Recurrent Neural Network based MPC for Process Industries

Lanzetti, Nicolas; Lian, Ying Zhao; Cortinovis, Andrea; Domínguez, Luis F.; Mercangöz, Mehmet; Jones, Colin N.

doi:10.23919/ecc.2019.8795809

Cited by 37 publications

(37 citation statements)

References 6 publications

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“…As a summary, our data-based enhanced control strategy, which is implemented by means of ANNs, allows us to improve a control approach by means of (i) decreasing the design process complexity, and (ii) increasing its scalability [ 14 , 16 ]. The complexity reduction is achieved due to the fact that ANNs do not require a such precise adjustment to the scenario as usual filtering strategies do.…”

Section: Data-based Enhanced Control Strategymentioning

confidence: 99%

“…Data-driven methods and ANNs have arisen as new approaches able to offer a good control performance at the same time they increase the scalability and decoupling of the control strategy from the highly complex mathematical models [ 14 , 15 , 16 , 17 , 18 , 19 ]. In such a context, ANNs have been considered to perform different tasks: (i) act as soft-sensors, (ii) complement the model-based controllers, and (iii) act as a control strategy as such.…”

Section: Introductionmentioning

confidence: 99%

“…For that reason, the approach proposed in [ 36 ] has been enhanced in this work by means of a complete and easy tuning data-based enhanced control solution. The new proposed approach will achieve the reduction of the design process complexity and the increment of its scalability [ 14 , 16 ]. It considers a new denoising approach, the data-based Denoising Stage.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Industrial Control under Non-Ideal Measurements: Data-Based Signal Processing as an Alternative to Controller Retuning

Pisa

Morell

Vilanova

et al. 2021

Sensors

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Industrial environments are characterised by the non-lineal and highly complex processes they perform. Different control strategies are considered to assure that these processes are correctly performed. Nevertheless, these strategies are sensible to noise-corrupted and delayed measurements. For that reason, denoising techniques and delay correction methodologies should be considered but, most of these techniques require a complex design and optimisation process as a function of the scenario where they are applied. To alleviate this, a complete data-based approach devoted to denoising and correcting the delay of measurements is proposed here with a two-fold objective: simplify the solution design process and achieve its decoupling from the considered control strategy as well as from the scenario. Here it corresponds to a Wastewater Treatment Plant (WWTP). However, the proposed solution can be adopted at any industrial environment since neither an optimization nor a design focused on the scenario is required, only pairs of input and output data. Results show that a minimum Root Mean Squared Error (RMSE) improvement of a 63.87% is achieved when the new proposed data-based denoising approach is considered. In addition, the whole system performance show that similar and even better results are obtained when compared to scenario-optimised methodologies.

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Section: Data-based Enhanced Control Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Industrial Control under Non-Ideal Measurements: Data-Based Signal Processing as an Alternative to Controller Retuning

Pisa

Morell

Vilanova

et al. 2021

Sensors

View full text Add to dashboard Cite

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“…Restrictions are imposed on the range of allowed values as displayed on Table 1. The setpoint range for air temperatures is [19,24] • C to ensure occupants' thermal comfort. Moreover, the bottom temperatures of the tanks have to stay 5 • C lower than the heat pump supply temperature.…”

Section: Building Test Casementioning

confidence: 99%

“…The metric C has the weakness to be relatively insensible to the magnitude of the temperature violations, as the factor N −1 provides an average over the entire evaluation period. To overcome this issue the third metric, ∆, estimates the mean temperature deviation per violating points i.e., ∆ = N N out C (13) where N out is the number of points lying outside the interval [19,24] • C. Finally, the mean power electric consumption by the thermal systems are also reported to make sure that optimizers do not globally consume more than rule-based controllers:…”

Section: Control Performancementioning

confidence: 99%

State-space models for building control: how deep should you go?

Schubnel

Carrillo

Taddeo

et al. 2020

Journal of Building Performance Simulation

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Power consumption in buildings show non-linear behaviors that linear models cannot capture whereas recurrent neural networks (RNNs) can. This ability makes RNNs attractive alternatives for the modelpredictive control (MPC) of buildings. However RNN models lack mathematical regularity which makes their use challenging in optimization problems. This work therefore systematically investigates whether using RNNs for building control provides net gains in an MPC framework. It compares the representation power and control performance of two architectures: a fully non-linear RNN architecture and a linear statespace model with non-linear regressor. The comparison covers five instances of each architecture over two months of simulated operation in identical conditions. The error on the one-hour forecast of temperature is 69% lower with the RNN model than with the linear one. In control the linear state-space model outperforms by 10% on the objective function, shows 2.8 times higher average temperature violations, and needs a third of the computation time the RNN model requires. This work therefore demonstrates that in their current form RNNs do improve accuracy but on balance well-designed linear state-space models with non-linear regressors are best in most cases of MPC.

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Learning model predictive control with long short‐term memory networks

Terzi

Bonassi

Farina

et al. 2021

Intl J Robust & Nonlinear

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This article analyzes the stability-related properties of long short-term memory (LSTM) networks and investigates their use as the model of the plant in the design of model predictive controllers (MPC). First, sufficient conditions guaranteeing the Input-to-State stability (ISS) and Incremental Input-to-State stability (𝛿ISS) of LSTM are derived. These properties are then exploited to design an observer with guaranteed convergence of the state estimate to the true one. Such observer is then embedded in a MPC scheme solving the tracking problem. The resulting closed-loop scheme is proved to be asymptotically stable.The training algorithm and control scheme are tested numerically on the simulator of a pH reactor, and the reported results confirm the effectiveness of the proposed approach.

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Recurrent Neural Network based MPC for Process Industries

Cited by 37 publications

References 6 publications

Industrial Control under Non-Ideal Measurements: Data-Based Signal Processing as an Alternative to Controller Retuning

Industrial Control under Non-Ideal Measurements: Data-Based Signal Processing as an Alternative to Controller Retuning

State-space models for building control: how deep should you go?

Learning model predictive control with long short‐term memory networks

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