Formalizing the General Plant for Development of Hybrid Process Control Systems

Many process control systems are a kind of hybrid systems. In order to develop a satisfied control strategy, i. e., to make the whole system satisfy some processing requirements, the knowledge of plant is indispensable. This paper proposes a formal model for the general plant for a kind of process control systems. Based on the model, requirements for the system can be specified from goals, and the controller can be designed according to plant based formal approach . An industrial process control system is used to illustrate our models and methods. Duration Calculus, a real time interval logic, is utilized to specify some characters of the model and development of control program for the exemplified system.

Removal of Agricultural Non-Point Source Pollutants by Artificial Aquatic Food Web System: A Study Case of the Control of Cynobacterial Bloom in Jiyu River

An artificial aquatic food web (AAFW) system was designed to remove the non-point source pollutants in eutrophic Jiyu river. A certain amount of Scenedesmus obliquus and Daphnia pulex was cultured in the system for the control of serious cyanobacterial bloom. The AAFW system was a continuous-flow system including one storage basin of 3 m3 capacity with polluted river water (the total nitrogen-TN: 4.49 mg⋅l-1;the total phosphorus-TP: 0.192 mg⋅l-1), one phytoplankton tank of 3 m3 capacity with an initial concentrations of S. obliquus about 5.8 × 103 ind⋅l-1, and one zooplankton growth chamber of 1.5 m3 capacity with an initial abundance of D. pulex about 22.5 ind⋅l-1. The system was optimized by setting hydraulic retention time of phytoplankton tank as 5 days and the experiments were operated for 45 days. Compared with the polluted river, TN and TP were removed about 28% and 47% by the AAFW system, respectively. The biomass of phytoplankton decrease from 6.33 mg⋅l-1 to 1.48 mg⋅l-1 and the percentage of cyanobacteria decrease from 43.93% to 2.36%, the biomass of Crustacean zooplankton increase from 0.34 mg⋅l-1 to 1.53 mg⋅l-1 and the percentage of D. pulex increase from 19.19% to 57.62%. Our results indicated that the AAFW system not only is an efficient, flexible system for reducing nutrient levels in tributary rivers, but also has an ability to control the cyanobacteria bloom and rebuilding the aquatic ecosystem from the polluted river water.

Multiobjective optimization and multivariable control of the beer fermentation process with the use of evolutionary algorithms

This paper describes empirical research on the model, optimization and supervisory control of beer fermentation.Conditions in the laboratory were made as similar as possible to brewery industry conditions. Since mathematical models that consider realistic industrial conditions were not available, a new mathematical model design involving industrial conditions was first developed. Batch fermentations are multiobjective dynamic processes that must be guided along optimal paths to obtain good results.The paper describes a direct way to apply a Pareto set approach with multiobjective evolutionary algorithms (MOEAs).Successful finding of optimal ways to drive these processes were reported.Once obtained, the mathematical fermentation model was used to optimize the fermentation process by using an intelligent control based on certain rules.

Design and Analysis of Integrated Predictive Iterative Learning Control for Batch Process Based on Two-dimensional System Theory

Based on the two-dimensional （2D） system theory, an integrated predictive iterative learning control （2D-IPILC） strategy for batch processes is presented. First, the output response and the error transition model predictions along the batch index can be calculated analytically due to the 2D Roesser model of the batch process. Then, an integrated framework of combining iterative learning control （ILC） and model predictive control （MPC） is formed reasonably. The output of feedforward ILC is estimated on the basis of the predefined process 2D model. By min- imizing a quadratic objective function, the feedback MPC is introduced to obtain better control performance for tracking problem of batch processes. Simulations on a typical batch reactor demonstrate that the satisfactory tracking performance as well as faster convergence speed can be achieved than traditional proportion type （P- t-we） ILC despite the model error and disturbances.

Optimal Production Control of Hybrid Manufacturing/Remanufacturing Failure-Prone Systems under Diffusion-Type Demand

The problem of production control for a hybrid manufacturing/remanufacturing system under uncertainty is analyzed. Two sources of uncertainty are considered: machines are subject to random breakdowns and repairs, and demand level is modeled as a diffusion type stochastic process. Contrary to most of studies where the demand level is considered constant and fewer results where the demand is modeled as a Poisson process with few discrete levels and exponentially distributed switching time, the demand is modeled here as a diffusion type process. In particular Wiener and Ornstein-Uhlenbeck processes for cumulative demands are analyzed. We formulate the stochastic control problem and develop optimality conditions for it in the form of Hamilton-Jacobi-Bellman (HJB) partial differential equations (PDEs). We demonstrate that HJB equations are of the second order contrary to the case of constant demand rate (corresponding to the average demand in our case), where HJB equations are linear PDEs. We apply the Kushner-type finite difference scheme and the policy improvement procedure to solve HJB equations numerically and show that the optimal production policy is of hedging-point type for both demand models we have introduced, similarly to the known case of a constant demand. Obtained results allow to compute numerically the optimal production policy in hybrid manufacturing/ remanufacturing systems taking into account the demand variability, and also show that Kushner-type discrete scheme can be successfully applied for solving underlying second order HJB equations.

Energy-saving design and optimization of pressure-swing-assisted ternary heterogenous azeotropic distillations

A huge amount of energy is always consumed to separate the ternary azeotropic mixtures by distillations.The heterogeneous azeotropic distillation and the pressure-swing distillation are two kinds of effective technologies to separate heterogeneous azeotropes without entrainer addition.To give better play to the synergistic energy-saving effect of these two processes,a novel pressure-swing-assisted ternary heterogeneous azeotropic distillation(THAD)process is proposed firstly.In this process,the ternary heterogeneous azeotrope is decanted into two liquid phases before being refluxed into the azeotropic distillation column to avoid the aqueous phase remixing,and three columns'pressures are modified to decrease the flowrates of the recycle streams.Then the dividing wall column and heat integration technologies are introduced to further reduce its energy consumption,and the pressureswing-assisted ternary heterogeneous azeotropic dividing-wall column and its heat integration structure are achieved.A genetic algorithm procedure is used to optimize the proposed processes.The design results show that the proposed processes have higher energy efficiencies and lower CO_(2)emissions than the published THAD process.

A Practical Study of Intelligent Image-Based Mobile Robot for Tracking Colored Objects

Object tracking is one of the major tasks for mobile robots in many real-world applications.Also,artificial intelligence and automatic control techniques play an important role in enhancing the performance of mobile robot navigation.In contrast to previous simulation studies,this paper presents a new intelligent mobile robot for accomplishing multi-tasks by tracking red-green-blue(RGB)colored objects in a real experimental field.Moreover,a practical smart controller is developed based on adaptive fuzzy logic and custom proportional-integral-derivative(PID)schemes to achieve accurate tracking results,considering robot command delay and tolerance errors.The design of developed controllers implies some motion rules to mimic the knowledge of experienced operators.Twelve scenarios of three colored object combinations have been successfully tested and evaluated by using the developed controlled image-based robot tracker.Classical PID control failed to handle some tracking scenarios in this study.The proposed adaptive fuzzy PID control achieved the best accurate results with the minimum average final error of 13.8 cm to reach the colored targets,while our designed custom PID control is efficient in saving both average time and traveling distance of 6.6 s and 14.3 cm,respectively.These promising results demonstrate the feasibility of applying our developed image-based robotic system in a colored object-tracking environment to reduce human workloads.

Application of an expert system using neural network to control the coagulant dosing in water treatment plant

The coagulation process is one of the most important stages in water treatment plant, which involves many complex physical and chemical phenomena. Moreover, coagulant dosing rate is non-linearly correlated to raw water characteristics such as turbidity, conductivity, PH, temperature, etc. As such, coagulation reaction is hard or even impossible to control satisfactorily by conventional methods. Based on neural network and rule models, an expert system for determining the optimum chemical dosage rate is developed and used in a water treatment work, and the results of actual runs show that in the condition of satisfying the demand of drinking water quality, the usage of coagulant is lowered.

Computation Method for the Real-Time Control Problems in Differential-Algebraic Systems

In this paper, a real-time computation method for the control problems in differential-algebraic systems is presented. The errors of the method are estimated, and the relation between the sampling stepsize and the controlled errors is analyzed. The stability analysis is done for a model problem, and the stability region is ploted which gives the range of the sampling stepsizes with which the stability of control process is guaranteed.

Stability and Stabilization of Networked Control Systems with Bounded Packet Dropout

在这份报纸，与围住的包退学学生一起的联网的控制系统(NCS ) 的一个类的稳定性和稳定问题被调查。一条反复的途径被建议作为 Markovian 与围住的包退学学生一起为 NCS 建模跳线性系统(MJLS ) 。MJLS 的转变可能性由于网络的复杂性是部分未知的。在考虑下面的系统是更一般的，它完全用盖住系统知道并且完全未知的转变可能性作为二个空间案例。而且， sensor-to-controller 和 controller-to-actuator 包退学学生同时被考虑。为内在的系统的随机的稳定性和稳定的足够的条件经由线性矩阵不平等(LMI ) 被导出明确的表达。最后，二个解说性的例子被给表明建议结果的有效性。

EXPERT SYSTEM FOR CONTROLLING THE STATE OF SINTERING PROCESS

In order to control sintering process,improve permeability and stabilize burn through point, a control scheme which combines thermal state with permeability state is proposed, and an expert system for controlling sintering process state is developed, the software which includes about 1000 expert rules is successfully applied to off line control of sintering process.

Feeding Control System of Conveyor-belt Based on Image Processing

Based on the real time measurement of the width of coal flow, the method for measuring the width and the relative position of coal flow on a conveyor-belt by image processing was presented. A feeding control system of conveyor-belt was proposed using a fuzzy controller. This control system consists of CCD camera, universal image sampling system, control network and executor. The result shows that the algorithm used in the image processing is simple and efficient, and the measuring error of width is less than 4%.

Scheduling and control of failure prone systems with demand uncertainty and job overlaps

Given that the overlapping of jobs is permitted, the paper studies the scheduling and control of failure prone production systems, i.e. so-called settings with demand uncertainty and job overlaps. Because a variable demand resource is involved in the production and corrective maintenance control problems of the system, which switched randomly between zero and a maximum level, it is difficult to obtain the analytical solutions of the optimal single hedging point policy. An asymptotic optimal scheduling policy is presented and a double hedging point policy is offered to control simultaneously the production rate and the corrective maintenance rate of the system. The corresponding analytical solutions and approximate solutions are obtained. Considering the relationship of production, corrective maintenance and demand variable, an approximate optimal single hedging point control policy is proposed. Numerical results are presented.

Stability in Probability and Inverse Optimal Control of Evolution Systems Driven by Levy Processes

This paper first develops a Lyapunov-type theorem to study global well-posedness(existence and uniqueness of the strong variational solution)and asymptotic stability in probability of nonlinear stochastic evolution systems(SESs)driven by a special class of Levy processes,which consist of Wiener and compensated Poisson processes.This theorem is then utilized to develop an approach to solve an inverse optimal stabilization problem for SESs driven by Levy processes.The inverse optimal control design achieves global well-posedness and global asymptotic stability of the closed-loop system,and minimizes a meaningful cost functional that penalizes both states and control.The approach does not require to solve a Hamilton-Jacobi-Bellman equation(HJBE).An optimal stabilization of the evolution of the frequency of a certain genetic character from the population is included to illustrate the theoretical developments.

Implementation of Control Algorithms in Small Embedded Systems

This work describes how a control algorithm can be implemented in a small (8-bit) microcontroller for the main purpose of merging embedded systems and control theory in electrical engineering undergraduate classes. Two different methods for discretizing the control expression are compared: Euler transformation and bilinear transformation. The sampling rate’s impact on the algorithm is discussed and theoretical results are verified by an application to a temperature control system in a heating plant. Four control algorithms are compared: PID and PI algorithms discretized with Euler and bilinear transformation, respectively. It is shown that for the heating plant used in this work, a bilinear PI algorithm implemented in a small 8-bit microcontroller outperforms a commercial controller from Panasonic. It is also demonstrated that all the derived algorithms can be implemented using integer calculations only, obviating the need for expensive and time-consuming floating-point calculations. This work bridges the gap between control theory equations and the implementation of control systems in small embedded systems with no inherent floating-point processing power.

基于EtherCAT通信的双CPU总线型柔性折弯加工中心专用数控系统研发

面向柔性折弯加工中心控制的低成本、便捷性、功能集成与定制化需求,提出并开发了一款基于EtherCAT通信的双CPU总线型柔性折弯加工中心专用数控系统。首先,通过分析十轴柔性折弯加工中心成型原理,基于EtherCAT通信协议,提出了一种“PC+运动控制卡”的双CPU总线型控制模式,确定了数控系统的硬件架构;然后,采用C#编程语言,完成了专用数控系统软件开发,重点解决了板材精确定位、考虑回弹的折弯控制、人机交互界面优化等关键问题;最后,开展了加工测试和实验验证。结果表明,开发的专用数控系统加工角度误差范围小于±4 5′,直线度间隙误差小于0.3 mm,符合钣金行业GB/T 33644-2017标准,且人机交互性友好、功能集成度高、运行稳定,能够很好地满足实际生产需要。

MDL-based root-mean-squared delay spread estimation for MIMO OFDM systems

This paper studied the application of minimum description length (MDL) criterion for estimating root-mean-squared (RMS) delay spread (RDS) for MIMO OFDM systems. The analytic relationship between the powers and the correlation matrix of multipath components established the feasibility of the application of the MDL criterion to RDS estimation. The estimator presented both the estimate of instantaneous RDS and the estimates of noise variance, channel power and SNR of current channel with low computational complexity. Given the powers of the estimated multipath components, the MDL criterion was adopted to acquire the number of paths and the time delays of each path of current channel without making eigendecomposition of the correlation matrix normally required by MDL criterion, following which the noise variance and the power of each path can be estimated. The power delay profile (PDP) and RDS of the current channel were achieved. Simulation results showed that the proposed estimator was insensitive to variance of SNR and robust against frequency-selectivity.

Robust H_∞ Control for Uncertain Markovian Jump Linear Time-Delay Systems

This paper studies the robust stochastic stabilization and robust H∞ control for linear time-delay systems with both Markovian jump parameters and unknown norm-bounded parameter uncertainties. This problem can be solved on the basis of stochastic Lyapunov approach and linear matrix inequality (LMI) technique. Sufficient conditions for the existence of stochastic stabilization and robust H∞ state feedback controller are presented in terms of a set of solutions of coupled LMIs. Finally, a numerical example is included to demonstrate the practicability of the proposed methods.

RUN TO RUN CONTROL OF TIME-PRESSURE DISPENSING SYSTEM

In electronics packaging the time-pressure dispensing system is widely usedto squeeze the adhesive fluid in a syringe onto boards or substrates with the pressurized air.However, complexity of the process, which includes the air-fluid coupling and the nonlinearuncertainties, makes it difficult to have a consistent process performance. An integrated dispensingprocess model is first introduced and then its input-output regression relationship is used todesign a run to run control methodology for this process. The controller takes EWMA scheme and itsstability region is given. Experimental results verify the effectiveness of the proposed run to runcontrol method for dispensing process.

A review of control loop monitoring and diagnosis:Prospects of controller maintenance in big data era

Owing to wide applications of automatic control systems in the process industries, the impacts of controller performance on industrial processes are becoming increasingly significant. Consequently, controller maintenance is critical to guarantee routine operations of industrial processes. The workflow of controller maintenance generally involves the following steps: monitor operating controller performance and detect performance degradation, diagnose probable root causes of control system malfunctions, and take specific actions to resolve associated problems. In this article, a comprehensive overview of the mainstream of control loop monitoring and diagnosis is provided, and some existing problems are also analyzed and discussed. From the viewpoint of synthesizing abundant information in the context of big data, some prospective ideas and promising methods are outlined to potentially solve problems in industrial applications.