PI and PID auto-tuning procedure based on simplified single parameter optimization

Romero, Julio Ariel; Sanchis, Roberto; Balaguer, Pedro

doi:10.1016/j.jprocont.2011.04.003

Cited by 43 publications

(21 citation statements)

References 15 publications

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“…Shahni & Malwatkar, 2011;Veronesi & Visioli, 2010) as well as the autotuning procedures for the PI controller (see e.g. Romero, Sanchis, & Balaguer, 2011;Vilanova, Alfaro, & Arrieta, 2012) that can potentially allow for the B-BAC autotuning. The only limitation results from its level of sophistication that must be acceptable for the particular user and from the stability condition given by Equation (B5).…”

Section: Discussionmentioning

confidence: 99%

General tuning procedure for the nonlinear balance-based adaptive controller

Stebel

Czeczot

Laszczyk

2013

International Journal of Control

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This paper presents the intuitive and ready-to-use, general procedure for tuning the balance-based adaptive controller (B-BAC) based on its equivalence to the controller with PI term and with additional improvements shown for the linearised approximation of the dynamics of the nonlinear controlled process. The simple formulas are suggested to calculate the B-BAC tunings based on the PI tunings determined by any PI tuning procedure chosen accordingly to the desired closed-loop performance. This methodology is verified by comparing the closed-loop performance of the equivalently tuned B-BAC and PI/PI + feedforward controllers under the same scenario, both by the simulation and practical experiments.

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Section: Discussionmentioning

confidence: 99%

General tuning procedure for the nonlinear balance-based adaptive controller

Stebel

Czeczot

Laszczyk

2013

International Journal of Control

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“…Romero et al [1] presented an auto tuning algorithm for PI and PID controllers to 24 minimize the load disturbance integral error. Leva and Maggio [2] used constrained 25 optimization to find tuning rules for both the PID parameters and the noise filter.…”

Section: Introduction 17mentioning

confidence: 99%

PID based nonlinear processes control model uncertainty improvement by using Gaussian process model

Chan

Chen

2016

Journal of Process Control

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“…With the appearance, development and application of the intelligent control theory and artificial intelligence techniques [5], such as fuzzy theory, rough set theory, neural networks and genetic algorithms, particle swarm optimization, ant colony optimization algorithm, simulated annealing algorithm and so on, these methods has opened up new avenues to control the PID control of the complex system [6]. Li et al proposed a new type of multivariable fuzzy control algorithm based on fuzzy control theory in order to effectively solve the control problem of the ball mill pulverizing system [7].…”

Section: Introductionmentioning

confidence: 99%