Design of a Smart Pressure Transmitter and Its Temperature Compensation Using Artificial Neural Network
01 Feb 2019-Journal of Control, Automation and Electrical Systems (Springer US)-Vol. 30, Iss: 1, pp 95-103
TL;DR: In this article, the authors presented a smart pressure transmitter using bellow as primary sensor and the deflection of bellow is converted into electrical output using hall probe sensor as secondary sensor.
Abstract: This paper presents a smart pressure transmitter using bellow as primary sensor. The deflection of bellow is converted into electrical output using hall probe sensor as secondary sensor. The output Hall voltage is affected by change in input parameters like temperature. So firstly the effect of temperature on Hall voltage is derived mathematically and then experimentally analyzed. This effect of temperature on output Hall voltage is then compensated using artificial neural network. The compensated output Hall voltage is then converted into (4–20) mA current signal using signal conditioning circuit. The proposed design, experimental and testing results are reported in this paper.
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TL;DR: The results validate that, the AGA-BP compensation model has the best effect, compared to the multiple linear regression (MLR) compensation model and GA- BP compensation model, of high reference values for compensating rapid temperature variation.
21 citations
TL;DR: Employing a proportional integral (PI) adaptive sliding mode controller (ASMC), both terms of matched and unmatched uncertainties as well as the disturbance, are addressed in this work for the MEMS AC VRS so that a strict voltage is stabilized while the system is simultaneously subjected into uncertainties and exogenous disturbance.
Abstract: Tunable micro-electro-mechanical systems (MEMS) capacitors as the fundamental parts are embedded in MEMS AC voltage reference sources (VRS). Being concerned with the accuracy of the output voltage in the reference sources, it gets important to address uncertainties in the physical parameters of the MEMS capacitor. The uncertainties have the great inevitable potentiality of bringing about output voltage perturbation. The output deterioration is more remarkable when the uncertainties are accompanied by disturbance and noise. Manufacturers have been making great attempts to make the MEMS adjustable capacitor with desired rigorous physical characteristics. They have also tried to mitigate physical parameter veracity. However, ambiguity in the values of the parameters inescapably occurs in fabrication procedures since the micro-machining process might itself suffer from uncertainties. Employing a proportional integral (PI) adaptive sliding mode controller (ASMC), both terms of matched and unmatched uncertainties as well as the disturbance, are addressed in this work for the MEMS AC VRS so that a strict voltage is stabilized while the system is simultaneously subjected into uncertainties and exogenous disturbance. Cross-talk, some inertial forces, and electrostatic coercions may appear as matched and unmatched disturbances. Alteration in stiffness and damping coefficients might also take place as matched uncertainties due to variations in the fabrication process or even working environment. The simulation results in the paper are persuasive and the controller design has shown a satisfactory tracking performance.
2 citations
01 Mar 2021
TL;DR: In this paper, the authors presented an innovative control system of a hybrid tankless water heater that provides a rapid response to deliver hot water within a desired temperature range at lower energy consumption.
Abstract: This paper presents an innovative control system of a hybrid tankless water heater that provides a rapid response to deliver hot water within a desired temperature range at lower energy consumption. The proposed tankless water heater provides a hybrid heating process that composes of a gas fired heater as primary heater and an electric heating system as secondary heater. LabVIEW designing software was used in designing the control system. A controller was designed using a PID controller which response with respect to given input parameter such as changes in water flow and the inlet temperature. Through alteration of heat source, this efficiently reduces the power consumption of the tankless water heater during low water demand, and eliminate overshoots. Simulation results demonstrated that the proposed system has performed efficiently in a range of water flow and temperature demand while reducing power consumption for about 30%.
2 citations
Cites methods from "Design of a Smart Pressure Transmit..."
...In this work the training sets used to train the neural network is from the experimental data of Sinha and Mandal 2018 [17]....
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06 May 2021
TL;DR: This study presents a conventional Ziegler-Nichols (ZN) Proportional Integral Derivative (PID) controller, and proposes a fuzzy PID controller which demonstrates a better tracking performance in the presence of measurement noise, in comparison with conventional ZN-based PID controllers.
Abstract: This study presents a conventional Ziegler-Nichols (ZN) Proportional Integral Derivative (PID) controller, having reviewed the mathematical modeling of the Micro Electro Mechanical Systems (MEMS) Tunable Capacitors (TCs), and also proposes a fuzzy PID controller which demonstrates a better tracking performance in the presence of measurement noise, in comparison with conventional ZN-based PID controllers. Referring to importance and impact of this research, the proposed controller takes advantage of fuzzy control properties such as robustness against noise. TCs are responsible for regulating the reference voltage when integrated into Alternating Current (AC) Voltage Reference Sources (VRS). Capacitance regulation for tunable capacitors in VRS is carried out by modulating the distance of a movable plate. A successful modulation depends on maintaining the stability around the pull-in point. This distance regulation can be achieved by the proposed controller which guarantees the tracking performance of the movable plate in moving towards the pull-in point, and remaining in this critical position. The simulation results of the tracking performance and capacitance tuning are very promising, subjected to measurement noise.
1 citations
Cites methods from "Design of a Smart Pressure Transmit..."
...The recent control methods demonstrate that efficient control techniques are employed for MEMS and other mechatronic systems to reduce error and enhance performance: Adaptive Sliding Mode Control (ASMC) with application to MEMS gyroscope [12], control of uncertain systems [29], disturbance rejection control with application to aerospace engineering [40], Artificial Neural Network (ANN) based adaptive control [38] and fuzzy adaptive control [37]....
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TL;DR: In this article , an elastic matrix layer with a network of embedded piezoelectric sensors is proposed to address the issue of bulky pressure sensors, which need to be bolted to the structure, and/or only provide point-wise measurements.
References
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Book•
30 Aug 2004
TL;DR: artificial neural networks, artificial neural networks , مرکز فناوری اطلاعات و اصاع رسانی, کδاوρزی
Abstract: artificial neural networks , artificial neural networks , مرکز فناوری اطلاعات و اطلاع رسانی کشاورزی
2,254 citations
Book•
01 Jan 1997
TL;DR: An Introduction to Nueral Networks will be warmly welcomed by a wide readership seeking an authoritative treatment of this key subject without an intimidating level of mathematics in the presentation.
Abstract: From the Publisher:
An Introduction to Nueral Networks will be warmly welcomed by a wide readership seeking an authoritative treatment of this key subject without an intimidating level of mathematics in the presentation.
2,135 citations
Book•
01 Jan 1983
TL;DR: In this paper, the authors present general principles of measurement systems, including reliability, choice and economics of measurement system elements, as well as the accuracy and reliability of the measurement system in the steady state.
Abstract: Part I: General Principles 1. The general measurement system. 2. Static characteristics of measurement system elements. 3. The accuracy of measurement systems in the steady state. 4. Dynamic characteristics of measurement systems. 5. Loading effects and two port networks. 6. Signals and noise in measurement systems. 7. Reliability, choice and economics of measurement systems. Part II: Typical Measurement System elements. 8. Sensing elements. 9. Signal conditioning elements. 10. Signal processing elements. 11. Data presentation elements. Part III: Speciaised Measurement Systems 12. Flow measurement systems. 13. Intrinsically safe measurement systems. 14. Heat transfer effects in measurement systems. 15. Optical measurement systems. 16. Ultrasonic measurement systems. 17. Gas chromatography. 18. Data acquisition. Answers to numerical problems. Index.
347 citations
Book•
01 Jan 2003
TL;DR: In this article, the authors present a detailed description of the characteristics of a flowmetering system and its application in a variety of applications, including the following: anemometers BTU Flowmeters for Heat Exchangers BTUs for Gaseous Fuels Cross-Correlation Flow Metering Elbow Taps Flow Switches Jet Deflection Flow Detectors Laminar Flow Meters, Magnetic FlowMeters, Coriolis Mass Flow-meters-Miscellaneous Mass Flowmetmers-Thermal Metering Pumps Orifices Pitot Tubes and
Abstract: GENERAL CONSIDERATIONS Flowsheet Symbols and P&I Diagrams Functional Diagrams and Function Symbols Instrument Terminology and Performance System Accuracy Uncertainty Calculations Configuring Intelligent Devices Instrument Installation Instrument Calibration Response Time and Drift Testing Redundant and Voting Systems Instrument Evaluation Binary Logic Diagrams FLOW MEASUREMENT Application and Selection Anemometers BTU Flowmeters for Heat Exchangers BTU Flowmeters for Gaseous Fuels Cross-Correlation Flow Metering Elbow Taps Flow Switches Jet Deflection Flow Detectors Laminar Flowmeters Magnetic Flowmeters Mass Flowmeters, Coriolis Mass Flowmeters-Miscellaneous Mass Flowmeters-Thermal Metering Pumps Orifices Pitot Tubes and Area Averaging Units Polyphase (Oil/Water/Gas) Flowmeters Positive-Displacement Gas Flowmeters Positive-Displacement Liquid Meters and Provers Purge Flow Regulators Segmental Wedge Flowmeter Sight Flow Indicators Solids Flowmeters and Feeders Target Meters Turbine and Other Rotary Element Flowmeters Ultrasonic Flowmeters Variable-Area, Gap, and Vane Flowmeters V-Cone Flowmeter Venturi Tubes, Flow Tubes, and Flow Nozzles Vortex and Fluidic Flowmeters Weirs and Flumes LEVEL MEASUREMENT Application and Selection Bubblers Capacitance and Radio Frequency (RF) Admittance Probes Conductivity and Field Effect Level Switches Diaphragm Level Detectors Differential Pressure Level Detectors Displacer Level Detectors Float Level Devices Laser Level Sensors Level Gauges, Including Magnetic Microwave Level Switches Optical Level Devices Radar, Noncontacting Level Sensors Radar, Contact Level Sensors (TDR, GWR, PDS) Radiation Level Sensors Resistance Tapes Rotating Paddle Switches Tank Gauges Including Float-Type Tape Gauges Thermal Level Sensors Time Domain Reflectometry and Phase Difference Sensors Ultrasonic Level Detectors Vibrating Level Switches TEMPERATURE MEASUREMENT Application and Selection Bimetallic Thermometers Calibrators and Simulators Color Indicators, Crayons, Pellets Fiber-Optic Thermometers Filled-Bulb and Glass-Stem Thermometers Integrated Circuitry (IC) Transistors and Diodes Miscellaneous Temperature Sensors Pneumatic and Suction Pyrometers Pyrometric Cones Radiation and Infrared Pyrometers Quartz Crystal Thermometry Resistance Temperature Detectors (RTDs) Temperature Switches and Thermostats Thermistors Thermocouples Thermowells Ultrasonic Thermometers PRESSURE MEASUREMENT Selection and Application Accessories: Seals, Snubbers, Calibrators, and Manifolds Bellows-Type Pressure Sensors Bourdon and Helical Pressure Sensors Diaphragm or Capsule-Type Sensors Differential Pressure Instruments Electronic Pressure Sensors High-Pressure Sensors Manometers Multiple Pressure Scanners Multiple Pressure Scanners Pressure Gauges Pressure Repeaters Pressure and Differential Pressure Switches Vacuum Sensors DENSITY MEASUREMENT Density: Applications and Selection Displacement- and Float-Type Densitometers Hydrometers Hydrostatic Densitometers Oscillating Coriolis Densitometer (Gas, Liquid, and Slurry Services) Radiation Densitometers Ultrasonic Sludge and Slurry Densitometers Liquid/Slurry/Gas Density-Vibrating Densitometers Weight-Based and Miscellaneous Densitometers Gas Densitometers SAFETY AND MISCELLANEOUS SENSORS Boroscopes Electrical and Intrinsic Safety Electrical Meters and Sensors Energy Management Devices (Peak Load Shedding) Excess Flow and Regular Check Valves Explosion Suppression and Deluge Systems Flame Arresters, Conservation Vents, and Emergency Vents Flame, Fire, and Smoke Detectors Leak Detectors Linear and Angular Position Detection Machine Vision Technology Metal Detectors Noise Sensors Proximity Sensors and Limit Switches Relief Valves-Determination of Required Capacity Relief Valves-Sizing, Specification, and Installation Rupture Discs Soft Sensors Tachometers and Angular Speed Detectors Thickness and Dimension Measurement Torque and Force Transducers Vibration, Shock, and Acceleration Weather Stations Weighing Systems: General Considerations Weight Sensors ANALYTICAL INSTRUMENTATION Analyzer Application and Selection Analyzer Sampling: Process Samples Analyzer Sampling: Stack Particulates Analyzers Operating on Electrochemical Principles Air Quality Monitoring Biometers Biochemical Oxygen Demand, Chemical Oxygen Demand, and Total Oxygen Demand Calorimeters Carbon Dioxide Carbon Monoxide Chlorine Chromatographs: Gas Chromatographs: Liquid Coal Analyzers Colorimeters Combustibles Conductivity Analyzers Consistency Analyzers Corrosion Monitoring Differential Vapor Pressure Sensor Dioxin Analysis Elemental Monitors Fiber-Optic Probes Fluoride Analyzers Hydrocarbon Analyzers Hydrogen Sulfide Infrared Analyzers Ion-Selective Electrodes Mass Spectrometers Mercury in Air Mercury in Water Moisture in Air: Humidity and Dew Point Moisture in Gases and Liquids Moisture in Solids Molecular Weight Nitrate, Ammonia, and Total Nitrogen Nitrogen Oxide Analyzers Odor Detection Oil in or on Water Open Path Spectrometry Oxidation-Reduction Potential (ORP) Oxygen in Gases Oxygen in Liquids (Dissolved Oxygen) Ozone in Gas Ozone in Water Particulates, Opacity, Dust, and Smoke Particle Size and Distribution Monitors pH Measurement Phosphorus Analyzer Physical Properties Analyzers - ASTM Methods Raman Analyzers Refractometers Rheometers Streaming Current or Particle Charge Analyzer Sulfur-in-Oil Analyzers Sulfur Oxide Analyzers Thermal Conductivity Detectors Total Carbon Analyzers Toxic Gas Monitoring Turbidity, Sludge, and Suspended Solids Ultraviolet and Visible Analyzers Viscometers-Application and Selection Viscometers-Laboratory Viscometers-Industrial Water Quality Monitoring Wet Chemistry and Autotitrator Analyzers APPENDIX International System of Units Engineering Conversion Factors Chemical Resistance of Materials Composition of Metallic and Other Materials Steam and Water Tables Friction Loss in Pipes Tank Volumes Directory of "Lost" Companies INDEX
93 citations
TL;DR: The prime aim of the present paper is to develop an intelligent model of the CPS involving less computational complexity, so that its implementation can be economical and robust.
Abstract: A capacitor pressure sensor (CPS) is modeled for accurate readout of applied pressure using a novel artificial neural network (ANN). The proposed functional link ANN (FLANN) is a computationally efficient nonlinear network and is capable of complex nonlinear mapping between its input and output pattern space. The nonlinearity is introduced into the FLANN by passing the input pattern through a functional expansion unit. Three different polynomials such as, Chebyschev, Legendre and power series have been employed in the FLANN. The FLANN offers computational advantage over a multilayer perceptron (MLP) for similar performance in modeling of the CPS. The prime aim of the present paper is to develop an intelligent model of the CPS involving less computational complexity, so that its implementation can be economical and robust. It is shown that, over a wide temperature variation ranging from -50 to 150 degrees C, the maximum error of estimation of pressure remains within +/- 3%. With the help of computer simulation, the performance of the three types of FLANN models has been compared to that of an MLP based model.
69 citations