Design of PI Controller using Characteristic Ratio Assignment Method for Coupled Tank SISO Process

Senthilkumar, M.; S.AbrahamLincon,; Selvakumar, P.

doi:10.5120/3056-4164

Cited by 5 publications

(2 citation statements)

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“…The optimal weight derived from this stage is used to generate an initial 100 population for a GA, which was run for 500 generations to obtain the optimal values of the unknown parameters of the WNN. This is a case of non-linear MIMO system, which is more challenging than most recently reported SISO in literature [8,18,28,41] , and the training approach was adopted for the following reasons in order to avoid the problem of being trapped in a local minimum solution. Moreover, it was shown after some heuristic study that the proposed is a faster approach.…”

Section: The Term δϕX δXmentioning

confidence: 99%

“…The non-linearity in the CTS is mainly due to the basic dynamic equations of the CTS, the characteristics of the valves and as a result of the nonlinear flow characteristics in the tank system [4] . Most of the literature concerned with CTSs deals with the non-linearity of the SISO system case only [8,28,29] and to the best of the authors knowledge no work has been reported in the literature indicating real-time control of the MIMO system which is recognised as being difficult to achieve. This paper uses a novel approach based on an efficient wavelet neural network (WNN) based model for an MIMO system and the design a non-linear model predictive control (NMPC) strategy for the CTS.…”

Section: Introductionmentioning

confidence: 99%

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A wavelet neural network based non-linear model predictive controller for a multi-variable coupled tank system

Owa

Sharma

Sutton

2014

Int. J. Autom. Comput.

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Abstract:In this paper, a novel real time non-linear model predictive controller (NMPC) for a multi-variable coupled tank system (CTS) is designed. CTSs are highly non-linear and can be found in many industrial process applications. The involvement of multi-input multi-output (MIMO) system makes the design of an effective controller a challenging task. MIMO systems have inherent couplings, interactions in-between the process input-output variables and generally have an complex internal structure. The aim of this paper is to design, simulate, and implement a novel real time constrained NMPC for a multi-variable CTS with the aid of intelligent system techniques. There are two major formidable challenges hindering the success of the implementation of a NMPC strategy in the MIMO case. The first is the difficulty of obtaining a good non-linear model by training a non-convex complex network to avoid being trapped in a local minimum solution. The second is the online real time optimisation (RTO) of the manipulated variable at every sampling time. A novel wavelet neural network (WNN) with high predicting precision and time-frequency localisation characteristic was selected for an MIMO model and a fast stochastic wavelet gradient algorithm was used for initial training of the network. Furthermore, a genetic algorithm was used to obtain the optimised parameters of the WNN as well as the RTO during the NMPC strategy. The proposed strategy performed well in both simulation and real time on an MIMO CTS. The results indicated that WNN provided better trajectory regulation with less mean-squared-error and average control energy compared to an artificial neural network. It is also shown that the WNN is more robust during abnormal operating conditions.

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Section: The Term δϕX δXmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%