Observer-based dynamic event-triggered control for distributed parameter systems over mobile sensor-plus-actuator networks

We develop a policy of observer-based dynamic event-triggered state feedback control for distributed parameter systems over a mobile sensor-plus-actuator network.It is assumed that the mobile sensing devices that provide spatially averaged state measurements can be used to improve state estimation in the network.For the purpose of decreasing the update frequency of controller and unnecessary sampled data transmission, an efficient dynamic event-triggered control policy is constructed.In an event-triggered system, when an error signal exceeds a specified time-varying threshold, it indicates the occurrence of a typical event.The global asymptotic stability of the event-triggered closed-loop system and the boundedness of the minimum inter-event time can be guaranteed.Based on the linear quadratic optimal regulator, the actuator selects the optimal displacement only when an event occurs.A simulation example is finally used to verify that the effectiveness of such a control strategy can enhance the system performance.

Improved control of distributed parameter systems using wireless sensor and actuator networks:An observer-based method

In this paper,the control problem of distributed parameter systems is investigated by using wireless sensor and actuator networks with the observer-based method.Firstly,a centralized observer which makes use of the measurement information provided by the fixed sensors is designed to estimate the distributed parameter systems.The mobile agents,each of which is affixed with a controller and an actuator,can provide the observer-based control for the target systems.By using Lyapunov stability arguments,the stability for the estimation error system and distributed parameter control system is proved,meanwhile a guidance scheme for each mobile actuator is provided to improve the control performance.A numerical example is finally used to demonstrate the effectiveness and the advantages of the proposed approaches.

Cyber-Physical Systems as General Distributed Parameter Systems:Three Types of Fractional Order Models and Emerging Research Opportunities

Cyber-physical systems(CPSs) are man-made complex systems coupled with natural processes that, as a whole,should be described by distributed parameter systems(DPSs) in general forms. This paper presents three such general models for generalized DPSs that can be used to characterize complex CPSs. These three different types of fractional operators based DPS models are: fractional Laplacian operator, fractional power of operator or fractional derivative. This research investigation is motivated by many fractional order models describing natural, physical, and anomalous phenomena, such as sub-diffusion process or super-diffusion process. The relationships among these three different operators are explored and explained. Several potential future research opportunities are then articulated followed by some conclusions and remarks.

Impedance-based Stability Criterion for the Stable Evaluation of Grid-connected Inverter Systems with Distributed Parameter Lines

For a multi-inverter grid-connected system,the stability of the point of common coupling(PCC)voltage is evaluated considering the distribution parameters of the transmission lines.First,the systems on both sides of the PCC are equalized,a smallsignal equivalent circuit similar to the“current source-grid”is established,and a mathematical model for the voltage of the PCC is derived.Then,using Euler’s formula and Nyquist stability criterion,the PCC voltage stability of the grid-connected system is evaluated by the impedance analysis method under the premise that the single-side excitation is stable.In addition,the gridconnected conditions causing PCC voltage instability are studied.A phase compensation method based on an impedance phase compensation control strategy is introduced.The stability of the grid-connected system is improved by compensating the phase margin at the equivalent impedance crossover-section frequency on both sides of the grid-connected system PCC.Finally,a simulation circuit is built to simulate and analyze the proposed model and phase compensation method.The simulation results verify the accuracy and effectiveness of the theoretical analysis.

Harmonic Amplification Analysis of Cable Lines with Distributed Parameters Based on Kalman Filter and Convolution Inversion

Harmonic amplification phenomena could appear at the point of common connection(PCC) of the cable line terminal. However, the distributed parameter model of the cable line contains hyperbolic functions with plural variables, which makes it challenging to obtain the harmonic amplification factor(HAF). Hence, a time-domain method combining the Kalman filter and convolution inversion(KFCI) methods is proposed to address this problem. First, the Kalman filter method optimizes the square wave pulse response(SWPR) with measurement error. Then, the optimized SWPR data are used to get the HAF by the convolution inversion method. Next, the harmonic amplification characteristics of cable lines are explored. Finally, an experimental simulation model is built on the PSCAD software, verifying the optimization effectiveness of Kalman filter for the SWPR with error and the accuracy of the HAF calculated by the proposed method. The analysis rationality of harmonic amplification properties is also demonstrated.

Differentially Private Distributed Parameter Estimation

Data privacy is an important issue in control systems,especially when datasets contain sensitive information about individuals.In this paper,the authors are concerned with the differentially private distributed parameter estimation problem,that is,we estimate an unknown parameter while protecting the sensitive information of each agent.First,the authors propose a distributed stochastic approximation estimation algorithm in the form of the differentially private consensus+innovations(DP-CI),and establish the privacy and convergence property of the proposed algorithm.Specifically,it is shown that the proposed algorithm asymptotically unbiased converges in mean-square to the unknown parameter while differential privacy-preserving holds for finite number of iterations.Then,the exponentially damping step-size and privacy noise for DP-CI algorithm is given.The estimate approximately converges to the unknown parameter with an error proportional to the step-size parameter while differential privacy-preserving holds for all iterations.The tradeoff between accuracy and privacy of the algorithm is effectively shown.Finally,a simulation example is provided to verify the effectiveness of the proposed algorithm.

Mathematical modeling of an armature assembly in pilot stage of a hydraulic servo valve based on distributed parameters

The dynamic performance of a nozzle-flapper servo valve can be affected by several factors such as the disturbance of the input signal,the motion of the armature assembly and the oscillation of the jet force.As the part of vibrating at high frequency,the armature assembly plays a vital role during the operation of the servo valve.In order to accurately predict the transient response of the armature assembly during the vibration,a mathematical model of armature assembly is established based on the distributed parameters method(DPM)and Hamilton principle.The new mathematical model is composed of three main parts,the modal eigenfunction,modal mechanical response expressions of the spring tube and the motion equation of the other armature assembly.After programing,the purpose of using the DPM to predict the dynamic response of different positions located on the armature assembly is achieved.For verifying the validity of the mathematical model,the finite element method(FEM)and classic model(CM)of armature assembly are applicated by commercial software under the same condition.The comparison results prove that the DPM can effectively predict the axial and tangential deflection of the armature assembly different positions which the CM can’t duing to its over-simplification.A certain error is generated when predicting the axial deformation at different heights by DPM,which is caused by an approximate method to simulate the torsion of the spring tube.The comparison results of the spring tube deflection at different vibration frequencies shows that the adaptability of DPM is significantly higher than the classic model,which verify the model is more adaptable for predicting the dynamic response of the armature assembly.

Adaptive neural networks control for uncertain parabolic distributed parameter systems with nonlinear periodic time-varying parameter

This paper studies the problem of adaptive neural networks control(ANNC) for uncertain parabolic distributed parameter systems(DPSs) with nonlinear periodic time-varying parameter(NPTVP). Firstly, the uncertain nonlinear dynamic and unknown periodic TVP are represented by using neural networks(NNs) and Fourier series expansion(FSE), respectively. Secondly, based on the ANNC and reparameterization approaches, two control algorithms are designed to make the uncertain parabolic DPSs with NPTVP asymptotically stable. The sufficient conditions of the asymptotically stable for the resulting closed-loop systems are also derived. Finally, a simulation is carried out to verify the effectiveness of the two control algorithms designed in this work.

Controllability of Singular Distributed Parameter Systems in the Sense of Mild Solution

Necessary and sufficient conditions for the exact controllability and approximate controllability of a singular distributed parameter system are obtained.These general results are used to examine the exact controllability and approximate controllability of the Dzektser equation in the theory of seepage.

Parameter identification of partially covered piezoelectric cantilever power scavenger based on the coupled distributed parameter solution

Among the various techniques of power scavenging,piezoelectric energy harvesting usually has more power density.Although piezoceramics are usually more efficient than other piezoelectric materials,since they are very brittle and fragile,researchers are looking for alternative materials.Recently Cellulose Electro-active paper(EAPap)has been recognized as a smart material with piezoelectric behavior that can be used in energy scavenging systems.The majority of researches in energy harvesting area,use unimorph piezoelectric cantilever beams.This paper presents an analytical solution based on distributed parameter model for partially covered pieoelectric cantilever energy harvester.The purpose of the paper is to describe the changes in generated power with damping and the load resistance using analytical calculations.The analytical data are verified using experiment on a vibrating cantilever substrate that is partially covered by EAPap films.The results are very close to each other.Also asymptotic trends of the voltage,current and power outputs are investigated and expressions are obtained for the extreme conditions of the load resistance.These new findings provide guidelines for identification and manipulation of effective parameters in order to achieve the efficient performance in different ambient source conditions.

Mobile observation for distributed parameter system with moving boundary over mobile sensor networks

This paper considers the state observation for a class of distributed parameter systems(DPSs)with moving boundaries modelled by parabolic partial differential equations(PDEs).The method of mobile observation is presented to improve the observation performance of the systems and to eliminate the influence of moving boundaries on system observation with the aids of mobile sensor networks(MSNs).The MSNs which can move throughout the time-dependent spatial domain are used to provide the spatially averaged observations of the DPSs.By using the abstract evolution equation theory of parabolic PDEs and the Lyapunov stability arguments,a centralised observer is designed and a mobile control scheme for each of the mobile sensors is presented while taking account of the dynamics of the MSNs.A numerical example is finally presented to illustrate the effectiveness and the advantages of the proposed approach.

Exponential stability and L_(2) gain analysis of switched distributed parameter systems with time delay

The present paper is concerned with stability and L_(2) gain analysis of switched distributed parameter system(SDPS)with time delay.First,exponential stability is discussed,and the state decay estimate of the system is explicitly given by using linear matrix inequalities(LMIs)incorporating with the average dwell time(ADT)method.Second,L_(2) gain analysis is also studied.At last,illustrative examples are given to show the effectiveness of the proposed method.The main contribution of the paper is:some criteria of exponential stability and L_(2) gain for multiple-input multiple-output(MIMO)switched PDE are developed in the form of LMIs and ADT signal for the first time.The advantage of the work is we generalize the application range of the related research.The proposed method is expected to provide an effective tool for stability and H∞control analysis of SDPS.

A modified lumped parameter model of distribution transformer winding

The modelling of the distribution transformer winding is the starting point and serves as important basis for the transformer characteristics analysis and the lightning pulse response prediction.A distributed parameters model can depict the winding characteristics accurately,but it requires complex calculations.Lumped parameter model requires less calculations,but its applicable frequency range is not wide.This paper studies the amplitude-frequency characteristics of the lightning wave,compares the transformer modelling methods and finally proposes a modified lumped parameter model,based on the above comparison.The proposed model minimizes the errors provoked by the lumped parameter approximation,and the hyperbolic functions of the distributed parameter model.By this modification it becomes possible to accurately describe the winding characteristics and rapidly obtain the node voltage response.The proposed model can provide theoretical and experimental support to lightning protection of the distribution transformer.

Backstepping-Based Distributed Abnormality Detection for Nolinear Parabolic Distributed Prameter Systems

In this paper, we proposed a model-based abnormality detection scheme for a class of nonlinear parabolic distributed parameter systems (DPSs). The proposed methodology consists of the design of an observer and an abnormality detection filter (ADF) based on the backstepping technique and a limited number of in-domain measurements plus one boundary measurement. By taking the difference between the measured and estimated outputs from observer, a residual signal is generated for fault detection. For the detection purpose, the residual is evaluated in a lumped manner and we propose an explicit expression for the time-varying threshold. The convergence properties of the PDE observer and the residual are analyzed by Lyapunov stability theory. Eventually, the proposed abnormality detection scheme is demonstrated on a nonlinear DPS.

High-Accuracy Confidence Regions for Distribution Parameters

With the help of today’s computers, it is always relatively easy to find maximum-likelihood estimators of one or more parameters of any specific statistical distribution, and use these to construct the corresponding approximate confidence interval/region, facilitated by the well-known asymptotic properties of the likelihood function. The purpose of this article is to make this approximation substantially more accurate by extending the Taylor expansion of the corresponding probability density function to include quadratic and cubic terms in several centralized sample means, and thus finding the corresponding -proportional correction to the original algorithm. We then demonstrate the new procedure’s usage, both for constructing confidence regions and for testing hypotheses, emphasizing that incorporating this correction carries minimal computational and programming cost. In our final chapter, we present two examples to indicate how significantly the new approximation improves the procedure’s accuracy.

Vibration Control of a High-Rise Building Structure:Theory and Experiment

In this study,an innovative solution is developed for vibration suppression of the high-rise building.The infinite dimensional system model has been presented for describing high-rise building structures which have a large inertial load with the help of the Hamilton’s principle.On the basis of this system model and with the use of the Lyapunov’s direct method,a boundary controller is proposed and the closed-loop system is uniformly bounded in the time domain.Finally,by using the Smart Structure laboratory platform which is produced by Quancer,we conduct a set of experiments and find that the designed method is resultful.

Multi-Model Based PSO Method for Burden Distribution Matrix Optimization With Expected Burden Distribution Output Behaviors

Burden distribution is one of the most important operations, and also an important upper regulation in blast furnace(BF) iron-making process. Burden distribution output behaviors(BDOB) at the throat of BF is a 3-dimensional spatial distribution produced by burden distribution matrix(BDM),including burden surface output shape(BSOS) and material layer initial thickness distribution(MLITD). Due to the lack of effective model to describe the complex input-output relations,BDM optimization and adjustment is carried out by experienced foremen. Focusing on this practical challenge, this work studies complex burden distribution input-output relations, and gives a description of expected MLITD under specific integral constraint on the basis of engineering practice. Furthermore, according to the decision variables in different number fields, this work studies optimization of BDM with expected MLITD, and proposes a multi-mode based particle swarm optimization(PSO) procedure for optimization of decision variables. Finally, experiments using industrial data show that the proposed model is effective, and optimized BDM calculated by this multi-model based PSO method can be used for expected distribution tracking.

INTELLIGENT INTEGRATION CONTROL OF ROTATING DISK VIBRATION

The rotating disk is a basic machine part that is u sed widely in industry. The motion equation is transformed into the dynamic equa tion in real modal space. The personating intelligent integration is introduced to improve the existing control method. These modes that affect the transverse vibration mainly are included to simulate the vibration of rotating disk, and two methods are applied separately on condition that the sensor and the ac tuator are collocated and non collocated. The results obtained by all sided si mulations show that the new method can obtain better control effect, especially when the sensor and the actuator are non collocated.

Identification of Artificial Neural Network Models for Three-Dimensional Simulation of a Vibration-Acoustic Dynamic System

Industrial noise can be successfully mitigated with the combined use of passive and Active Noise Control (ANC) strategies. In a noisy area, a practical solution for noise attenuation may include both the use of baffles and ANC. When the operator is required to stay in movement in a delimited spatial area, conventional ANC is usually not able to adequately cancel the noise over the whole area. New control strategies need to be devised to achieve acceptable spatial coverage. A three-dimensional actuator model is proposed in this paper. Active Noise Control (ANC) usually requires a feedback noise measurement for the proper response of the loop controller. In some situations, especially where the real-time tridimensional positioning of a feedback transducer is unfeasible, the availability of a 3D precise noise level estimator is indispensable. In our previous works [1,2], using a vibrating signal of the primary source of noise as an input reference for spatial noise level prediction proved to be a very good choice. Another interesting aspect observed in those previous works was the need for a variable-structure linear model, which is equivalent to a sort of a nonlinear model, with unknown analytical equivalence until now. To overcome this in this paper we propose a model structure based on an Artificial Neural Network (ANN) as a nonlinear black-box model to capture the dynamic nonlinear behaveior of the investigated process. This can be used in a future closed loop noise cancelling strategy. We devise an ANN architecture and a corresponding training methodology to cope with the problem, and a MISO (Multi-Input Single-Output) model structure is used in the identification of the system dynamics. A metric is established to compare the obtained results with other works elsewhere. The results show that the obtained model is consistent and it adequately describes the main dynamics of the studied phenomenon, showing that the MISO approach using an ANN is appropriate for the simulation of the investigated process. A clear conclusion is reached highlighting the promising results obtained using this kind of modeling for ANC.

Distributed Recursive Projection Identification with Binary-Valued Observations

This paper investigates a distributed recursive projection identification problem with binaryvalued observations built on a sensor network,where each sensor in the sensor network measures partial information of the unknown parameter only,but each sensor is allowed to communicate with its neighbors.A distributed recursive projection algorithm is proposed based on a specific projection operator and a diffusion strategy.The authors establish the upper bound of the accumulated regrets of the adaptive predictor without any requirement of excitation conditions.Moreover,the convergence of the algorithm is given under the bounded cooperative excitation condition,which is more general than the previously imposed independence or persistent excitations on the system regressors and maybe the weakest one under binary observations.A numerical example is supplied to demonstrate the theoretical results and the cooperative effect of the sensors,which shows that the whole network can still fulfill the estimation task through exchanging information between sensors even if any individual sensor cannot.