Predictive Control Approaches for PID Control Design and Its Extension to Multirate System

Satô, Takao

doi:10.1007/978-1-4471-2425-2_18

Cited by 4 publications

(4 citation statements)

References 28 publications

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“…Tuning type 1 results in a PID equivalent to GPC, described in literature by [12], however it is necessary for this case to adjust both the reference filter and output filter in the PID controller.…”

Section: Observationsmentioning

confidence: 99%

“…The first of these methods, called Type 1, results in a control law equivalent to GPC's and is known in literature [12], it is, however, limited to applications where both a reference and process output filters are available. In order to circumvent this limitation five other tuning methods are proposed, Types 2, 2*, 2**, 3 and 3*, which are based on approximations of Type 1's control law, hence pseudo predictive.…”

mentioning

confidence: 99%

“…Amongst these are the predictive tuning methods such as [6,9,10,12,14,15], to name a few. However, these tunings are tailored to specific PID structures and are hard to generalize, reducing its practicality in situations where embedded control is desired, i.e.…”

mentioning

confidence: 99%

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Pseudo Predictive Tuning Methods for PLC Embedded PID Controllers

Jeronymo¹,

Araújo²,

Suárez³

et al. 2016

Proceeding Series of the Brazilian Society of Computational and Applied Mathematics

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Abstract. In this paper novel tuning settings for Proportional-Integral-Derivative (PID) controllers are proposed. These tunings are based on approximations of the generalized predictive controller (GPC) resulting in pseudo predictive PID controllers. It is possible in this way to embed industrial PID controllers as predictive controllers without hardware changes being necessary. In order to obtain this pseudo predictive tuning the PID structure is matched to the GPC structure and approximations are taken in the cases where the structures are not equivalent. A first order model implies in a PI while a second order model in a PID controller designs, respectively. Depending on which tuning is used, either set-point tracking or closed-loop characteristics is favored. Future work will include results and comparisons of the proposed tuning with other methods from literature.

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Section: Observationsmentioning

confidence: 99%

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confidence: 99%

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Pseudo Predictive Tuning Methods for PLC Embedded PID Controllers

Jeronymo¹,

Araújo²,

Suárez³

et al. 2016

Proceeding Series of the Brazilian Society of Computational and Applied Mathematics

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“…In Moradi (2003), the velocity form of PID prohibited the variable gain structure, thus a fixed PID gain must be used across the prediction horizon. As shown in Camacho et al (2003); Arousi et al (2008);Sato (2012) the GPC-based PID results apply to the plants approximated by a first or second order model, thus limiting their applications to multivariable plants. The solution in Di Cairano and Bemporad (2010) partially solves the problem, but the two controllers MPC and PID must operate in parallel.…”

Section: Introductionmentioning

confidence: 99%

From Parametric Model-based Optimization to robust PID Gain Scheduling

Nguyen¹,

Tan²

2013

Preprint

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In chemical process applications, model predictive control (MPC) effectively deals with input and state constraints during transient operations. However, industrial PID controllers directly manipulates the actuators, so they play the key role in small perturbation robustness. This paper considers the problem of augmenting the commonplace PID with the constraint handling and optimization functionalities of MPC. First, we review the MPC framework, which employs a linear feedback gain in its unconstrained region. This linear gain can be any preexisting multi-loop PID design, or based on the two stabilizing PI/PID designs for multivariable systems proposed in the paper. The resulting controller is a feedforward PID mapping, a straightforward form without the need of tuning PID to fit an optimal input. The parametrized solution of MPC under constraints further leverages a familiar PID gain scheduling structure. Steady state robustness is achieved along with the PID design so that additional robustness analysis is avoided.

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