on the nonsingular fast terminal sliding mode (NFTSM) and barrier function-based adaptive super twisting (BFAST) algorithm. The finite time stability of the proposed FTC scheme is demonstrated by Lyapunov theory. Finally, simulations are performed to demonstrate the effectiveness of the proposed control method.

Autonomous mobile robots have been applied in many industries, but fast and robust trajectory tracking control remains a major challenge. This paper investigates a predefined-time trajectory tracking problem of a Mecanum-wheeled omnidirectional mobile robot (MWOMR) under the conditions of parameter uncertainties and external disturbances. First, a novel predefined-time stable system with adjustable convergence rate is proposed. Second, a predefined-time sliding mode control scheme is developed, which has better convergence performance and smaller control input. Based on Lyapunov stability theory, the effectiveness of the scheme is proven. The numerical simulation results show that the compared with traditional predefined-time control schemes, the proposed scheme has the advantages of a shorter convergence time and smaller control input.

Permanent magnet linear synchronous motor (PMLSM) is widely used in robot systems. Nonsingular terminal sliding mode control based on robust compensator was proposed to improve position tracking ability and anti-interference ability of PMLSM to improve the system control performance. Firstly, the sliding mode controller was designed by using nonsingular terminal sliding mode, which ensures that the system has good control accuracy and faster convergence speed. Then, the super twisting algorithm was used to construct the auxiliary sliding mode function, and deigned the robust compensator. The controller handles the uncertainty of the system by smoothing the control input, and ensures that the system can reach a stable state in a limited time. Finally, the model is built and verified by simulation. The comparison results showed that the controller has high tracking performance and strong anti-interference ability, which can significantly reduce the position tracking error of the system and weaken the chattering phenomenon of sliding mode.

This article concentrates on adaptive tracking control of strict-feedback uncertain nonlinear systems with an event-based learning scheme. A novel neural network (NN) learning law is proposed to design the adaptive control scheme. The NN weights information driven by the prediction-error-based control process is intermittently transmitted in the event-triggered context to the NN learning law mainly for signal tracking. The online stored sampled data of NN driven by the tracking error are utilized in the event context to update the learning law. With the adaptive control and NN learning law updated via the event-triggered communication, the improvements of NN learning capability, tracking performance, and system computing resource saving are guaranteed. In addition, it is proved that the minimum time interval for triggering errors of the two types of events is bounded and the Zeno behavior is strictly excluded. Finally, simulation results illustrate the effectiveness and good performance of the proposed control method.

This article investigates the co-design problem of adaptive event-triggered schemes (AETSs) and asynchronous fault detection filter (AFDF) for nonhomogeneous higher-level Markov jump systems, involving the hidden Markov model (HMM), higher-level Markov chain (MC), and conic-type nonlinearities. The transformation of the system transition probability can be reflected by the designed higher-level MC. An HMM with another conditional transition probability is applied to detect higher-level Markov processes and make the system be more practical. In order to balance the utilization of network resources and system performance, a novel AETS is proposed and used in the construction of the AFDF. By the Lyapunov theory, sufficient conditions are given to ensure the existences of the AETS and AFDF. It is not only an appropriate tradeoff between the utilization of network resources and system performance, but also reduces the conservatism. Finally, a numerical example is given to detect the faults effectively by the co-designed AFDF.

This article investigates the robust adaptive learning control for space robots with target capturing. Based on the momentum conservation theory, the impact dynamics is constructed to derive the relationship of generalized velocity in the pre-impact and post-impact phase. Considering the nonlinear dynamics with contact impact, the robust control using nonsingular terminal sliding mode (NTSM) and fast NTSM is designed to achieve the fast realization of the desired states. Furthermore, for the unknown dynamics of the combination system after capturing a target, the adaptive learning control is developed based on neural network and disturbance observer. Through the serial-parallel estimation model, the prediction error is constructed for the update of adaptive law. The system signals involved in the Lyapunov function are proved to be bounded and the sliding mode surface converges in finite time. Simulation studies present the desired tracking and learning performance.

The double inverted pendulum (DIP) system is a benchmark underactuated mechanical system. The control problem of the DIP system is challenging since it not only has high nonlinearity, but also has underactuation degree equal to two. In this article, a switched energy-based swing-up and neural network (NN) controller is proposed to solve the swing-up and tracking control problem of the DIP system when the desired position is time varying. The implementation of the proposed controller can be divided into three steps. In step one, the energy-based control method is adopted to drive the first pendulum from the downward position to the neighborhood of the upright position. In step two, the sliding mode control method is adopted to stabilize the first pendulum. Meanwhile, a method combining energy-based control and “equivalent cart” is adopted to swing up the second pendulum. In step three, on the basis of approximate nonlinear output regulation theory, the NN controller is used for achieving satisfactory position tracking performance. Finally, the effectiveness of our design is verified by experimental results.

In this article, the problem of the asynchronous fault detection (FD) observer design is discussed for 2-D Markov jump systems (MJSs) expressed by a Roesser model. In general, the FD observer cannot work synchronously with the system, that is, the mode of the observer varies with the mode of the system in line with some conditional transitional probabilities. For dealing with this difficult point, a hidden Markov model (HMM) is employed. Then, combining the$H_{\infty }$attenuation index and$H_{\_{}}$increscent index, a multiobjective solution to the FD problem is formed. In terms of linear matrix inequality technology, sufficient conditions are gained to guarantee the existence of the asynchronous FD. Simultaneously, an asynchronous FD algorithm is generated to acquire the optimal performance indices. Finally, a numerical example concerned with the Darboux equation is demonstrated to exhibit the soundness of the developed approach.

This article is concerned with the robust adaptive fault-tolerant control (FTC) circuit designs for a class of continuous-time disturbed systems. A circuit realization method is investigated to convert the robust adaptive FTC control schemes into analog control circuits. An adaptive compensation control scheme against state-dependent and partially bounded actuator faults and disturbances is first developed to demonstrate the approach clearly, then its equivalent control circuits are implemented by using the circuit theory. Compared with simulation results achieved by MATLAB and professional circuit simulation software, the effectiveness of the proposed robust adaptive FTC circuits is validated by a rocket fairing system and a Chua's circuit system.

This article explores a novel adaptive optimal control strategy for a class of sophisticated discrete-time nonlinear Markov jump systems (DTNMJSs) via Takagi–Sugeno fuzzy models and reinforcement learning (RL) techniques. First, the original nonlinear system model is represented by fuzzy approximation, while the relevant optimal control problem is equivalent to designing fuzzy controllers for linear fuzzy systems with Markov jumping parameters. Subsequently, we derive the fuzzy coupled algebraic Riccati equations for the fuzzy-based discrete-time linear Markov jump systems by using Hamiltonian–Bellman methods. Following this, an online fuzzy optimization algorithm for DTNMJSs as well as the associated equivalence proof is given. Then, a fully model-free off-policy fuzzy RL algorithm is derived with proved convergence for the DTNMJSs without using the information of system dynamics and transition probability. Finally, two simulation examples, respectively, related to the single-link robotic arm and the half-car active suspension are given to verify the effectiveness and good performance of the proposed approach.

Based on the interval type-2 fuzzy (IT2F) approach, this article investigates the fault detection filter design problem for a class of nonhomogeneous higher level Markov jump systems with uncertain transition probabilities. Considering that the mode information of the system cannot be obtained synchronously by the filter, the hidden Markov model can be seen as a detector to handle this asynchronous problem, and the parameter uncertainty can be processed by the IT2F approach with the lower and upper membership functions. Then, the asynchronous IT2F filter is designed to deal with the fault detection problem. Furthermore, the Gaussian transition probability density function is introduced to describe the uncertainty transition probabilities of the system and the filter. Based on the Lyapunov theory, the existence of the designed asynchronous IT2F filter and the dissipativity of the filter error system can be well ensured. In this article, the simulation study on a quarter-car suspension system verifies that the designed asynchronous IT2F filter can detect faults without error alarms.

In this article, we consider the distributed fault-tolerant resilient consensus problem for heterogeneous multiagent systems (MASs) under both physical failures and network denial-of-service (DoS) attacks. Different from the existing consensus results, the dynamic model of the leader is unknown for all followers in this article. To learn this unknown dynamic model under the influence of DoS attacks, a distributed resilient learning algorithm is proposed by using the idea of data-driven. Based on the learned dynamic model of the leader, a distributed resilient estimator is designed for each agent to estimate the states of the leader. Then, a new adaptive fault-tolerant resilient controller is designed to resist the effect of physical failures and network DoS attacks. Moreover, it is shown that the consensus can be achieved with the proposed learning-based fault-tolerant resilient control method. Finally, a simulation example is provided to show the effectiveness of the proposed method.

Prognosis of discrete component with intermittent fault in hybrid systems is challenging since the component has only two states (i.e., ON and OFF) and no associated physical parameter in the model can quantify the degradation. This article aims to solve the discrete component prognosis problem under the model-based paradigm. First, the fault detection and isolation module help find the possible faulty discrete components. Based on the isolated possible faulty discrete components, Levy flight biogeography-based optimization is proposed to identify the faulty discrete component states, as well as the fault appearing and fault disappearing instants. Second, a Weibull function-based degradation model which can capture the duration evolution of intermittent fault of discrete component in observation window (OW) is developed using coordinate reconstruction approach, and the degradation model coefficients can be calculated from the fault identification results. After that, the concept of failure threshold for faulty discrete component is defined based on the ratio of fault duration to OW, which enables the prognosis of intermittent fault in discrete component. Finally, the proposed methodologies are validated by experiment results.Note to Practitioners—This article is motivated by the intermittent fault prognosis problem of discrete components (e.g., relays and hydraulic valves) in hybrid systems. Existing fault prognosis researches do not consider discrete component which is an important part of hybrid systems. For the intermittent fault prognosis of discrete component, the observation window (OW) concept and coordinate reconstruction (CR) method are proposed to establish the degradation model, and the ratio of fault duration to OW is used to define the failure threshold of discrete component. To show the effectiveness of the proposed methods, an application on a hybrid circuit system is considered. It is noted that the degradation pattern (e.g., increase of frequency or duration of intermittent fault) of discrete components may vary in different systems, while the degradation process can be quantified by the OW and CR methods developed in this article, which enables the prognosis of intermittent fault in discrete component for various hybrid industrial systems. The proposed approach can be applied to industrial hybrid systems if the following conditions are satisfied: 1) the hybrid bond graph model of the monitored system can be established, based on which the fault detection and isolation can be implemented and 2) the monitored system contains multiple discrete components suffering from intermittent faults whose appearing and disappearing instants can be identified by certain method.

The tracking performance of linear motor (LM) positioners is subject to payload uncertainty and external time-varying disturbances. Conventional robust controllers typically employ a high control gain that is substantially greater than the known a priori upper bound of the disturbance to ensure tracking error convergence. The main disadvantage of those controllers lies in that when the disturbance decreases, the control input is often overly generated, which then results in undesired control chattering effect or even actuator saturation. To overcome this problem, this article develops a robust tracking controller based on barrier function adaptive sliding mode (BFASM) for the LM positioners. The main benefits of BFASM are twofold: first, the controller is designed without the need for any disturbance information; second, its control gain is adaptively adjusted in terms of the amplitude of disturbance and, thus, leads to decreased control input when the disturbance becomes small. Furthermore, a modified barrier function (MBF) is proposed for applications with actuator saturation. It is proved that both the BFASM and MBF-based controllers can ensure the convergence of the tracking error into a prespecified neighborhood of zero in finite time. Experimental results on a real LM positioner demonstrate the superior properties of the developed controllers in comparison with two existing robust control schemes.

Much recently, an internal model approach from output regulation theory has been adopted to solve the speed tracking control problem of the permanent magnet synchronous motor (PMSM) system on simulation level. A striking advantage of this approach is that it can achieve multiple goals including trajectory tracking, disturbance rejection, and robustness simultaneously. This article further studies a position tracking control problem of the PMSM system under nonlinear load torque disturbance. A nonlinear internal model based output feedback controller is proposed, which can achieve exact position tracking and allow certain parameter uncertainties. Besides simulation results, a real-time experimental setup is built and experimental results are provided to illustrate the effectiveness of the proposed controller. It is worth mentioning that the proposed controller can lead to a high precision position tracking performance under nonlinear load torque disturbance.

This paper addresses the prognosis problem for hybrid systems with intermittent faults and uncertain parameters. First, a diagnostic hybrid bond graph in linear fractional form is used to model the uncertain hybrid system to generate the mode-dependent adaptive thresholds for fault detection purpose. Then, a global combinative fault signature matrix integrating independent and dependent augmented global analytical redundancy relations is proposed to improve the system fault isolability under the multiple-fault condition. After the possible fault set is isolated, an adaptive reinforcement unscented Kalman filter is introduced to identify the intermittent fault magnitude, where an auxiliary indicator is used to capture the fault appearing and disappearing time steps. To describe the degradation trend of the intermittent fault, a dynamic model with a mode-dependent degradation coefficient is used. Taking the variation of the degradation coefficient into account, an event-based sequential prognosis method is proposed, where the prognoser is only reactivated if the discrete event representing mode change is observed and its associated conditions are satisfied. Finally, the key concept of the proposed method is verified by experimental studies.

This paper deals with bond-graph-based fault detection, isolation, and estimation, which is applied to a nonlinear electromechanical system, in the presence of parametric faults and nonparametric faults. The concept of extended analytical redundancy relations (EARRs) is developed where the nonparametric faults of both multiplicative and additive natures can be considered and distinguished. The integration of dependent EARR with independent EARR leads to a more efficient fault isolation where the number of potential faults could be decreased. For the purpose of fault estimation, a new pitch adjustment biogeography-based optimization is developed where the pitch adjustment in harmony search is embedded in a biogeography-based optimization mutation stage to enhance the search ability of the algorithm. The proposed methodologies are validated by simulation and experiment results.

In this paper, a robust control scheme (RCS) for Steer-by-Wire (SbW) systems with partially known dynamics is proposed. It is shown that an SbW system can be represented by a nominal model and an unknown portion. A nominal feedback controller can then be used to stabilize the nominal model and a sliding mode compensator (SMC) is designed to remove the effects of both the unknown system dynamics and uncertain road conditions on the steering performance. For practical consideration, robust exact differentiator (RED) technique is utilized to estimate the derivatives of the position signals for controller design. It is further shown that the designed RCS is able to guarantee a robust steering performance against system and road uncertainties. The comparative experimental studies are given to verify the excellent performance of the proposed RCS for SbW systems.