ISA transactions最新文献

This paper considers the event-triggered adaptive fault-tolerant control (FTC) problem for a class of stochastic nonlinear systems suffering from finite number of actuator failures and abrupt system external failure. Unlike existing event-triggered mechanisms (ETMs), this paper proposes an improved switching threshold mechanism (STM) that effectively addresses the potential system security hazards caused by large signal impulses when both the magnitude size of the controller and its rate of change are too large, while also saving energy consumption. Especially, when the occurrence of both actuator failure and system external failure may lead to over-change rate of the controller, by using the multi-dimensional Taylor network (MTN) approximation technique, the adaptive fault-tolerant control scheme designed based on the improved STM not only has lower resource consumption, but also indirectly improves the control performance of the system by ensuring the system security operation. Not only does it ensure that all signals of the closed-loop system are bounded in probability and the tracking error converges through the proposed control scheme. The feasibility and superiority of the developed scheme is well shown by dynamic model simulations.

Microgrids play an important role in stabilizing the electrical grid and they are the best route to develop green and sustainable energy. Since microgrids are expanding rapidly, it is necessary to consider the related control issues including power quality, bidirectional power flow, voltage and frequency control, and stability analysis. One of the main measurement challenges is the communication delay. It means the delay in sending data from the sensor or measuring unit to the processing unit. The communication delay gets more important when the microgrid is widespread and complex. In this paper, a novel soft switching voltage control system is proposed to solve the voltage control problem of a widespread micro-grid while there are time-varying communication delays. The novel soft switching method is based on a static output feedback controller and deep neural networks. Another novelty of this paper is considering the 33-bus microgrid as a large-scale system that helps develop local and central controllers. The simulation's results show the effectiveness of a soft switching controller in the presence of dynamic time-varying communication delays. It means that while encountering static communication delays, the static output feedback controller without a soft switching method is sufficient in a large-scale microgrid.

Approximation-free control effectively addresses uncertainty and disturbances without relying on approximation techniques such as fuzzy logic systems (FLS) and neural networks (NNs). However, singularity problems-where signals exceed preset boundaries under dynamic operating conditions-remain a challenge. This paper proposes an improved approximation-free control (I-AFC) method for the multi-agent system, which introduces a novel singularity compensator, providing a low-complexity design with exceptional adaptability while reducing the risk of singularity issues under changing working conditions (random initial values, system parameter variations, and changes in topology graph and followers' dynamics). Furthermore, theoretical analysis guides parameter selection by demonstrating the method's favorable convergence rate and appropriate control gain. Simulation results validate the approach.

This paper explores a novel challenge regarding bidirectional Automated Guided Vehicles (AGVs): supervisory control amidst potential sensor faults. The proposed approach uses an event-based control architecture, guided by Supervisory Control Theory (SCT), to achieve non-blocking routing of AGVs. Unlike most routing approaches assuming full event observability, this paper investigates scenarios where events might become unobservable due to sensor faults or disturbances, which may affect the supervisor efficiency. The paper addresses two new key issues regarding AGV systems. First, it examines the diagnosis problem of automated transport systems from a discrete-event systems perspective. Secondly, it presents a control architecture enhanced with a diagnostic layer to improve fault tolerance. The theory of automata and languages is used to address control and diagnostic issues. The proposed methodology offers a systematic approach to design specification and diagnostic automata for routes shared by AGVs. The new specification automata integrate information from the diagnostic automata via synchronized transition guards, guaranteeing the synthesis of a robust supervisor that avoids deadlocks even when observability is compromised. The efficiency of the proposed architecture is examined and showcased by simulation. In addition, a modelling framework based on stochastic timed automata is introduced, applying model checking to assess system reliability which is redefined as the probability of deadlock avoidance.

An independent metering system (IMS) realizes the decoupling of the meter-in and meter-out orifices. The energy efficiency of the hydraulic system can be effectively improved by switching between different operational modes. However, the tracking accuracy of the IMS mode-switching system is difficult to ensure, which can easily lead to instability in the hydraulic system. In view of this, this paper proposes a mode switching controller based on an IMS. First, the K-filters theory is innovatively applied to the mode switching hydraulic system to estimate unmeasurable state variables of a system accurately. In addition, fuzzy logic systems (FLSs) are applied to handle the unmodeled errors and disturbances in the mechanical system dynamics model and hydraulic system. Further, aiming at the stability and trajectory tracking problems in the mode switching control (MSC) process of an IMS, the average dwell time (ADT) stability analysis method is applied to the mode switching hydraulic system to construct a set of switching rules to make the closed-loop switching system stable. Moreover, based on the prescribed performance control (PPC) theory, all state errors of a hydraulic system are guaranteed to reach the performance function constraint boundary at the specified time. Also, a dynamic surface control (DSC) technique is used to avoid the explosion of computational complexity caused by iterative differentiation inherent in the traditional backstepping method. Finally, the feasibility and effectiveness of the proposed method are verified by simulation, and experiments are carried out on mini-excavators. The results show that the designed controller can not only ensure the tracking accuracy, but also effectively suppress the instability of the hydraulic system caused by MSC.

In recent years, exoskeleton robots have attracted great interest from researchers in the area of robotics due to their ability to assist human functionality improvement. A wearable lower limb exoskeleton is aimed at supporting the limb functionality rehabilitation process and to assist physical therapists. Development of a stable and robust control system for multi-joint rehabilitation robots is a challenging task due to their non-linear dynamic systems. This paper presents the development of a Swarm-Initialized Adaptive (SIA) based controller, which is a combination of a swarm-based intelligence, named Swarm Beetle Antenna Searching (SBAS), and an adaptive Lyapunov-based controller. The SBAS initializes the parameters of SIA to efficiently improve the performance of the control system and then these controller parameters are updated by an adaptive controller. The control system is validated in a lower limb exoskeleton prototype with four degrees of freedom, using a healthy human subject for sit-to-stand and walking motions. The experimental results show the applicability of the proposed method and demonstrate that our approach obtained efficient control performance in terms of steady-state error and robustness and can be used for a lower limb exoskeleton to improve human mobility.

This paper addresses the critical challenge of interpretability in machine learning methods for machine fault diagnosis by introducing a novel ad hoc interpretable neural network structure called Sparse Temporal Logic Network (STLN). STLN conceptualizes network neurons as logical propositions and constructs formal connections between them using specified logical operators, which can be articulated and understood as a formal language called Weighted Signal Temporal Logic. The network includes a basic word network using wavelet kernels to extract intelligible features, a transformer encoder with sparse and structured neural attention to locate informative signal segments relevant to decision-making, and a logic network to synthesize a coherent language for fault explanation. STLN retains the advantageous properties of traditional neural networks while facilitating formal interpretation through temporal logic descriptions. Empirical validation on experimental datasets shows that STLN not only performs robustly in fault diagnosis tasks, but also provides interpretable explanations of the decision-making process, thus enabling interpretable fault diagnosis.

This paper investigates the self-triggered control for stabilizing an n-dimensional linear time-invariant system under communication constraints, including finite bit rates and transmission delay. The concerned system is further perturbed by bounded process noise. To resolve these issues, a self-triggering strategy is proposed. Specifically the proposed self-triggering strategy selects the next sampling time from a set of pre-designed time instants based on the sampled system states. By fully exploiting the encoded information of receive time instants of feedback packets, we can achieve the desired input-to-state stability (ISS) at a lower bit rate than that of periodic sampling. Moreover, the proposed self-triggering strategy is free of the burdens of continuously monitoring the system state compared with event-triggered sampling strategies. The efficiency of the proposed self-triggering strategy is further confirmed by simulations.

Existing cross-domain mechanical fault diagnosis methods primarily achieve feature alignment by directly optimizing interdomain and category distances. However, this approach can be computationally expensive in multi-target scenarios or fail due to conflicting objectives, leading to decreased diagnostic performance. To avoid these issues, this paper introduces a novel method called domain feature disentanglement. The key to the proposed method lies in computing domain features and embedding domain similarity into neural networks to assist in extracting cross-domain invariant features. Specifically, the neural network architecture designed based on information theory can disentangle key features from multiple entangled latent variables. It employs the concept of contrastive learning to extract domain-relevant information from each data point and uses the Wasserstein distance to determine the similarity relationships across all domains. By informing the neural network of domain similarity relationships, it learns how to extract cross-domain invariant features through adversarial learning Eight multi-target domain adaptation tasks were set up on two public datasets, and the proposed method achieved an average diagnostic accuracy of 96.82%, surpassing six other advanced domain adaptation methods, demonstrating its superiority.

This article addresses the secure synchronization problem for complex dynamical networks (CDNs) with observer-based event-triggered communication strategy (ETCS) under multi-channel denial-of-service attacks (MCDSAs). Due to external environmental interference, the observers are designed to accurately estimate the state of the network systems. Meanwhile, the impact of cyber attacks on system security is considered. Differing from other studies, MCDSAs in this paper are considered, which present the interference of malicious attacks on the communication topology, observers and controllers. Based on the characteristics of cyber attacks, the model for CDNs with nonperiodic switching topology is established, and the observer-based intermittent control is proposed to achieve synchronization of CDNs. To decrease the network communication frequency and reduce the risk of cyber attacks, a new ETCS depending on estimate states is designed. Then, using Lyapunov stability theory, several synchronization conditions depending on the status parameters of MCDSAs are deduced, which may estimate the allowable ranges for the duration and frequency of MCDSAs. From these theoretical results, it can be observed that the destructive power of network attacks increases exponentially with the increase of attack duration. Finally, from simulation experiments, the availability of the designed controller and communication strategy is verified, which can ensure the synchronization for CDNs with MCDSAs.