FREQUENCY-DOMAIN IMPLEMENTATION OF FILTERED-X ALGORITHMS WITH ON-LINE SYSTEM IDENTIFICATION FOR VIBRATION CONTROL

This paper describes the implementation of frequency-domain least mean squares (LMS) and Filtered-X algorithms and compares the performance of the frequencydomain adaptive control algorithm to a comparable timedomain controller. When the frequency-domain LMS step size is allowed to vary as a function of frequency,the frequency-domain algorithm exhibits a better vibration reduction than the time-domain algorithm for the weaker frequencies in the energy spectrum.

Self-adaptive PID controller of microwave drying rotary device tuning on-line by genetic algorithms

The control design, based on self-adaptive PID with genetic algorithms(GA) tuning on-line was investigated, for the temperature control of industrial microwave drying rotary device with the multi-layer(IMDRDWM) and with multivariable nonlinear interaction of microwave and materials. The conventional PID control strategy incorporated with optimization GA was put forward to maintain the optimum drying temperature in order to keep the moisture content below 1%, whose adaptation ability included the cost function of optimization GA according to the output change. Simulations on five different industrial process models and practical temperature process control system for selenium-enriched slag drying intensively by using IMDRDWM were carried out systematically, indicating the reliability and effectiveness of control design. The parameters of proposed control design are all on-line implemented without iterative predictive calculations, and the closed-loop system stability is guaranteed, which makes the developed scheme simpler in its synthesis and application, providing the practical guidelines for the control implementation and the parameter design.

Modeling of Microstructure Evolution and Mechanical Properties of Steel Plates Produced by Thermo-Mechanical Control Process and Its On-line Application

An integrated metallurgical model was developed to predict microstructure evolution and mechanical properties of low-carbon steel plates produced by TMCP. The metallurgical phenomena occurring during TMCP and mechanical properties were predicted for different process parameters. In the later passes full recrystallization becomes difficult to occur and higher residual strain remains in austenite after rolling. For the reasonable temperature and cooling schedule, yield strength of 30 mm plain carbon steel plate can reach 310 MPa. The first on-line application of prediction and control of microstructure and properties (PCMP) in the medium plate production was achieved. The predictions of the system are in good agreement with measurements.

STRONGLY CONVERGENT INERTIAL FORWARD-BACKWARD-FORWARD ALGORITHM WITHOUT ON-LINE RULE FOR VARIATIONAL INEQUALITIES

This paper studies a strongly convergent inertial forward-backward-forward algorithm for the variational inequality problem in Hilbert spaces.In our convergence analysis,we do not assume the on-line rule of the inertial parameters and the iterates,which have been assumed by several authors whenever a strongly convergent algorithm with an inertial extrapolation step is proposed for a variational inequality problem.Consequently,our proof arguments are different from what is obtainable in the relevant literature.Finally,we give numerical tests to confirm the theoretical analysis and show that our proposed algorithm is superior to related ones in the literature.

DCS devices based non-linear process control system design for plants with distributed time-delay using particle filter

Remote control process system with distributed time-delay has attracted much attention in different fields.In this paper,non-linear remote control of a single tank process system with wireless network is considered.To deal with the distributed time-delay in a large-scale plant,the time-delay compensation controller based on DCS devices is designed by using operator theory and particle filter.Distributed control system(DCS)device is developed to monitor and control from the central monitoring room to each process.The particle filter is a probabilistic method to estimate unobservable information from observable information.First,remote control system and experimental equipment are introduced.Second,control system based on an operator theory is designed.Then,process system with distributed time-delay using particle filter is carried out.Finally,the actual experiment is conducted by using the proposed time-delay compensation controller.When estimating with the proposed method,the result is close to the case in which the distributed time-delay does not exist.The effectiveness of the proposed control system is confirmed by experiment results.

A New Strategy of Integrated Control and On-line Optimization on High-purity Distillation Process

For high-purity distillation processes,it is difficult to achieve a good direct product quality control using traditional proportional-integral-differential(PID)control or multivariable predictive control technique due to some difficulties,such as long response time,many un-measurable disturbances,and the reliability and precision issues of product quality soft-sensors.In this paper,based on the first principle analysis and dynamic simulation of a distillation process,a new predictive control scheme is proposed by using the split ratio of distillate flow rate to that of bottoms as an essential controlled variable.Correspondingly,a new strategy with integrated control and on-line optimization is developed,which consists of model predictive control of the split ratio,surrogate model based on radial basis function neural network for optimization,and modified differential evolution optimization algorithm. With the strategy,the process achieves its steady state quickly,so more profit can be obtained.The proposed strategy has been successfully applied to a gas separation plant for more than three years,which shows that the strategy is feasible and effective.

Transient Stability Improvement of Power System Using Non-Linear Controllers

This paper presents the design of a non-linear controller to prevent an electric power system losing synchronism after a large sudden fault and to achieve good post fault voltage level. By Direct Feedback Linearization (DFL) technique robust non-linear excitation controller is designed which will achieve stability enhancement and voltage regulation of power system. By utilizing this technique, there is a possibility of selecting various control loops for a particular application problem. This method plays an important role in control system and power system engineering problem where all relevant variables cannot be directly measured. Simulated results carried out on a single machine infinite bus power system model which shows the enhancement of transient stability regardless of the fault and changes in network parameters.

On-line current feed and computer aided control tactics for automatic balancing head

In the designed automatic balancing head, a non contact induction transformer is used to deliver driving energy to solve the problem of current fed and controlling on line. Computer controlled automatic balancing experiments with phase magnitude control tactics were performed on a flexible rotor system. Results of the experiments prove that the energy feeding method and the control tactics are effective in the automatic balancing head for vibration controlling.

Load Frequency Control of Small Hydropower Plants Using One-Input Fuzzy PI Controller with Linear and Non-Linear Plant Model

This study presents an intelligent approach for load frequency control (LFC) of small hydropower plants (SHPs). The approach which is based on fuzzy logic (FL), takes into account the non-linearity of SHPs—something which is not possible using traditional controllers. Most intelligent methods use two- input fuzzy controllers, but because such controllers are expensive, there is economic interest in the relatively cheaper single-input controllers. A non- linear control model based on one-input fuzzy logic PI (FLPI) controller was developed and applied to control the non-linear SHP. Using MATLAB/Si- mulink SimScape, the SHP was simulated with linear and non-linear plant models. The performance of the FLPI controller was investigated and compared with that of the conventional PI/PID controller. Results show that the settling time for the FLPI controller is about 8 times shorter;while the overshoot is about 15 times smaller compared to the conventional PI/PID controller. Therefore, the FLPI controller performs better than the conventional PI/PID controller not only in meeting the LFC control objective but also in ensuring increased dynamic stability of SHPs.

Application of on-line quality control for salvianolic acid B by near infrared spectroscopy

OBJECTIVE: To study and establish quality con-trol model of the Salvianolic Acid B by Near In-frared Spectroscopy (NIRS), and to realize on-line quality control of extracting and purifying proc-esses of industrial scale herbal product manu-facturing. METHOD: NIR chromatography was obtained from on-line NIR detection of extract-ing process and purifying process. HPLC analysis was carried out to determine the con-tents of salvianolic acid B. Partial Least Squares Regression (PLS) was used to establish the model between the information between NIRS and HPLC. RESULTS: For extracting model: the optimum Near Infrared (NIR) wavelength range was 9815- 5430cm-1, R=0.9784, RMSEC=0.258;for puri-fying model: the optimum NIR wavelength range was 9815-5430cm-1, R=0.9776, RMSEC=4.02. The average relative error was <5%. CONCLUSION: NIR technique is applicable for on-line quality control in production of salvianolic acid B.

Hybrid Q-learning for data-based optimal control of non-linear switching system

In this paper,the optimal control of non-linear switching system is investigated without knowing the system dynamics.First,the Hamilton-Jacobi-Bellman(HJB)equation is derived with the consideration of hybrid action space.Then,a novel data-based hybrid Q-learning(HQL)algorithm is proposed to find the optimal solution in an iterative manner.In addition,the theoretical analysis is provided to illustrate the convergence and optimality of the proposed algorithm.Finally,the algorithm is implemented with the actor-critic(AC)structure,and two linear-in-parameter neural networks are utilized to approximate the functions.Simulation results validate the effectiveness of the data-driven method.

Development and application of ocean station auto monitoring data on-line quality control system

The quality control of ocean observation data is conducted by monthly review,yet no on-line quality control system is deployed.An on-line quality control system is provided by this article.The threshold of extreme and rate of change is created by analyzing the character of observation items.The alert will be triggered to remind the operator if the system detects data out of threshold.therefore the on-line data quality control is accomplished.

An on-line constraint softening strategy to guarantee the feasibility of dynamic controller in double-layered MPC

A double-layered model predictive control(MPC), which is composed of a steady-state target calculation(SSTC)layer and a dynamic control layer, is a prevailing hierarchical structure in industrial process control. Based on the reason analysis of the dynamic controller infeasibility, an on-line constraints softening strategy is given. At first, a series of regions of attraction(ROA) of the dynamic controller is calculated according to the softened constraints;then a minimal ROA containing the current state is chosen and the corresponding softened constraint is adopted by the dynamic controller. Note that, the above measures are performed on-line because the centers of the above ROA are the steady-state targets calculated at each instant. The effectiveness of the presented strategy is illustrated through two examples.

Real-time Non-linear Target Tracking Control of Wheeled Mobile Robots

A control strategy for real-time target tracking for wheeled mobile robots is presented.Using a modified Kalman filter for environment perception,a novel tracking control law derived from Lyapunov stability theory is introduced.Tuning of linear velocity and angular velocity with mechanical constraints is applied.The proposed control system can simultaneously solve the target trajectory prediction,real-time tracking,and posture regulation problems of a wheeled mobile robot.Experimental results illustrate the effectiveness of the proposed tracking control laws.

Comparative Study of Linear and Non-Linear Controls of Three-Phase Shunt Active Filters for Improving the Quality of Electrical Energy

The aim of this work is to comparatively study two types of control of a three-phase shunt active filter (TSAF) in order to realize a system of depollution of electrical networks so as to improve the quality of electrical energy. We used two TSA control models which differ in the method of disturbed currents detection, one linear and the other non-linear. The results show that the non-linear control method, although with high calculation blocks, gives more promising results than the linear control method. When connecting a non-linear load (televisions, lamps, variable speed drives, etc.) directly to the public distribution network, the non-linear nature of the load causes the source current to be deformed due to the presence of harmonic currents and voltages. These harmonics thus generate a high reactive power, and therefore considerable electrical losses in the network. One of the solutions to reduce losses on the electricity grid is to incorporate an electronic circuit called a filter into the electricity grid. This work carried out a comparative study of two types of control of a three-phase shunt active filter (TSAF) in order to carry out a system for cleaning up electricity networks in order to improve the quality of electrical energy. We used two TSA control models that differ in the method of detecting disturbed currents, one linear and the other non-linear. The results show that the non-linear control method, although with high calculation blocks, gives better results than the linear control method.

CONTROLLABILITY OF NON-LINEAR MODELS OF INTERACTING BIOLOGICAL POPULATIONS

Inthispaper,theauthorstudiedthecontrollabilityforakindofinteractingbiologicalpopulations.whichwasconsideredasamodelonepredatortwopreysystems,theauthorformulatedandprovedthesufficientconditionsforthelocalcontrollabilityofthisdynamicalsystems.

Non-Linear Tank Level Control for Industrial Applications

Tank level control is ubiquitous in industry. The focus of this paper is on accurate liquid level control in single tank systems which can be actuated continuously and modulation of the level setpoint is also required, for example in cascade control loops or supervisory Model Predictive Control (MPC) applications. To avoid common problems encountered when using fixed gain or adaptive/gain scheduled schemes, an accurate technique based around feedback linearization and Proportional Integral (PI) control is introduced. This simple controller can maintain linear performance over the full operating range of a uniform tank. As will be demonstrated, the implementation overhead compared to a regular PI controller is negligible, making it ideal for industrial implementation. Implementation details and parameter identification for adaptive implementation are discussed. Simulations coupled with experimental results using a large-scale laboratory level control system using commercial industrial control equipment validate the approach, and illustrate its effectiveness for both level tracking and disturbance rejection.

On-Line Tuning Scheme for the Generalized Predictive Controller via Simulation Optimization

Predictive control has recently received much attention from researchers. However a challenging problem to be solved is how to tune the parameters of the predictive controller. So far, only few guidelines related to tuning of the parameters of predictive controllers have been provided in literature. In practice, these parameters are generally off-line determined by the designers' experience. From the point of view of process control, it is difficult to find out the optimal parameters for the control system based on a single quadratic performance index, which is used in the standard predictive control algorithm. The fuzzy decision-making function is investigated in this paper. Firstly, M control actions are achieved by unconstrained predictive control algorithm, and fuzzy goals and fuzzy constraints are then calculated and the global satisfaction degree is obtained by fuzzy inference. Moreover, the weighting coefficient λ in the cost function is tuned using simulation optimization according to the fuzzy criteria.

Advanced Functional Electromagnetic Shielding Materials:A Review Based on Micro‑Nano Structure Interface Control of Biomass Cell Walls

Research efforts on electromagnetic interference(EMI)shielding materials have begun to converge on green and sustainable biomass materials.These materials offer numerous advantages such as being lightweight,porous,and hierarchical.Due to their porous nature,interfacial compatibility,and electrical conductivity,biomass materials hold significant potential as EMI shielding materials.Despite concerted efforts on the EMI shielding of biomass materials have been reported,this research area is still relatively new compared to traditional EMI shielding materials.In particular,a more comprehensive study and summary of the factors influencing biomass EMI shielding materials including the pore structure adjustment,preparation process,and micro-control would be valuable.The preparation methods and characteristics of wood,bamboo,cellulose and lignin in EMI shielding field are critically discussed in this paper,and similar biomass EMI materials are summarized and analyzed.The composite methods and fillers of various biomass materials were reviewed.this paper also highlights the mechanism of EMI shielding as well as existing prospects and challenges for development trends in this field.

Adaptive Nonlinear Model Predictive Control Using an On-line Support Vector Regression Updating Strategy

The performance of data-driven models relies heavily on the amount and quality of training samples, so it might deteriorate significantly in the regions where samples are scarce. The objective of this paper is to develop an online SVR model updating strategy to track the change in the process characteristics efficiently with affordable computational burden. This is achieved by adding a new sample that violates the Karush–Kuhn–Tucker conditions of the existing SVR model and by deleting the old sample that has the maximum distance with respect to the newly added sample in feature space. The benefits offered by such an updating strategy are exploited to develop an adaptive model-based control scheme, where model updating and control task perform alternately.The effectiveness of the adaptive controller is demonstrated by simulation study on a continuous stirred tank reactor. The results reveal that the adaptive MPC scheme outperforms its non-adaptive counterpart for largemagnitude set point changes and variations in process parameters.