Comparing MIMO Process Control Methods on a Pilot Plant
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Comparing MIMO Process Control Methods on a Pilot Plant E. Boeira1 · V. Bordignon1 · D. Eckhard2 · L. Campestrini1 Received: 11 October 2017 / Revised: 25 April 2018 / Accepted: 17 May 2018 © Brazilian Society for Automatics–SBA 2018
Abstract This work presents a comparison among three different control strategies for multivariable processes. The techniques were implemented in a pilot plant with coupled control loops, where all steps used to design the controllers were described allowing to establish a trade-off between algorithm complexity, information needed from the process and achieved performance. Two data-driven control techniques are used: multivariable ultimate point method to design a decentralized PID controller and virtual reference feedback tuning to design a centralized PID controller. A mathematical model of the process is obtained and used to design a model-based generalized predictive controller. Experimental results allow us to evaluate the performance achieved for each method, as well as to infer on their advantages and disadvantages. Keywords MIMO control · Ultimate point method · VRFT · System identification · MPC
1 Introduction Multivariable systems are ubiquitous in process control specially in oil & gas and pulp & paper industries (Skogestad and Postlethwaite 2005; Al-Naumani and Rossiter 2015; Rojas et al. 2012; Dumont 1986). Multiple-input–multiple-output (MIMO) processes present interactions between control loops, such that operational changes in one subsystem disturb or affect properties of other subsystems. Different strategies are used to control MIMO processes. When interactions are weak, simple strategies that completely disregard the multivariable nature of the process can be used. However, when E. Boeira, V. Bordignon, D. Eckhard and L. Campestrini are supported by CNPq.
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L. Campestrini [email protected] E. Boeira [email protected] V. Bordignon [email protected] D. Eckhard [email protected]
1
Departamento de Sistemas Elétricos de Automação e Energia, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
2
Departamento de Matemática Pura e Aplicada, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
coupling is strong, using single-input–single-output (SISO) techniques would result in poor performance. When high performance is expected usually a more complex control structure is used, which demands a larger amount of information from the process (sensors, models and experimental data), uses more complex algorithms (for instance model predictive, adaptive or nonlinear control) and finally requires more time to be designed (García et al. 1989; Bodson and Groszkiewicz 1997; Krstic et al. 1995). When low performance is accepted, it is usual to use simpler control structures (for instance a decentralized PID controller) that require less information about the process, are easier to implement and tune, resulting in smaller time to obtain an adequate performance (Campestrini et al. 2006; Vu and Lee 2010; Jin et al. 2013). Contr
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