Variable Sampling Period Research Articles

Robust Adaptive Control System of Variable Sampling Period for Cement Raw Mix Quality Control

The advanced quality control of the raw mix remains a priority for the cement industry, particularly in recent years, where large quantities of alternative fuels and raw materials are used in clinker production, aiming to reduce the CO2 footprint. This study presents an adaptive control system with a variable sampling period for regulating raw mix quality in the raw mill (RM) output in a process with four independent inputs and four outputs: the lime saturation factor (LSF), silica modulus (SM), alumina modulus (AM), and SO3. The three pillars of the system are (1) mill dynamics calculation using exclusively industrial data, (2) the design of the controllers to meet robustness criteria, and (3) performance enhancement through simulators. Our technique periodically adjusts the gains of the controllers based on the mill’s dynamic parameters, which are computed from raw mix laboratory analyses. The presented results correspond to more than 14,000 h of mill operation. The standard deviation of the LSF at the mill outlet ranged from 1.5 to 3, which is equivalent to 1 to 2 standard deviations of LSF reproducibility. The standard deviation of the other moduli was close to the corresponding reproducibility of each. The presented adaptive gain-scheduling controller for LSF can be applicable to a broad range of raw meal grinding systems.

Identification of fractional order time delay system with measurement noise using variable period integration operational matrix

This paper proposes a parameter identification method for fractional-order time-delay systems with measurement noise, based on the Legendre wavelet variable-period integration operational matrix. A variable-period integration operational matrix and a variable-period time-delay integration operational matrix are constructed by variable-period sampling principle. For the impulse noise, global Gaussian noise and local Gaussian noise present in the identification data, the variable-period sampling, operational matrix global coefficient decomposition and matrix local coefficient decomposition methods are used to reconstruct the variable-period integration operational matrix to separate the noise from the data; the fractional-order time-delay system is expressed as an integral equation by using the reconstructed variable-period operational matrix, this reduces the influence of measurement noise on system identification accuracy. To further improve the parameter identification accuracy, the integral equation is solved by the multi-innovation least squares algorithm. The proposed method’s effectiveness is verified through several simulations and an experimental study of a constant-temperature system.

Dynamic event-triggered control for CPSs under QoS-based variable sampling approach

In this article, a quality of service (QoS) dependent variable sampling dynamic event-triggered control method is designed for a cyber–physical system (CPS) with delays and packets dropout to cope with non-ideal network environments, maintain the desired control performance and improve the communication efficiency. To achieve the variable period sampling, a sampler is designed based on the QoS of the wireless network by using the delta operator discretization method. Then, a variable period sampling scheme for the delta operator system converted from the CPS is designed. Furthermore, a dynamic event-triggered mechanism (DETM) is proposed using the variable period sampling signal, which can reduce event triggered data calculations and increase event triggered intervals. By utilizing the average dwell time (ADT) approach, sufficient conditions contains the explicit variable sampling period are derived for the derived switched CPS. Finally, the effectiveness of the designed method is verified by numerical examples.

Stability analysis of interval type-2 sampled-data polynomial fuzzy-model-based control system with a switching control scheme

The stability of an interval type-2 (IT2) sampled-data (SD) polynomial fuzzy-model-based control system with a switching control scheme is studied in this paper. The uncertain nonlinear plant is depicted via an IT2 polynomial fuzzy model. To realize control, a switching IT2SD polynomial fuzzy controller is generated. This paper adopts a switching control scheme with a variable sampling period. The modeling domain consists of several sub-domains, and each sub-domain corresponds to a local IT2SD polynomial fuzzy controller. These local IT2SD polynomial fuzzy controllers form the switching IT2SD polynomial fuzzy controller. To aid in the stability analysis, this paper adopts a looped-functional-based technique. The imperfect premise matching concept is brought in to solve the mismatch dilemma caused by the SD control strategy and uncertainties. For decreasing the conservativeness, this paper takes into account the state information as well as the information of IT2 membership functions. The stability analysis is performed for each sub-domain, providing the potential for further relaxation. As polynomials exist in the stability conditions, this paper employs the sum-of-squares method for the stability investigation. The simulation outcomes confirm the efficacy of the proposed method.

Dynamic Event-Triggered Intermittent Fault Detection for Time-Varying Stochastic Systems

In this paper, the problem of intermittent fault (IF) detection is investigated for stochastic linear time-varying (LTV) systems using dynamic event-triggered methods. Using the nonuniform sampling approach, the event-triggered system is transformed into a time-varying system with varying sampling periods. Using the moving horizon estimation strategy, a new IF detection filter is designed to generate residual signals which are decoupled from event-triggered transmission errors and estimation errors. Moreover, an event-triggered IF detection algorithm is proposed such that the appearance time and disappearance time of IFs can be detected quickly for stochastic LTV systems. In order to analyze the detectability of IFs for systems with/without event-triggered cases, the concept of distinguishability is introduced for IFs. Sufficient conditions are derived to guarantee the detectability of IFs for LTV systems. Finally, an experiment concerning the rotary steerable drilling tool system is provided to illustrate the effectiveness of the proposed IF detection method.

Prediction of chlorophyll-a as an indicator of harmful algal blooms using deep learning with Bayesian approximation for uncertainty assessment

Data-driven models are efficient decision support tools for monitoring harmful algal blooms (HABs), particularly with the advent of the Internet of Things (IoT) and continuous data acquisition sensing systems. While high chlorophyll-a concentrations in freshwater systems are often used as an index of HABs, diurnal variations in physicochemical water quality parameters influence chlorophyll-a concentrations in these systems. Previous studies have demonstrated the effectiveness of deep learning models in predicting chlorophyll-a as an index of HABs. However, predicting HABs before they occur often comes with uncertainties related to the frequency of observed data. This study used varying sampling periods to develop various Long Short-Term Memory (LSTM) models coupled with a Monte Carlo dropout scheme to estimate model uncertainties for chlorophyll-a predictions in a freshwater lake. The model’s performances were compared using indices such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and correlation coefficient. The results showed that while a daily water quality dataset predicted chlorophyll-a concentrations 3-days in advance with the conventional 0.2 dropout rate, an hourly dataset predicted chlorophyll-a concentrations 7-days in advance. When the effect of the sampling period was considered, the full dataset (both daytime and nighttime datasets combined) provided better chlorophyll-a predictions compared to the model that only used either the daytime or nighttime dataset. This research outcome will assist in predicting HABs timely using chlorophyll-a as an index, aid in developing early warning systems, and understand the data-sampling, data-transmission, and data-storage needs, which are often the critical elements for data-driven modeling.

An Accelerated Approach on Adaptive Gradient Neural Network for Solving Time-Dependent Linear Equations: A State-Triggered Perspective.

To improve the acceleration performance, a hybrid state-triggered discretization (HSTD) is proposed for the adaptive gradient neural network (AGNN) for solving time-dependent linear equations (TDLEs). Unlike the existing approaches that use an activation function or a time-varying coefficient for acceleration, the proposed HSTD is uniquely designed from a control theory perspective. It comprises two essential components: adaptive sampling interval state-triggered discretization (ASISTD) and adaptive coefficient state-triggered discretization (ACSTD). The former addresses the gap in acceleration methods related to the variable sampling period, while the latter considers the underlying evolutionary dynamics of the Lyapunov function to determine coefficients greedily. Finally, compared with commonly used discretization methods, the acceleration performance and computational advantages of the proposed HSTD are substantiated by the numerical simulations and applications to robotics.

Scheduling strategy for networked control system based on variable sampling period and deadband feedback

SummaryA scheduling strategy of variable sampling period combined with deadband feedback for networked control system is proposed. Variable sampling period algorithm can allocate a reasonable sampling period to each controlled loop according to the network utilization and packet transmission time. Deadband feedback algorithm can alleviate network congestion by appropriately adjusting the packets in the network when the networked control system cannot be scheduled. According to the actual overload and utilization of the network, the designed scheduling strategy dynamically adjusts the sampling period and priority, and improves the performance of the system combined with deadband feedback. Based on the TrueTime platform, the proposed scheduling strategy is verified on a three controlled loops networked control system with interference nodes and limited network resources. The simulation results demonstrate that the designed scheduling strategy can overcome the uncertainty of the upper bound of network resources, improve output control performance, reduce integral absolute error value of the controlled loop, and shorten the packet transmission time. The overall control performance of the system is improved. The designed scheduling strategy is effective.

On Robustification of Sampled-Data Dynamic Output-Feedback Stabilizers for Control-Affine Nonlinear Systems

In this paper, the robust sampled–data stabilization of nonlinear systems is investigated. In particular, a methodology for the design of robust sampled–data Dynamic Output Feedback Controllers (DOFCs) is provided for control–affine nonlinear systems affected by actuation disturbances and measurement errors. The notion of dynamic output steepest descent feedback, continuous or not, induced by suitably Control Lyapunov functions is used together with the ISS redesign methodology for the development of the proposed robust sampled–data stabilizer. Under the assumption that the actuation disturbances and measurement errors are bounded with a–priori known bounds and that the measurement error affects marginally the control term designed via the ISS redesign methodology it is proved that: there exists a suitably fast sampling such that the proposed sampled–data DOFC yields semi–global practical stability of the related closed–loop system regardless of the above actuation disturbances and measurement errors. In the methodology here proposed, time–varying sampling periods are allowed and the intersampling system behaviour is taken into account. The provided results are validated on a chemical reactor system.

Dynamic Event-Triggered Hᵢ/H∞ Optimization Approach to Fault Detection for Unmanned Aerial Vehicles

This paper dealt with the problem of dynamic event-triggered fault detection (FD) for unmanned aerial vehicles (UAVs) in the Hi/H∞ optimization criterion. In order to make use of limited communication resources, a dynamic event-triggered mechanism (ETM) was considered to determine whether system information will be transmitted to the ground station. Note that the transmitted data failing to satisfy a predefined triggered condition were discarded directly. As such, FD performance was affected by not only disturbances and faults, but also dynamic event-triggered transmission errors, namely the errors between the data of non-event time and the current actual system. To solve this problem, a new dynamic event-triggered Hi/H∞ optimization approach was presented, whose contribution lied in the complete decoupling of residuals and event transmission errors under dynamic ETM, which can avoid continuous communication and Zeno phenomenon. Firstly, a dynamic ETM was presented to determine communication time. Then, a variable sampling period model of UAV was established based on the dynamic ETM. On this basis, a dynamic event-triggered residual generator was constructed. In the Hi/H∞ optimization criterion, Riccati recursion was used to compute the optimum solution of a dynamic event-triggered FD filter. Moreover, a new residual evaluation method was proposed, whose effectiveness and feasibility were finally verified by the altitude control system of a UAV.

Tracking control with aperiodic sampling over networks with delay and dropout

This paper introduces a discrete-time technique for output feedback tracking control with aperiodic sampling over networks suffering delay and dropout. Introducing the concept of pseudo sampling, NCS is modelled as a linear parameter varying (LPV) system. Two methods are explained to obtain a convex approximation of the uncertainty region. It is clarified how to perform the approximation to have the uncertainty space as small as possible. Using this uncertainty space, a polytopic approximation is obtained for the LPV model. The approximated model is used to synthesise a controller for set-point tracking and disturbance rejection. An observer is also synthesised based on this model. The simplicity of the model together with a small uncertainty space, results in less conservative LMI conditions. Compared to the existing results, an improved tracking performance is obtained. The final value theorem for discrete-time LTI systems with constant sampling period is extended to time varying sampling periods. This theorem is used to design a feedforward gain for zero steady-state tracking of staircase reference signals.

Deadband feedback-based scheduling approach for networked control system with variable sampling period

Reasonable information scheduling strategies in the networked control system (NCS) can improve the quality of service of the network, reduce the conflict of information transmission in the network, and improve the overall performance of the NCS. In order to improve the performance of the NCS, a deadband feedback-based scheduling approach for the NCS with a variable sampling period is proposed. For the NCS with multi control loops, considering the limitation of network bandwidth resources, the dynamic real-time adjustment of a multi-loop sampling period is achieved through network utilization prediction, network bandwidth configuration and sampling period calculation. Furthermore, deadband feedback scheduling is combined with a variable sampling period algorithm. Deadband is set in the sensor and controller nodes to effectively adjust the information flow of the forward channel and the feedback channel. The proposed scheduling approach can reduce the impact of network conflict and network delay on system stability, make the network resources allocated reasonably, save network data traffic, and improve the overall performance of the NCS. A NCS with five control loops is used as the simulation object and carried out by True Time toolbox. The simulation results show that the proposed scheduling approach can improve output control performance of the system, reduce integral absolute error value of the control loops, and improve network utilization. The overall control performance of the system is improved.

An $H_{i}/H_{\infty }$ Optimization Approach to Event-Triggered Fault Detection for Linear Discrete Time Systems

In this article, a new event-triggered $H_{i}/H_{\infty }$ optimization fault detection (FD) scheme is proposed for linear discrete time systems. A linear discrete time system with varying sampling periods is first presented to describe the dynamics of an event-triggered system. Based on this, an observer-based fault detection filter (FDF) is constructed as an event-triggered residual generator so that the generated residual signal is completely decoupled from the event-triggered transmission error. The design of event-triggered FDF is formulated as a multiobjective optimization problem denoted by $H_{i}/H_{\infty }$ and an optimal solution can be obtained by recursive calculation of Riccati equations. In addition, an event-triggered residual evaluation strategy is also presented. Comparing with the existing event-triggered FD schemes, the major contribution of this article is to decouple the residual signals from the event-triggered transmission errors completely and, consequently, the FD performance can be improved significantly. Moreover, an optimal tradeoff between the robustness of residual to unknown input and the sensitivity of residual to fault is achieved in the sense of $H_{i}/H_{\infty }$ optimization, while the design of FDF and event generator can be carried out independently. To demonstrate the effectiveness of the proposed method, a vehicle lateral dynamic system is used as a simulation example.

AUToSen: Deep-Learning-Based Implicit Continuous Authentication Using Smartphone Sensors

Smartphones have become crucial for our daily life activities and are increasingly loaded with our personal information to perform several sensitive tasks, including, mobile banking and communication, and are used for storing private photos and files. Therefore, there is a high demand for applying usable authentication techniques that prevent unauthorized access to sensitive information. In this article, we propose AUTo Sen , a deep-learning-based active authentication approach that exploits sensors in consumer-grade smartphones to authenticate a user. Unlike conventional approaches, AUTo Sen is based on deep learning to identify user distinct behavior from the embedded sensors with and without the user’s interaction with the smartphone. We investigate different deep learning architectures in modeling and capturing users’ behavioral patterns for the purpose of authentication. Moreover, we explore the sufficiency of sensory data required to accurately authenticate users. We evaluate AUTo Sen on a real-world data set that includes sensors data of 84 participants’ smartphones collected using our designed data-collection application. The experiments show that AUTo Sen operates accurately using readings of only three sensors (accelerometer, gyroscope, and magnetometer) with a high authentication frequency, e.g., one authentication attempt every 0.5 s. Using sensory data of one second enables an authentication F1-score of approximately 98%, false acceptance rate (FAR) of 0.95%, false rejection rate (FRR) of 6.67%, and equal error rate (EER) of 0.41%. While using sensory data of half a second enables an authentication F1-score of 97.52%, FAR of 0.96%, FRR of 8.08%, and EER of 0.09%. Moreover, we investigate the effects of using different sensory data at variable sampling periods on the performance of the authentication models under various settings and learning architectures.

Exponential Stabilization of Delayed Complex-valued Neural Networks with Aperiodic Sampling: A Free-matrix-based Time-dependent Lyapunov Functional Method

In this paper, the exponential stabilization of delayed complex-valued neural networks (DCNNs) is addressed via sampled-data control. First, aperiodic sampled-data control aimed at further reducing the frequency of data transmission is adopted, which covers the periodic sampling as a special case. Then, a free-matrix-based time-dependent Lyapunov functional is specially constructed for stability analysis of closed-loop DCNNs, in which two extra free matrices are introduced and the available information of system states at the sampling instants are fully utilized. Accordingly, some less conservative stability conditions are established. By resorting to a matrix transformation, the design scheme for the feedback gains can be obtained. Meanwhile, the qualitative relationship between the decay rate and the upper bound of the variable sampling period is established and the maximum allowable value of the variable sampling period is determined. Finally, an illustrative example is provided to demonstrate the feasibility of the proposed stabilization criteria.

Event-based state-space model predictive control of a renewable hydrogen-based microgrid for office power demand profiles

This paper focuses on the design and implementation of an event-based control architecture to manage a renewable-based microgrid. This microgrid has renewable-energy generation and a hybrid energy storage system that uses electricity and hydrogen. The main load of the microgrid is the energy demand of an office. The primary control objective is to satisfy this load using the available renewable generation and stored energy while reducing the amount of energy purchased from the Utility Power Grid and the degradation of the electromechanical storage devices. To do that, the control architecture defined within an event framework, makes use of a set of state-space model predictive controllers which are selected as a function of a variable sampling period. To evaluate the performance of the proposed architecture, simulation tests for a summer day as well as an analytical study is performed. The obtained results show that the use of the event-based control architecture allows a significant reduction of the number of changes in the control action at the expense of an acceptable deterioration of set-point tracking for a microgrid with several types of electrochemical storage.

Novel stability criteria for sampled-data systems with variable sampling periods

This paper is concerned with a novel Lyapunovlike functional approach to the stability of sampled-data systems with variable sampling periods. The Lyapunov-like functional has four striking characters compared to usual ones. First, it is time-dependent. Second, it may be discontinuous. Third, not every term of it is required to be positive definite. Fourth, the Lyapunov functional includes not only the state and the sampled state but also the integral of the state. By using a recently reported inequality to estimate the derivative of this Lyapunov functional, a sampled-interval-dependent stability criterion with reduced conservatism is obtained. The stability criterion is further extended to sampled-data systems with polytopic uncertainties. Finally, three examples are given to illustrate the reduced conservatism of the stability criteria.

State estimation and self-triggered control of CPSs against joint sensor and actuator attacks

In this paper, a practical state estimation strategy against joint sensor and actuator attacks is proposed for cyber-physical systems (CPSs). The CPSs are formulated based on delta operator for the requirement of real control systems with high-sampling or variable-sampling periods. The attacks fulfilling sparse observation condition are supposed to be randomly injected into both sensor and actuator units. Various numbers of attacks on sensors and actuators can be properly handled. Based on multiple observations of the attacked measurements, a new estimator in delta-domain is proposed to estimate the states of the attacked CPS. The convergence of the corresponding estimation error is analyzed based on pole assignment analysis and some matrix equality constraints. Further, by utilization of a dissipativity approach, estimator-based time-driven controller and self-triggered controller are designed for the enhancement of the estimations and the resource saving. Simulations for the joint attack scenario are provided to testify the applicability and effectiveness of the proposed estimation methods.

Finite-time synchronization of sampled-data Markovian jump complex dynamical networks with additive time-varying delays based on dissipative theory

This paper is concerned with the problem of finite-time synchronization issue of sampled data Markovian jump complex dynamical networks (MJCDNs) with additive time-varying delays based on dissipative theory. Sufficient conditions to guarantee the finite-time stability of MJCDNs with additive time-varying delays are presented. Sampled-data control with stochastically varying sampling periods is considered. The closed-loop system is not just finite-time bounded but also satisfies the dissipativity conditions. Further we handled a nonuniform sampled data controller. By utilizing the properties of Kronecker product combined with the appropriate Lyapunov-functionals technique, a novel delay-dependent finite-time stability of dissipativity rule is derived in terms of linear matrix inequalities (LMIs) to ensure that the delayed complex dynamical systems to be dissipative. Finally numerical examples are given to represent the applicability of the proposed approach.

Sampled-data internet of connected vehicles control with channel fading and time-varying delay

Abstract This paper is concerned with sampled-data internet of connected vehicles (IOCV) control systems with a communication channel fading and time-varying delay. A hybrid IOCV system model is established, where the communication channel fading and time-varying delay are given full considerations. Based on the model, by employing Lyapunov-Krosovskii functional, a new sampled-data controller method with variable sampling period is derived, which can robustly stabilize the IOCV system with a given level of disturbance attenuation. Furthermore, the obtained sampled-data controller is complemented by additional conditions established to guaranteeing string stability and zero steady state tracking errors. Finally, the effectiveness and superiority of the proposed control strategies are verified by both numerical simulation and experiment with four-laboratory scale cars.