Design of the fractional order internal model controller using the swarm intelligence techniques for the coupled tank system

Abstract

The coupled tank system (comprising two tanks) is used in the chemical industries, water treatment plants etc. Level control of the coupled tank system is a common problem in the process control industry. This work proposes a fractional order internal model controller (FOIMC) with a higher order fractional filter for the level control of the coupled tank system. A first order plus delay time (FOPDT) model of the system is used in the controller design. FOIMC has advantages like robustness to changes in the system gain and extended stability margins. The proposed higher order fractional filter makes the controller physically realisable, and quickly roll off the magnitude Bode plot, neglecting the high frequency noise. The PSO algorithm is a swarm intelligence based algorithm used for the optimisation problems. The parameters of the FOIMC are optimised with the particle swarm optimisation (PSO) algorithm, by minimising an objective function constructed using time domain specifications. The novel objective function includes weighted peak overshoot, settling time, and integral square error. A MATLAB based tool, fractional order modelling and control (FOMCON) is used to simulate the fractional order controller. Performance of the proposed FOIMC is compared with two state of the art. Robustness to change in the operating point (tank height) is verified. The proposed FOIMC and the state of the art controllers are implemented on the laboratory setup, and the experimental results are compared.

Research on vibration mechanism and control technology of building structure under earthquake action

“…Big data analytics have also been frequently used in the complex heterogeneous environment for getting insight of the data. Authors in [29] provided a review of utilization of big data in the construction sector for data processing, storage and data distribution. Various methodologies involve data mining, statistics, clustering, regression, classification and natural language processing for big data evaluation [29,30].…”

“…Authors in [29] provided a review of utilization of big data in the construction sector for data processing, storage and data distribution. Various methodologies involve data mining, statistics, clustering, regression, classification and natural language processing for big data evaluation [29,30]. In the recent years, the new strategies have been adopted for overcoming the big data complexities like probabilistic graphics, support vector machines, and various other regression, clustering and data mining methods [31][32][33].…”

Research on Big Data Text Clustering Algorithm Based on Swarm Intelligence

“…If the amount of noise added is too small to meet the privacy needs of location information and if the noise is added too much, the availability of location information data will be lost. Therefore, it is necessary to reasonably allocate the differential privacy budget to achieve the best privacy protection effect [26]. The location information data of a given user is shown in Table 1.…”