Diagnosis of Air-Gap Eccentricity Fault for Inverter Driven Induction Motor Drives in the Transient Condition
Modelling and Simulation Study of a Helicopter with an External Slung Load System
Comparative Study of Single Phase Power Inverters Based on Efficiency and Harmonic Analysis
Trichotomous Exploratory Data Analysis [Tri–EDA]: A Post Hoc Visual Statistical Cumulative Data Analysis Instrument Designed to Present the Outcomes of Trichotomous Investigative Models
LabVIEW Based Design and Analysis of Fuzzy Logic, Sliding Mode and PID Controllers for Level Control in Split Range Plant
Control systems can be defined as device configuration part interconnections that provide the time needed for the system response. Control systems provide the necessary output with mechanical, optical or electronic devices. For example, the best drilling parameters for the desired ROP are determined for rotational drilling operations. In this case, the regulated performance is the penetration rate for drilling, lower-hole relationship, and drilling parameters. The controller must attempt to design a control system which provides the highest possible ROP for drilling. When human input is limited to a small range, the computer becomes an automatic control mechanism. The objective of this research was to establish an optimized closed loop control system for automated drilling operations, taking into account all factors affecting the penetration rate of drilling. These factors considered in this research include drilling parameters, formation parameters, bit parameter, and drilling fluid parameters. In order to automate the process, closed loop control systems have been developed to optimize these individual parameters, which eventually reduce physical or mental human interference. Thus the optimum penetration rate of drilling was achieved and the overall drilling performance was increased.
For drilling optimization, the Rate of Penetration (ROP) is crucial; optimizing the ROP will reduce high boost costs considerably. This work uses the standard severe learning system and an effective test model (USA) for the ROP prediction. The shape of the structure, rock mechanical properties, hydraulic characteristics, bit size and characteristics (bit weight and rotary speed), and mud characteristics, are the most important ROP input parameters, and these are known as ROP predictions. The prediction model was built using industrial data collected in an oil reservoir at Bohai Bay, China. The prediction model has been designed and tested for its prediction accuracy and contrasted with the widely used traditional Artificial Neural Network (ANN). The results show that of all ROP prediction models, such as ANN, ELM and USA, have the benefit of higher learning rates and improved generalization vision both in the ELM and USA models. In the area of ROP forecasts throughout modern oil and gas explorations, the simulation findings are very good for ELM and the USA models as they surpass the ANN pattern. Meantime, this work offers ROP prediction to drilling engineers, more choices according to their calculation and precision requirements.
At present times, robotic technology is gaining interest amoung researchers and practitioners. Different robotic models are available to solve vehicle platform problems through CAD software like Solidworks, ProE etc. There is a need for smart robots to solve the problem of industrial operations and military applications. Surveillance and risk-free movement is the most essential part of the military and industrial operations. In this paper, we have proposed a design of a robot which can be used for intelligent, reconnaissance missions through the connection of sensor via robot operating system (ROS). The proposed robot provides an interface to an operator for hands-free operations and gestures, which also presents the inverse dynamics of different reconfigurable gestures and postures through simulation. Graphical abstract represents the system architecture of the proposed model, where trajectory points are supervised by logics and robot operating system (ROS). ROS mainly work on sensor data which is procced through forwarding kinematic, i.e., controller system. According to supervisory logic, inverse kinematics worked with the help of actuators and further simulated the proposed work, which is completed by hardware.
Enhancing the smartness of any electronic gadget or measuring system is highly encouraging. This article mainly focused on the design of a smart temperature transmitter for thermocouple. The transmitter consists of four functional block, viz. sensor module, signal conditioning unit (SCU), the processing unit and output module. The signals from thermocouples are fed to SCU for amplification and filtration of the thermocouple output. The proper amplified signal is now ready to be processed by a processing unit called a microcontroller. With the piecewise linearization technique, it computes the measured temperature and also performs the cold junction compensation using voltage from the IC sensor. Auto ranging logic is performed to generate 4 to 20 mA (milliampere) output signal for all thermocouples irrespective of their temperature range. The output module consists of a DAC, followed by a voltage to current converter and an LCD display panel for displaying various parameters or information. This design and fabrication work considers only three thermocouples (J, K, and T), but can accommodate upto seven thermocouples.
In this paper discusses various algorithms on optimal tuning Fractional-Order Proportional Integral Derivative (FOPID) controller to govern the rotor speed of sensorless Brushless Direct Current (BLDC) motor. Bat Algorithm (BA) is used as the optimization algorithm for developing a novel approach to generate five degrees of freedom parameters namely kp, ki, l and m of FOPID controller. A robust performance is achieved by using the FOPID closed loop speed controller using BA for optimal tuning along with a control on the desired speed. The study measured the time domain specifications of a dynamic system for unit step input to FOPID controller for speed response such as peak time ( tr), Percentage of overshoot (PO), settling time (ts), rise time (tr) have been evaluated and the steady-state error (ess) of sensorless speed control of BLDC motor. The characteristics are compared with the results from Artificial Bee Colony (ABC) and Modified Genetic Algorithm (MGA) for transient and steady state time domain. The results confirm that the proposed controller optimization using BA technique is more efficient in improving the transient characteristic performance and reducing steady state error for the FOPID.