Feedforward control of glass mold colling

In the manufacture of glass containers, good temperature regulation of the forming molds is essential for a high production yield. However daily and hourly temperature fluctuations of the cooling air, drawn from outside the glass plant, upsets the temperature stability. A 1°F change in the cooling air results in a 1.4°F change in the formed glass if air flow is unchanged. Since continuous mold or glass temperature measurements are impractical under production conditions, a feedforward control system, based upon a mathematical model of mold cooling and supported by experimentation, was designed to modulate the air flow to maintain mold cooling conditions. The design is implemented using a pressure controller manipulating a valve (or blower louver), with the controller setpoint computed from the air temperature. If the computed pressure exceeds a preset limit, due to temperature extremes, an override control function changes the glass feeder temperature controller setpoint and/or the machine speed instead of pressure. In the first installation, the feedforward control system paid for itself in several days. Since then, two years of operation at one plant has shown that two containers with production yields previously at 80–82% increased an average 7.6% and six containers with production yields at 90–92% increased an average 2.3%.     

H∞ state-feedback control of time-delay systems using reciprocally convex approach

In this paper, state-feedback H_∞ control of systems with time-varying delay is considered. The time-delay is assumed to be a time-varying continuous function which is bounded below and above by positive constants. The reciprocally convex approach based on linear matrix inequality (LMI) optimization is adapted to design the H_∞ state feedback control law. For the illustrative examples the physical model of liquid monopropellant rocket motor with a pressure feeding system and the problem of controlling the yaw angles of a satellite system with delays are considered along with their numerical simulations to show effectiveness of derived results. Furthermore few other numerical examples are also given to illustrate the advantages of the derived results.     

三相异步电动机电源保护模块的设计

介绍一种实用的三相电源保护电路模块。该模块对断相、三相电压不平衡、相序接反等故障均能检测。电路采用全电子电路实现,性能稳定可靠、价格低廉,可以直接作为三相异步电动机的保护模块或者继电接触器的控制组件,具有较高的实用价值。     

Load compensation based on frame considering low-order dominant harmonics and distorted power system

In this paper, by considering voltage distortion and frequency variation, the control strategy of synchronous reference frame (SRF) for three-phase shunt active power filter has been improved. In the SRF compensation method a conventional low pass filter (CLPF) is used to extract the dc component of the d-axis current (I_d). Unfortunately in the presence of low-order harmonics of the load current (second and third ones), the transient response time is increased. If the amplitude of these harmonics are high, the distortions in CLPF output signal are increased, and as a result, the desired compensation cannot be achieved. In this paper to overcome this problem, a novel numerical LPF is designed and implemented based on variable forgetting factor-recursive least squares (VFF-RLS). The advantages of the proposed filter over CLPFs include independence of the load current harmonic components, fast dynamic and high accuracy of the response. Moreover, due to the malfunction of the conventional phase locked loop (PLL) in polluted and variable frequency environment, a second order generation integrator-frequency locked loop (SOGI-FLL) based on fuzzy logic controller (FLC) and wavelet transform (WT) is proposed. Effectiveness of the proposed method is evaluated with both simulations and experimental results in a three-phase power system.     

Assessment of the temperature and pressure dependence of molar volume and phase diagrams of Cu and Zn

This study reports the thermodynamic assessment of the temperature and pressure dependence of molar volume of Cu and Zn, as well as their P–T phase diagrams based on the CALPHAD approach. The molar volume of fcc-Cu, hcp-Zn, and liquid phases as a function of both temperature and pressure was directly assessed from the data reported in the literature by implementing the CALPHAD-compatible equation of state (EOS). This EOS incorporates the quasi-harmonic model to eliminate the spurious predictions of negative heat capacity and thermal expansion at high pressure. Furthermore, the P–T phase diagram of each element was determined on the basis of the experimental results and calculated parameters. The experimentally estimated thermodynamic values are in good agreement with the calculated results.     

A robust adaptive fuzzy variable structure tracking control for the wheeled mobile robot: Simulation and experimental results

In this paper an adaptive fuzzy variable structure control (kinematic control) integrated with a proportional plus derivative control (dynamic control) is proposed as a robust solution to the trajectory tracking control problem for a differential wheeled mobile robot. The variable structure controller, based on the sliding mode theory, is a well known, proven control method, fit to deal with uncertainties and disturbances (e.g., structural and parameter uncertainties, external disturbances and operating limitations). To minimize the problems found in practical implementations of the classical variable structure controllers, an adaptive fuzzy logic controller replaces the discontinuous portion of the control signals (avoiding the chattering), causing the loss of invariance, but still ensuring the robustness to uncertainties and disturbances without having any a priori knowledge of their boundaries. Moreover, the adaptive fuzzy logic controller is a feasible tool to approximate any real continuous nonlinear system to arbitrary accuracy, and has a simple structure by using triangular membership functions, a low number of rules that must be evaluated, resulting in a lower computational load for execution, making it feasible for real time implementation. Stability analysis and the convergence of tracking errors as well as the adaptation laws are guaranteed with basis on the Lyapunov theory. Simulation and experimental results are explored to show the verification and validation of the proposed control strategy.     

Peaking free variable structure control of uncertain linear systems based on a high-gain observer

An output-feedback model-reference variable structure controller based on a high-gain observer (HGO) is proposed and analyzed. For single-input-single-output (SISO) linear plants with relative degree greater than one, the control law is generated using the HGO signals only to drive the sign function of the variable structure control component while the sign function gain, also called modulation, as well as the other components of the control signal are generated using signals from state variable filters which do not require high gain and are free of peaking. This scheme achieves global exponential stability with respect to a small residual set and does not generate peaking in the control signal.     

Implementing the induction-motor drive with four-switch inverter: An application of neural networks

This paper reports a four switch based three-phase voltage source inverter using space vector pulse width modulation (SVPWM), and designed with a three-layer feed forward back propagation based artificial neural network (ANN). The input–output samples, obtained using simulations in Matlab Simulink, were used for the extensive training of the neural network. Matlab interface with National Instruments’ NI-USB-6259 BNC was used for implementing the designed scheme with calculated weights and biases. The designed ANN based SVPWM model receives command voltage and reference speed as the inputs and generates pulse width modulated waves for the four-switch three-phase inverter bridge. The V/f ratio can be controlled by controlling the input parameters of the ANN generating PWM pulses. The simulations and experimental results, and harmonic analysis with the designed ANN structure are presented at different base speeds. The designed model was tested in under modulation, over modulation and unity modulation mode of operation and tuned to give minimum total harmonic distortion. Harmonic results at different modulation indexes are also presented. The ANN based implementation reduces the complexity of control system and overall cost reduction is achieved by the combination of FSTPI and ANN.     

高温压力控制器的设计

介绍了机械式高温压力控制器的设计方法;阐述了它的工作原理、结构特点和主要部件的设计与计算。同时,对宽温区压力控制器压力设定点的温度漂移进行了补偿和实验验证。比较了电子式和机械式压力控制器各自的优缺点,并提出了选择设计方案的原则。该压力控制器的压力设定点为0.3MPa;误差为±0.04MPa(全温区);工作温度为-55～150℃。实验表明:该高温压力控制器体积小、耐恶劣环境、可靠性高,满足使用要求。     

主要通风机远程监测系统的设计

针对现有的矿井主要通风机运行参数检测方式存在精度低、实时性差、无法实现全面监测的问题,提出了一种主要通风机远程监测系统的设计方案。该系统以C8051F020单片机为控制核心,通过数据采集模块采集主要通风机风压、风量、三相电压、三相电流、有功功率、效率、振动、电动机轴承温度等参数,对参数进行处理后实时显示在本地液晶屏上,并可通过GPRS网络将参数传输至上位机进行显示。试运行结果表明,该系统未出现错报及掉线现象。     

A modified method of calculating High Dimensional Model Representation (HDMR) Terms for parallelization with MPI and CUDA

If the values of a multivariate function f(x ₁,x ₂,??,x _N ) are given at only a finite number of points in the space of its arguments and an interpolation which employs continuous functions is considered standard multivariate routines may become cumbersome as the dimensionality grows. This urges us to develop a divide-and-conquer algorithm which approximates the function. The given multivariate data are partitioned into low-variate data. This approach is called High Dimensional Model Representation (HDMR). However, the method in its current form is not applicable to problems having huge volumes of data. With the increasing dimension number and the number of the corresponding nodes, the volume of data in question reaches such a high level that it is beyond the capacity of any individual PC because huge volume of data requires much higher RAM capacity. Another aspect is that the structure of equalities used in the calculation of HDMR terms varies according to the dimension number of the problem. The number of loops in the algorithm increases with the increasing dimension number. In this work, as a first step, the equations used are modified in such a way that their structure does not depend on the dimension number. With the newly obtained equalities, the method becomes appropriate for parallelization. Due to the parallelization, the RAM problem arising from problems with high volume of data is solved. Finally, the performance of the parallelized method is analyzed.     

RBF neural network inferential sensor for process emission monitoring

Inferential sensing, or soft sensing, gained popularity in recent years as an alternative to continuous emission monitoring systems because of its simplicity, reliability, and cost effectiveness as compared to analogous hardware sensors. In this paper we address the problem of NO_x emission using a model of furnace of an industrial boiler, and propose a neural network structure for high performance prediction of NO_x as well as O₂. The studied boiler is 160 MW, gas fired with natural gas, water-tube boiler, having two vertically aligned burners. The boiler model is a 3D problem that involves turbulence, combustion, radiation in addition to NO_x modeling. The 3D computational fluid dynamic model is developed using Fluent simulation package. The model provides calculations of the 3D temperature distribution as well as the rate of formation of the NO_x pollutant, enabling a better understanding on how and where NO_x are produced. The boiler was simulated under various operating conditions. The generated data is then used for initial development and assessment of neural network soft sensors for emission prediction based on the conventional process variable measurements. The performance of the proposed soft sensor is then evaluated using actual data from an industrial boiler. The developed soft sensor achieves comparable accuracy to the continuous emission monitor analyzer, however, with substantial reduction in the cost of equipment and maintenance.     

Novel wavelet neural network algorithm for continuous and noninvasive dynamic estimation of blood pressure from photoplethysmography

This paper proposes a novel wavelet neural network algorithm for the continuous and noninvasive dynamic estimation of blood pressure(BP). Unlike prior algorithms, the proposed algorithm capitalizes on the correlation between photoplethysmography(PPG) and BP. Complete BP waveforms are reconstructed based on PPG signals to extract systolic blood pressure(SBP) and diastolic blood pressure(DBP). To improve the robustness, Daubechies wavelet is implemented as the hidden layer node function for the neural network. An optimized neural network structure is proposed to reduce the computational complexity. Further, this paper investigates an inhomogeneous resilient backpropagation(IRBP) algorithm to calculate the weight of hidden layer nodes. The IRBP improves the convergence speed and reconstruction accuracy. Multiparameter intelligent monitoring in Intensive Care(MIMIC) databases, which contain a variety of physiological parameters captured from patient monitors, are used to validate this algorithm. The standard deviation σ between reconstructed and actual BP signals is 4.4797 mm Hg, which satisfies the American National Standards of the Association for the Advancement of Medical Instrumentation. The reconstructed BP waveform can be used to extract the SBP and DBP, whose standard deviations σ are 2.91 mm Hg and 2.41 mm Hg respectively.     

A new robust speed-sensorless control strategy for high-performance brushless DC motor drives with reduced torque ripple

This paper presents an analysis, design, and strategy of a high-performance speed-sensorless control scheme for estimating the phase-to-phase trapezoidal back-EMF of BLDC motor drive by means of a novel stochastic deconvolution technique in the H_∞ setting, named robust stochastic H_∞ deconvolution filter. In the proposed method, unlike the conventional observer-based approaches, the back-EMF is considered as an unknown input, and no need is felt for the constancy assumption of the rotor position and speed of machine within a short period of the time in the modeling of the BLDC motor which leads to ignoring the back-EMF dynamic at high and variable speed. In addition, since high-speed operation is vital for the motor, an improved approach has also been proposed to reduce the commutation-torque-ripple at high-speed for direct torque control (DTC) strategy of three-phase BLDC motor with 120° conduction mode in parallel with the proposed method due to the fact that drive performance intensely downgrades in this mode.     

Analysis and control of the near-wake flow over a square-back geometry

A 3D numerical simulation, based on the Lattice Boltzmann method is carried out on the near-wake flow behind a generic square-back blunt body to analyze and establish a method to control the near-wake flow. The flow topology is described by the velocity and the pressure fields. The influence of the wake vortices on the aerodynamic drag is clarified and quantified. In order to reduce this drag, an active open-loop flow control is applied by continuous blowing devices distributed around the base periphery. The blowing effect on the behind body flow is a reduction of the wake section and of the total pressure loss in the wake and an increase of the static pressure on the base of the square body. This control leads to a significant drag reduction of ΔC_x = −29% with a blowing velocity of 1.5V₀. The efficiency is then studied, and we found that the most efficient control is obtained for a blowing velocity of 0.5V₀ and a jet angle of 45°. In this case, a 20% drag reduction is obtained, and the energy needed to control the system is seven times lower than the energy saved by the control.     

Short-term production optimization of offshore oil and gas production using nonlinear model predictive control

The topic of this paper is the application of nonlinear model predictive control (NMPC) for optimizing control of an offshore oil and gas production facility. Of particular interest is the use of NMPC for direct short-term production optimization, where two methods for (one-layer) production optimization in NMPC are investigated. The first method is the unreachable setpoints method where an unreachable setpoint is used in order to maximize oil production. The ideas from this method are combined with the exact penalty function for soft constraints in a second method, named infeasible soft-constraints. Both methods can be implemented within standard NMPC software tools.The case-study first looks into the use of NMPC for ‘conventional’ pressure control, where disturbance rejection of time-varying disturbances (caused, e.g., by the ‘slugging’ phenomenon) is an issue. Then the above two methods for production optimization are employed, where both methods find the economically optimal operating point. Two different types of reservoir models are studied, using rate-independent and rate-dependent gas/oil ratios. These models lead to different types of optimums. The relative merits of the two methods for production optimization, and advantages of the two one-layer approaches compared to a two-layer structure, are discussed.     

Model-free Q-learning designs for linear discrete-time zero-sum games with application to H-infinity control

In this paper, the optimal strategies for discrete-time linear system quadratic zero-sum games related to the H-infinity optimal control problem are solved in forward time without knowing the system dynamical matrices. The idea is to solve for an action dependent value function Q(x,u,w) of the zero-sum game instead of solving for the state dependent value function V(x) which satisfies a corresponding game algebraic Riccati equation (GARE). Since the state and actions spaces are continuous, two action networks and one critic network are used that are adaptively tuned in forward time using adaptive critic methods. The result is a Q-learning approximate dynamic programming (ADP) model-free approach that solves the zero-sum game forward in time. It is shown that the critic converges to the game value function and the action networks converge to the Nash equilibrium of the game. Proofs of convergence of the algorithm are shown. It is proven that the algorithm ends up to be a model-free iterative algorithm to solve the GARE of the linear quadratic discrete-time zero-sum game. The effectiveness of this method is shown by performing an H-infinity control autopilot design for an F-16 aircraft.     

Temperature control in catalytic cracking reactors via a robust PID controller

The stabilization of fluid catalytic cracking reactors is tackled in this paper. A robust PID control law is developed in order to control the outlet reactor temperature. The suggested control is based on a reduced order model of the reactor given by a system of ordinary differential equations. The controller is synthesized using an input/output linearizing control law coupled to a proportional-derivative reduced order observer to infer on-line the unknown heat of reaction. The proposed control algorithm leads to a classical PID structure. New tuning rules are given, based on the system structure, estimations and closed-loop time constants. This control strategy turns out to be robust against model uncertainties, noisy temperature measurements and set point changes. The performance of the reaction temperature in a tubular riser reactor is numerically compared when the proposed control scheme and standard PID controllers are applied.