Time Domain Tuning of Fractional Order Controllers Combined With a Smith Predictor for Automation of Water Distribution in Irrigation Main Channel Pools

Abstract

This paper proposes a new technique for designing fractional order controllers applied to the automation of main canal pools of a water irrigation system. As large variation of all the plant parameters is present in such systems, a fractional order family of controllers combined with a Smith Predictor is designed using time domain specifications. The designed controllers are compared with the standard combination of a Smith Predictor with a proportional‐integral (PI) controller. All these controllers are tuned to fulfil the same time specifications in the case of canal nominal dynamics. In some canals the plant parameters may experience large changes that deteriorate the time response, and can even unstabilize the closed‐loop system. The fractional controllers are therefore designed to minimize the loss of performance of the system to variations in these parameters. Simulated results show the performance improvements achieved with these controllers compared with a conventional PI controller.

Modified Cascade Controller Design for Unstable Processes With Large Dead Time

“…The loop transfer function of the system shown in Figure 3 is, ( ) = 1 ( ) ( ) (37) Substituting 1 and in Eq (37) which gives Eq (38) as,…”

“…In [37], the authors applied swarm intelligence for obtaining optimum parameters of the FOPID controller which helps to obtain the desired performances from the unstable processes. The combination of both FO and smith predictor scheme is applied in many industrial applications like furnace, water irrigation system, and others [38][39][40][41][42]. The guaranteed results are obtained using the combination of fractional-order system and smith predictor system.…”

Design and implementation of smith predictor based fractional order PID controller on MIMO flow-level plant

“…1. Different methods have been proposed for tuning the parameters of Smith predictor based fractional order controllers [13][14][15][16][17]. In this paper, an FOPID controller is employed and the internal model is considered to be a First Order Plus Dead Time (FOPDT) transfer function.…”

“…The extra degrees of freedom in setting the integration and differentiation orders O and P lead to more flexible tuning strategy for achieving control requirements as compared with the classical controllers [8]. Fractional order control has been extended on advanced and simple control methods; such as adaptive control [9], fractional order model predictive control [10], set-point weighted fractional order controller [11,12], and Smith predictor based fractional order controller [13][14][15][16][17].…”

Design of a fractional order PI controller for steam pressure in the steam drum of a bagasse fired boiler

“…Fractional order controllers ( ) have consequently also been proposed and have received considerable attention (see e.g. Castillo-Garcia et al, 2013b;Feliu-Batlle et al, 2013;Monje et al, 2010;Podlubny, 1999;Rivas -Perez et al, 2014a;etc.). One interesting feature of fractional order controllers is that they exhibit certain advantages when designing robust control systems in the frequency domain for processes whose parameters vary in a large range.…”