Design and Implementation of Internal Model Control and Particle Swarm Optimization Based PID for Heat Exchanger System

Abstract

Heat exchanger plays vital role in chemical plants because they can withstand wide range of pressure and temperature. The heat exchanger transmit heat from hot liquid to cold liquid in order to avoid the loss of heat in chemical processes. Here we proposed different tuning strategies of PID controller for control the cold water temperature at desired set point. The best controller tuning method is found out from the transient response performance and the error criteria. The simulation is performed using MATLAB for different tuning strategies of the PID controller. The model result ensure that the Particle Swarm Optimization based PID provides efficient control action than conventional PID and Internal model control based PID controller.

Design of an Apple-Picking End Effector

“…Meanwhile, the well-designed motor rotation trajectory curve shall also be stored in the Memory of the controller. The controller parses the control command conveyed by the main controller of the picking robot [21] and [22], and then retrieves different grasping gestures of the end effector and corresponding planned trajectory curve of the joint motor to drive the joint motor, thus realizing the grasping gesture of the end effector. Chip YT5188 is selected to collect the information of the rotation angle by checking the output end of the potentiometer at the corner of motors, which constitutes the motor position feedback control system.…”

Research on the modeling of random drift error and filtering technology of low cost mems gyroscope

“…The random error identification of MEMS gyroscope mainly includes the autocorrelation analysis method, the power spectrum density, the Allan variance and so on [7][8]. The data acquisition time of auto-correlation analysis method is very long; Power spectrum density is the frequency domain analysis method for random error, and it is very difficult to separate the random error by analyzing the function of power spectrum density [9]; Allan variance analysis method has the advantages of easy calculation and separation, which can easily characterize and identify various error sources, and estimate the coefficient of the error [10].…”

On the Multi-objective Optimization Method of the Flexible Job-shop Scheduling Problem Based on Ant Colony Algorithm

“…At this time, all semi-finished products can be processed; 3. The processing times of all processes involved on the corresponding processing machines are known (Ahmadi et al, 2013;Seyed and Sadeghiazad, 2016;Kesavan et al, 2016;Rafiee and Sadeghiazad, 2016); 4. The processing sequences of all products are known; 5.…”