Despite being in use since the 1960’s, clock distribution networks continue to be important, mainly since the digitalization of numerous electronic tasks, which demands precise time measures for synchronizing internal and external processes in computational and instrumentation applications. Designing clock distribution networks requires determination/estimation of the appropriate topologies and parameters

This paper addresses the optimal vaccination and treatment control problems for regional approximate controllability of a new spatiotemporal epidemic model that is developed by afterwards adding at the basic susceptible-infected-recovered (SIR) epidemic system with the Caputo time-fractional derivative of order α∈(0,1] and the diffusion term in each compartment. The obtained results can be used by

Firefly algorithm (FA) is a meta-heuristic optimization algorithm inspired by nature. Due to its superior performance, it has been widely used in real life. However, it also has some shortcomings in some optimization cases, such as low solution accuracy and slow solution speed. Therefore, in this paper, distributed parallel firefly algorithm (DPFA) with four communication strategies is presented to

The main purpose of this paper is design and implementation of a new linear observer for an attitude and heading reference system (AHRS), which includes three-axis accelerometers, gyroscopes, and magnetometers in the presence of sensors and modelling uncertainties. Since the increase of errors over time is the main difficulty of low-cost micro electro mechanical systems (MEMS) sensors producing instable

In the contact type capacitive liquid level sensors, when an electrode with the insulating film is immersed in polar/ionic medium, it shows constant phase behavior at metal–insulator interface due to the formation of the double layer. This double layer effect is frequently modeled by a pure capacitor but its capacitance value depends on signal frequency. Therefore, when such a sensor is excited by

The reliability assessment of train control and management system (TCMS) is essential for the condition monitoring of high-speed train. Different from other general complex systems, the TCMS has the characteristics of multi-system unit, strong coupling and multiple factors. Considering the special system operating environment and high safety requirements of high-speed train. In this paper, for the

The design of an energy-efficient tracking framework is a well-investigated issue and a prominent sensor network application. The current research state shows a clear scope for developing algorithms that can work, accompanying both energy efficiency and accuracy. The prediction-based algorithms can save network energy by carefully selecting suitable nodes for continuous target tracking. However, the

This paper proposes a cooperative distributed model predictive control (DMPC) to control the constrained interconnected nonlinear large-scale systems. The main contribution of this approach is its proposed novel cooperative optimization that improves the global cost function of any subsystem. Each subsystem calculates its optimal control by solving the corresponding global cost function. For each subsystem

The paper centers on the H∞ finite-time (FT) composite anti-disturbance switching control (ADSC) issue for switched systems (SSs) suffered from multiple disturbances, the unavailable modeled disturbance and the available un-modeled disturbance. The aim is to arrive at the multiple disturbance suppression in FT. A switching estimator is firstly constructed to observe the modeled disturbance combined

False data injection (FDI) attack is a malicious kind of cyber attack that targets state estimators of power systems. In this paper, a dynamic Bayesian game-theoretic approach is proposed to analyze FDI attacks with incomplete information. In this approach, players’ payoffs are identified according to a proposed bi-level optimization model, and the prior belief of the attacker’s type is constantly

This paper develops a neural-network-based model reference adaptive control (MRAC) scheme for a rotorcraft in the presence of input saturation. Such a control scheme provides acceptable tracking performance for the rotorcraft in a wide range of flight conditions. Combined with hyperbolic tangent functions, the MRAC scheme is capable of tracking the reference signals without violating input constraints

Deep learning has gotten much attention in industrial field, many fault detection methods based on deep learning have been developed for nonlinear industrial processes. However, most of them do not take the quality-related faults into account. In order to extract the latent variables which can represent the separated quality-related and unrelated information, this paper proposes a novel deep VIB-VAE

Bilateral controller design for the teleoperation system is studied in this paper based on a motion prediction approach. To compensate the known long time-varying delays, novel predictors are presented to reconstruct the positions and velocities of robots on both sides through using the delayed measurements. The proposed predictors consist of several sub-predictors in a cascade structure, each of which

This paper investigates the neural adaptive control problem for air-breathing hypersonic vehicles. For the velocity subsystem, a radial basis function neural network (RBFNN)-based adaptive controller is first designed, which employs the auxiliary variable to compensate for the saturation nonlinearity of scramjet control commands. For the altitude subsystem, an RBFNN-based controller addresses the actuator

Distributed consensus tracking under variable communication network is an important research topic of multi-agent systems (MASs). Many results have been obtained for consensus of MASs with switching networks that are either jointly connected or periodically connected, but it remains challenging when the disconnected switching topologies are activated more frequently than the connected ones. This paper

In real-world applications, it is often desired that the design of a closed-loop system must attain not only high performance but also robustness. This paper presents a novel robustness-based formulation for control for discrete time minimum and non-minimum phase systems using the Virtual Reference Feedback Tuning (VRFT) framework. The proposed idea is to design robust lower and higher order Proportional

Obtaining a control algorithm capable of navigating the system both in forward and backward motions is one of the control objectives for tractor-trailer wheeled robots (TTWRs). In this paper, a relatively general structure is presented for both forward and backward control of an n-trailer wheeled mobile robot (NTWMR) in the presence of wheel slip effects. To keep better overall performance and track

In this paper, a practical and systematic tuning procedure combining both frequency-domain (FD) and time-domain (TD) specifications is proposed to obtain an optimal robust fractional order (FO) PIλD (FOPIλD) controller for the first order plus time delay (FOPTD) processes. The FD specifications (i.e. phase margin (PM), gain crossover frequency (ωgc) and flat phase constrain (FPC)) guarantee the systemic

The second-order synchrosqueezing S-transform (SSST2) is an important method for instantaneous frequency (IF) estimation of non-stationary signals. Based on the synchrosqueezing S-transform, the instantaneous frequency calculation method is modified using the second-order partial derivatives of time and frequency to achieve higher frequency resolution. However, weak multi-frequency signals with strong

The application of optimization algorithms to adaptive motion control is proposed in this paper. In order to provide optimal system response, optimization algorithm is used as adjustment mechanism of controller coefficients. Most of optimization algorithms are not able to work in continuous optimization mode and with non-constant search space (i.e. dataset). For this reason, the introduction of a novel

To date, most control strategies have been designed for the two-dimensional and three-dimensional overhead crane systems with the single pendulum effect. In fact, three-dimensional overhead crane systems often exhibit the double-pendulum effect in practical applications, which is more controlled than the general single-pendulum crane, and also makes the controller design more challenging. This paper

This paper provides a model-parameter-free control strategy for the trajectory tracking problem of the autonomous underwater vehicle exposed to external disturbances and actuator failures. Two control architectures have been constructed such that the system states could be forced to the desired trajectories with acceptable performance. By combining sliding mode control (SMC) technology and adaptive

This paper concentrates on the problem of continuous-time switched linear systems with all subsystems unstable under average dwell time (ADT) criteria. Inspired by the matrix polynomial approach, a new method is proposed to further lessen conservativeness and improve system performance. The new method is based on a matrix polynomial and the discretized Lyapunov function (DLF) technique. Using the matrix

Hysteresis severely reduces positioning accuracy of a piezoelectric nanopositioning system. Inverse hysteresis model-based control is difficult to maintain satisfied performance in presence of uncertainties and disturbances. Linear active disturbance rejection control (LADRC) is a practical approach. However, phase lag of the total disturbance estimation degrades its estimation ability and tracking

This article focuses on observer-based state feedback H∞ control for a jacket structure against DoS attacks and external wave loads. First, a networked model of the structure is formulated as a switched delay system, in which DoS attacks and network-induced delays are considered simultaneously. A matching switched observer is developed for estimating states of the networked jacket structure system

Nonlinear dynamics are ubiquitous in complex systems. Their applications range from robotics to computational neuroscience. In this work, the Koopman framework for globally linearizing nonlinear dynamics is introduced. Under this framework, the nonlinear observable states are lifted into a higher dimensional, linear regime. The challenge is to identify functions that facilitate the coordinate transformation

This paper proposed a double-layer model predictive control (DLMPC) strategy integrated with zone control. In the steady-state target calculation (SSTC) layer, the controlled output variables are pretreated based on the analysis of process principle and production data. Subsequently, the optimal input–output targets and corresponding zone parameters are obtained by solving the steady-state optimization

A respiratory syndrome COVID-19 pandemic has become a serious public health issue nowadays. The COVID-19 virus has been affecting more than tens of millions people worldwide. Some of them have recovered and have been released. Others have been isolated and few others have been unfortunately deceased. In this paper, we apply and compare different machine learning approaches such as decision tree models

Wind turbine systems are constructed using different types of generators, aero-mechanical components and control systems. Due to their ability to work in low speed, Axial Flux Permanent Magnet (AFPM) generators are becoming widespread in wind energy systems which contributes to eliminating the gearbox from the system, noticeable increase in efficiency and decrease in system weight. Due to the modular

In this paper, a disturbance observer (DOB) based predictive control approach is developed for the image-based visual servoing of an inertially stabilized platform (ISP). As the limitation in degrees of freedom of a two-axes ISP, it is hard to estimate the variable feature depth of the target at each control cycle when using an uncalibrated camera, which brings the challenge in the design of the visual

In this paper, Transfer Learning is used in LSTM networks to forecast new COVID cases and deaths. Models trained in data from early COVID infected countries like Italy and the United States are used to forecast the spread in other countries. Single and multistep forecasting is performed from these models. The results from these models are tested with data from Germany, France, Brazil, India, and Nepal

This paper studies the problem of cooperative searching for dynamical moving targets by multiple unmanned aerial vehicles (UAVs). The environmental information possessed by UAVs is inconsistent due to packet losses of shared environmental information in communication channels and the discrepancies of detected information among different UAVs. To unify the environmental information among UAVs, the lost

In recent years, the control synthesis and analysis of networked control systems (NCSs) have attracted increasing attention from both industrial and scientific communities, and many contributions have been published. With the development of advanced control theories, it has become a trend to combine networks with control systems. As a specific nonlinear control method, due to its complete robustness

Deconvolution methods have been proven to be effective tools to extract excitation sources from the noisy measured signal. However, its application is confined by the extraction of incomplete information. To tackle this problem, a new deconvolution method, named period-oriented multi-hierarchy deconvolution (POMHD) is proposed in this paper. Various filters are designed adaptively by the iterative

This paper proposes an adaptive and robust adaptive control strategy based on type-2 fuzzy neural network (T2FNN) for tracking the desired trajectories of a quadrotor. The designed methods can control both the position and the orientation of a quadrotor flying robot. Contrary to common sliding mode controllers (SMCs), the robust adaptive trajectory tracking scheme presented in this paper is based on

As one of the most important components of machinery, once the bearing has a failure, serious catastrophe may happen. Hence, for avoiding the catastrophe, it is valuable to predict the remaining useful life (RUL) of bearing. Health indicators (HIs) construction plays a greatly important role in the data-driven RUL prediction. Unfortunately, most of the existing HIs construction methods need prior knowledge

For industrial processes, one common drawback of conventional process monitoring methods is that they would make an increasing number of false alarms in cases of various factors such as catalyst deactivation, seasonal fluctuation and so forth. To address this issue, the present work proposes an online dictionary learning method, which can fulfill the process monitoring and fault diagnosis task adaptively

This work realizes the adaptive neural disturbance rejection for the leader-follower cooperative synchronization of surface ships with model perturbations and ocean disturbances without leader velocity measurements. The virtual ship alleviates the requirements on leader ship’s velocities such that the information requirements are only position and heading on the leader ship. The adaptive neural networks

Due to difficulties in identifying localized and incipient bearing faults, most proposed fault diagnosis methods focus on detecting these faults. However, it is not clear to what extent of fault severity the proposed methods are capable of detecting. In other words, the crucial issue remains in the literature as to what is the criteria for defining an incipient defect for the proposed methods. This

In greedy pursuit algorithm, atom selection is commonly a concerned topic for signal compressed recovery. To improve the recovery performance, an optimal atom selection strategy without the prior information is proposed in this paper. The sensing information entropy is defined to prune the possible false atoms in the estimated support set. Fewer iterations are required in the proposed strategy and

The increase of using electric vehicles (EVs) may increase power demand and therefore major effects on the power system. Therefore, if we do not have an appropriate program for utilizing and managing battery charging in the EVs, the charging process may coincide with the peak of power consumption and cause severe network problems. This paper deals with modeling the problem and estimating load consumption

This work combines the benefits of cascaded control, Smith predictor, moment matching, and outer-loop decomposition to design an enhanced series cascaded control approach with Smith Predictor for some industrial integrating plants. The inner-loop controller is assumed as a PI/PID type. If the outer-loop process model is of second-order, it is split into first-order models and an individual control-loop

The problem of nonlinear tracking and detection of small unmanned aerial vehicles and micro-drone targets is very challenging and has received great attention recently. Recently, the Cubature Kalman-multi-Bernoulli filter which employs a third-degree spherical-radical cubature rule has been presented to handle the nonlinear models. The Cubature Kalman filter is more principled and accurate in mathematical

This paper studies the fixed-time stability of attitude coordination control for spacecraft formation flying (SFF) in the presence of some external disturbance. Firstly, to ensure that the states converge to the origin within a fixed time, a novel nonsingular terminal sliding mode surface (NTSMS) is designed. The convergence time is bounded by some predefined constants. Secondly, an attitude synchronization

In this paper, an efficient adaptive control is designed for chaotic Permanent Magnet Synchronous Motors (PMSMs) with full-state asymmetric time-varying constraints in the input saturation presence. The strategy that is suggested in this work is equipped with the command filtering for addressing the problem of the “explosion of complexity” available in the common backstepping method. In addition, the

The problem of fault estimation for nonlinear systems with Lipschitz nonlinearities is addressed in this work for the estimation of both the system fault and states. In the proposed approach disturbance is regarded to be a function which is nonlinear and coupled with states of the system, and fault to be a function which is additive. In order to diagnose the fault and reduce the disturbances effects

This paper aims to devise a novel fractional orthogonal basis to solve a certain class of nonlinear fractional optimal control problems with delay whose system dynamics is governed by a nonlinear fractional differential equation of the Caputo type. The foundation of the new framework is based on a hybrid of block-pulse and fractional-order Legendre functions. A new integral operator associated with

Since the collected data are often polluted by numerous measured noise or outliers, traditional subspace discriminant analysis is difficult to extract optimal diagnostic information. To alleviate the impact of the problem, a robust principal subspace discriminant analysis algorithm for fault diagnosis is designed. On the premise of decreasing the impact of redundant information, the optimal latent

This paper presents a fractional order (FO)-active disturbance rejection control (ADRC) with a FO extended state observer (FOESO) design. Applying this FOESO, a typical second order motion plant can be converted into a cascadedfractionalorderintegrator (1∕s2r;0

This article presents a unified approach of controller design in cascade control structure (CCS) for unstable, integrating and stable processes with dead-time to achieve enhanced load disturbance rejection. The design of inner and outer loop controllers in CCS is based, partially on the direct synthesis approach and partially on the pole placement method. First, the parameters of the inner loop controller

CA50 and IMEP (indicated mean effective pressure) are two critical parameters indicating the combustion process and work output. CA50 and IMEP are playing important roles in advanced combustion modeling and control for compression-ignition and spark-ignition engines. However, both CA50 and IMEP are calculated by using the in-cylinder pressure trace, the failure or aging/wearing of cylinder pressure

In this work, the design and development of a new variable linear power supply, using the LM317 regulator, is proposed. The originality of this power supply is related with the control of the rectifier circuit, which is based on a reference of voltage, superimposed on the output voltage. This reference is used in the control strategy to maintain a constant voltage between the input and the output voltages

Active Disturbance Rejection Control (ADRC) emerged as a promising control solution in various engineering domains. However, increased ADRC order makes it difficult to implement and tune in practice. On the other hand, Reduced-order ADRC (RADRC) structure solves this issue with the appropriate tuning of its parameters to achieve the desired performance. This paper aims to develop analytical tuning

Gait planning for the humanoid robot is a very essential and basic requirement. The humanoid robot is balanced at two feet; therefore, special attention is required for gait analysis for the execution of assigned tasks. In this paper, the linear inverted pendulum (LIPM) model is considered to simplify the study and to obtain better gait planning of humanoid robot NAO. Centre of mass (COM) and zero

Seasonality is a fundamental and common property of most time series in the real world. In this article, we propose a grey seasonal least square support vector regression, abbreviated as GSLSSVR, by combining the dummy variables, framework of the LSSVR model, and grey accumulation generation operation to reflect seasonal variations in functional forms, variables, and parameters. Our framework provides

Aiming at the minority samples cannot be effectively diagnosed when the samples are limited and imbalanced, a multiple classifier ensemble of the weighted and balanced distribution adaptation method (MC-W-BDA) is presented to solve the rolling bearing’s fault diagnosis problem under the limited samples imbalance. We adopt random sampling to obtain enough different training sample sets whose base classifiers

Path planning is a basic function for autonomous vehicle (AV). However, it is difficult to adapt to different velocities and different types of obstacles including dynamic obstacle and static obstacle (such as road boundary) for AV. To solve the problem of path planning under different velocities and different types of obstacles, a two potential fields fused adaptive path planning system (TPFF-APPS)

Planetary gear reducer is widely used in industrial automation, and its performance highly affects the equipment reliability. The backlash and stiffness may cause the performance decline of planetary, hence the vibration, temperature, current and other signals are applied in planetary condition monitoring. The purpose of this paper is to develop a practical and effective method based on motor current