Improved quantum bacterial foraging algorithm for tuning parameters of fractional-order PID controller

The quantum bacterial foraging optimization(QBFO)algorithm has the characteristics of strong robustness and global searching ability. In the classical QBFO algorithm, the rotation angle updated by the rotation gate is discrete and constant,which cannot affect the situation of the solution space and limit the diversity of bacterial population. In this paper, an improved QBFO(IQBFO) algorithm is proposed, which can adaptively make the quantum rotation angle continuously updated and enhance the global search ability. In the initialization process, the modified probability of the optimal rotation angle is introduced to avoid the existence of invariant solutions. The modified operator of probability amplitude is adopted to further increase the population diversity.The tests based on benchmark functions verify the effectiveness of the proposed algorithm. Moreover, compared with the integerorder PID controller, the fractional-order proportion integration differentiation(PID) controller increases the complexity of the system with better flexibility and robustness. Thus the fractional-order PID controller is applied to the servo system. The tuning results of PID parameters of the fractional-order servo system show that the proposed algorithm has a good performance in tuning the PID parameters of the fractional-order servo system.

Optimal switching policy for performance enhancement of distributed parameter systems based on event-driven control

This paper aims to improve the performance of a class of distributed parameter systems for the optimal switching of actuators and controllers based on event-driven control. It is assumed that in the available multiple actuators, only one actuator can receive the control signal and be activated over an unfixed time interval, and the other actuators keep dormant.After incorporating a state observer into the event generator, the event-driven control loop and the minimum inter-event time are ultimately bounded. Based on the event-driven state feedback control, the time intervals of unfixed length can be obtained. The optimal switching policy is based on finite horizon linear quadratic optimal control at the beginning of each time subinterval. A simulation example demonstrate the effectiveness of the proposed policy.

An Improved Cooperative Team Spraying Control of a Diffusion Process With a Moving or Static Pollution Source

This paper is concerned with a control problem of a diffusion process with the help of static mesh sensor networks in a certain region of interest and a team of networked mobile actuators carrying chemical neutralizers.The major contribution of this paper can be divided into three parts:the first is the construction of a cyber-physical system framework based on centroidal Voronoi tessellations(CVTs),the second is the convergence analysis of the actuators location,and the last is a novel proportional integral(PI)control method for actuator motion planning and neutralizing control(e.g.,spraying)of a diffusion process with a moving or static pollution source,which is more effective than a proportional(P)control method.An optimal spraying control cost function is constructed.Then,the minimization problem of the spraying amount is addressed.Moreover,a new CVT algorithm based on the novel PI control method,henceforth called PI-CVT algorithm,is introduced together with the convergence analysis of the actuators location via a PI control law.Finally,a modified simulation platform called diffusion-mobile-actuators-sensors-2-dimension-proportional integral derivative(Diff-MAS2D-PID)is illustrated.In addition,a numerical simulation example for the diffusion process is presented to verify the effectiveness of our proposed controllers.

Coverage-optimization based guidance of mobile agents for improved control of distributed parameter systems

The control problem of a class of parabolic distributed parameter systems (DPSs) is investigated by using mobile agents with capabilities of sensing and actuating. The guidance strategies of mobile agents based on coverage optimization methods are proposed to improve the control performance of the system and make the state norm of the system converge to zero faster. The coverage optimization problems are constructed based on the measurement of each agent. By solving the coverage optimization problems, the local optimal moving direction of each agent can be obtained. Then the gradient-based agent motion control laws are established. With the indicator function and the surface delta function, this method is generalized to n-dimensional space, and suitable for any sensing region with piecewise smooth boundaries. The stability and control performance of the system are analyzed. Numerical simulations show the effectiveness of the proposed methods.

Introduction to the Special Issue on Advances on Modeling and State Estimation for Industrial Processes

In the past few years,significant progress has been made in modeling and state estimation for industrial processes to improve control performance,reliable monitoring,quick and accurate fault detection,diagnosis,high product quality,fule and resource consumption,etc.However,with the fast development of information technology,numerous essential issues are faced in modeling and state estimation,which generates the new need for novel modeling and or state estimation methodologies and in-depth studies of them.Therefore,this special issue is dedicated to innovative modeling and state estimation from applicability,computational efficiency,and effectiveness.

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.

State-dependent event-triggered control of multi-agent systems

Event-triggered control has been recently proposed as an effective strategy for the consensus of multi-agent systems.We present an improved distributed event-triggered control scheme that remedies a shortcoming of some previous eventtriggered control schemes in the literature. This improved distributed event-triggered method has no need for continuously monitoring each agent' neighbors. Moreover, each agent in the multi-agent systems will not exhibit the Zeno behavior.Numerical simulation results show the effectiveness of the proposed consensus control.

Enhancing the Effectiveness of Trimethylchlorosilane Purification Process Monitoring with Variational Autoencoder

In modern industry,process monitoring plays a significant role in improving the quality of process conduct.With the higher dimensional of the industrial data,the monitoring methods based on the latent variables have been widely applied in order to decrease the wasting of the industrial database.Nevertheless,these latent variables do not usually follow the Gaussian distribution and thus perform unsuitable when applying some statistics indices,especially the T^(2) on them.Variational AutoEncoders(VAE),an unsupervised deep learning algorithm using the hierarchy study method,has the ability to make the latent variables follow the Gaussian distribution.The partial least squares(PLS)are used to obtain the information between the dependent variables and independent variables.In this paper,we will integrate these two methods and make a comparison with other methods.The superiority of this proposed method will be verified by the simulation and the Trimethylchlorosilane purification process in terms of the multivariate control charts.

Bayesian Filtering for High-Dimensional State-Space Models With State Partition and Error Compensation

In the era of exponential growth of data availability,the architecture of systems has a trend toward high dimensionality,and directly exploiting holistic information for state inference is not always computationally affordable.This paper proposes a novel Bayesian filtering algorithm that considers algorithmic computational cost and estimation accuracy for high-dimensional linear systems.The high-dimensional state vector is divided into several blocks to save computation resources by avoiding the calculation of error covariance with immense dimensions.After that,two sequential states are estimated simultaneously by introducing an auxiliary variable in the new probability space,mitigating the performance degradation caused by state segmentation.Moreover,the computational cost and error covariance of the proposed algorithm are analyzed analytically to show its distinct features compared with several existing methods.Simulation results illustrate that the proposed Bayesian filtering can maintain a higher estimation accuracy with reasonable computational cost when applied to high-dimensional linear systems.

Modeling and Fault Monitoring of Bioprocess Using Generalized Additive Models （GAMs） and Bootstrap

差错监视 bioprocess 是重要的保证一个反应堆的安全并且维持产品的高质量。为 bioprocess 造一个精确机械学的模型是困难的，因此差错基于富有的历史或联机的数据库监视是一个有效方法。一组数据基于自举方法能随机地是采样，改善归纳能力当模特儿。在这份报纸，在网上指责与结合的概括添加剂模型(鲸鱼群) 监视自举为 glutamate 被建议发酵过程。鲸鱼群并且自举首先被用来从 glutamate 发酵实验基于联机、离线的正常取样的数据决定信心间隔。然后，鲸鱼群被用来在网上指责在时间，溶解的氧，氧举起率，和二氧化碳进化监视率。方法能在网上提供精确差错警报并且是有用的为在发酵移开差错和反常现象提供有用信息。

Estimation of spatially distributed processes using mobile sensor networks with missing measurements

This paper investigates the estimation problem for a spatially distributed process described by a partial differential equation with missing measurements.The randomly missing measurements are introduced in order to better reflect the reality in the sensor network.To improve the estimation performance for the spatially distributed process,a network of sensors which are allowed to move within the spatial domain is used.We aim to design an estimator which is used to approximate the distributed process and the mobile trajectories for sensors such that,for all possible missing measurements,the estimation error system is globally asymptotically stable in the mean square sense.By constructing Lyapunov functionals and using inequality analysis,the guidance scheme of every sensor and the convergence of the estimation error system are obtained.Finally,a numerical example is given to verify the effectiveness of the proposed estimator utilizing the proposed guidance scheme for sensors.

Forward Current Transport Mechanism and Schottky Barrier Characteristics of a Ni/Au Contact on n-GaN

The forward current transport mechanism and Schottky barrier characteristics of a Ni/Au contact on n-GaN are studied by using temperature-dependent current-voltage(T–I–V)and capacitance-voltage(C–V)measurements.The low-forward-bias I–V curve of the Schottky junction is found to be dominated by trap-assisted tunneling below 400 K,and thus can not be used to deduce the Schottky barrier height(SBH)based on the thermionic emission(TE)model.On the other hand,TE transport mechanism dominates the high-forward-bias region and a modified I–V method is adopted to deduce the effective barrier height.It is found that the estimated SBH(~0.95 eV at 300 K)by the I–V method is~0.20 eV lower than that obtained by the C–V method,which is explained by a barrier inhomogeneity model over the Schottky contact area.

Improved control for distributed parameter systems with time-dependent spatial domains utilizing mobile sensor-actuator networks

A guidance policy for controller performance enhancement utilizing mobile sensor–actuator networks(MSANs) is proposed for a class of distributed parameter systems(DPSs), which are governed by diffusion partial differential equations(PDEs) with time-dependent spatial domains. Several sufficient conditions for controller performance enhancement are presented. First, the infinite dimensional operator theory is used to derive an abstract evolution equation of the systems under some rational assumptions on the operators, and a static output feedback controller is designed to control the spatial process. Then, based on Lyapunov stability arguments, guidance policies for collocated and non-collocated MSANs are provided to enhance the performance of the proposed controller, which show that the time-dependent characteristic of the spatial domains can significantly affect the design of the mobile scheme. Finally, a simulation example illustrates the effectiveness of the proposed policy.

Iterative Learning Control for Distributed Parameter Systems Based on Non-Collocated Sensors and Actuators

In this paper, an open-loop PD-type iterative learning control(ILC) scheme is first proposed for two kinds of distributed parameter systems(DPSs) which are described by parabolic partial differential equations using non-collocated sensors and actuators. Then, a closed-loop PD-type ILC algorithm is extended to a class of distributed parameter systems with a non-collocated single sensor and m actuators when the initial states of the system exist some errors. Under some given assumptions, the convergence conditions of output errors for the systems can be obtained. Finally, one numerical example for a distributed parameter system with a single sensor and two actuators is presented to illustrate the effectiveness of the proposed ILC schemes.

Improved High Order Model-Free Adaptive Iterative Learning Control with Disturbance Compensation and Enhanced Convergence

In this paper,an improved high-order model-free adaptive iterative control(IHOMFAILC)method for a class of nonlinear discrete-time systems is proposed based on the compact format dynamic linearization method.This method adds the differential of tracking error in the criteria function to compensate for the effect of the random disturbance.Meanwhile,a high-order estimation algorithmis used to estimate the value of pseudo partial derivative(PPD),that is,the current value of PPD is updated by that of previous iterations.Thus the rapid convergence of the maximumtracking error is not limited by the initial value of PPD.The convergence of the maximumtracking error is deduced in detail.This method can track the desired output with enhanced convergence and improved tracking performance.Two examples are used to verify the convergence and effectiveness of the proposed method.

State Estimation Moving Window Gradient Iterative Algorithm for Bilinear Systems Using the Continuous Mixed p-norm Technique

This paper studies the parameter estimation problems of the nonlinear systems described by the bilinear state space models in the presence of disturbances.A bilinear state observer is designed for deriving identification algorithms to estimate the state variables using the input-output data.Based on the bilinear state observer,a novel gradient iterative algorithm is derived for estimating the parameters of the bilinear systems by means of the continuous mixed p-norm cost function.The gain at each iterative step adapts to the data quality so that the algorithm has good robustness to the noise disturbance.Furthermore,to improve the performance of the proposed algorithm,a dynamicmoving window is designed which can update the dynamical data by removing the oldest data and adding the newestmeasurement data.A numerical example of identification of bilinear systems is presented to validate the theoretical analysis.

Fault detection and diagnosis for data incomplete industrial systems with new Bayesian network approach

For the fault detection and diagnosis problem in largescale industrial systems,there are two important issues: the missing data samples and the non-Gaussian property of the data.However,most of the existing data-driven methods cannot be able to handle both of them.Thus,a new Bayesian network classifier based fault detection and diagnosis method is proposed.At first,a non-imputation method is presented to handle the data incomplete samples,with the property of the proposed Bayesian network classifier,and the missing values can be marginalized in an elegant manner.Furthermore,the Gaussian mixture model is used to approximate the non-Gaussian data with a linear combination of finite Gaussian mixtures,so that the Bayesian network can process the non-Gaussian data in an effective way.Therefore,the entire fault detection and diagnosis method can deal with the high-dimensional incomplete process samples in an efficient and robust way.The diagnosis results are expressed in the manner of probability with the reliability scores.The proposed approach is evaluated with a benchmark problem called the Tennessee Eastman process.The simulation results show the effectiveness and robustness of the proposed method in fault detection and diagnosis for large-scale systems with missing measurements.

Event-triggered dynamic output-feedback control for a class of Lipschitz nonlinear systems

This paper investigates the problem of dynamic output-feedback control for a class of Lipschitz nonlinear systems.First,a continuous-time controller is constructed and sufficient conditions for stability of the nonlinear systems are presented.Then,a novel event-triggered mechanism is proposed for the Lipschitz nonlinear systems in which new event-triggered conditions are introduced.Consequently,a closed-loop hybrid system is obtained using the event-triggered control strategy.Sufficient conditions for stability of the closed-loop system are established in the framework of hybrid systems.In addition,an upper bound of a minimum inter-event interval is provided to avoid the Zeno phenomenon.Finally,numerical examples of a neural network system and a genetic regulatory network system are provided to verify the theoretical results and to show the superiority of the proposed method.