Design and Implementation of the FOIMC Using the PSO Algorithm for the Coupled Tank System

Introduction

The coupled tank system has two modes of operation: interacting and noninteracting. In the interacting mode, the tanks are connected in cascade physically, and in the noninteracting mode the tanks are not connected physically. In this work interacting coupled tank system is considered as the case study. The level control problem of the coupled tank system usually involves, changing the level of the tank from the one value to another value, or keeping the level constant at one value. Many works in the control literature discussed the level control problem. Patel and Shah (2018) discussed a fault tolerant controller for the level control of the coupled tank system using the artificial intelligence (AI) techniques. The controller parameters are tuned using the neural networks. Sathish et al. (2017) developed a linear quadratic regulator (LQR) for the multi-input multi-output (MIMO) model of the cylindrical three tank system level control (Kumar et. al, 2017). Singla et al. (2018) proposed a modified proportional integrator differentiator (PID) controller for the coupled tank system. Mahapatro et al. (2019) discussed the design and implementation of a decentralized proportional integrator (PI) controller for the level control of the coupled tank system. A reduced order model of the coupled tank system is obtained based on the frequency response fitting. Gouta et al. (2019) discussed the design of an adaptive high gain observer-based controller design, for the coupled tank system. A generalized predictive controller is used to minimize the multi stage objective function. Dumlu and Ayten (2018) proposed an adaptive sliding mode controller for the coupled tank system level control. The design combines the feedforward action with the adaptive feedback. Other popular controllers for the level control of the coupled tank system are conventional PID controller, internal model control (IMC), Model predictive controller (MPC), and adaptive controller etc.

Among the controllers available for the coupled tank system level control, the IMC has the advantages like stability, and minimum number of the parameters to be tuned. Some of the recent literature works on the IMC design are presented here. Li and Zhu (2019) developed an IMC- PID based controller for the speed control of the servo motor using an extended state observer. Pati and Negi (2019) designed a two degrees of freedom IMC for the magnetic levitation system. The controller parameters are tuned using the PSO algorithm. Vadamalu and Beidl (2019) designed an adaptive IMC for the internal combustion engine’s torsional oscillations control. The controller reduces the disturbances at various operating speeds. Tasoujian et al. (2019) discussed the design of a robust IMC for the blood pressure regulation. An IMC-PID controller is developed including the uncertainty, with a time varying model. Li and Bing (2019) proposed a Smith predictor-based IMC for a second order time delay system.