Fuzzy subspace-based constrained predictive control design for a greenhouse micro-climate

Hamza, Azzedine; Chakour, Chouaib; Ramdani, Messaoud

doi:10.1007/s40435-022-01071-8

Cited by 3 publications

(3 citation statements)

References 27 publications

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“…The first step is to establish a high-fidelity predictive model. The datadriven methods such as neural network, [15][16][17] support vector machines, 18,19 random forests, 20 and subspace identification [21][22][23][24] are proposed in recent years. These methods learn the system information through input 1 and output data, which can refine the precision of predictive model.…”

Section: Introductionmentioning

confidence: 99%

Digital twin-based subspace model predictive control for thermal power plant

Zhao

Cai

Jiang

2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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In this article, a digital twin-based subspace model predictive control scheme is proposed for solving model mismatch caused by parameter perturbations of thermal power plants. First, the rational range of essential parameters in the nonlinear model is identified based on the nominal data of an actual 600 MW power plant, which provides necessary foundation for subsequent studies. Second, to improve the load-changing capacity under parameter disturbances, a novel digital twin-based subspace model predictive control algorithm is established by regarding the crucial parameter as extended input of predictive model. Finally, the small fluctuations during steady-state operation are effectively restrained through transforming weights and constraints slowly and smoothly at the end of load change. Simulation results illustrate that compared with the conventional subspace-based model predictive control, the proposed digital twin-based subspace model predictive control scheme can obtain a better performance in terms of both dynamic regulation rate and steady-state performance under parameter perturbations.

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Section: Introductionmentioning

confidence: 99%

Digital twin-based subspace model predictive control for thermal power plant

Zhao

Cai

Jiang

2023

Proceedings of the Institution of Mechanical Engineers, Part I:

View full text Add to dashboard Cite

show abstract

“…A well-known practical control strategy is to maintain the ideal growing climate near steady levels via onoff rule-based control logic or PID controllers. However, these classic approaches lack the ability to effectively cope with the complex dynamics of greenhouse systems with multiple inputs multiple outputs, see e.g., Hamza et al (2019), Lafont et al (2013) and Wang et al (2013). Despite the developments of the PID control to the multiple inputs and multiple outputs (MIMO) systems, see e.g., Vázquez and Morilla (2002), Astrom et al (2001) and Saab (2017), it is still challenging to apply the PID control to greenhouse systems, since we need to consider the interactions between multiple inputs and outputs in the system with various constraints, which may require substantial tuning efforts for PID controllers.…”

Section: Introductionmentioning

confidence: 99%

“…For example, in a study (Chen et al, 2020), a data-driven robust MPC approach is proposed to control temperature using historical data, but it primarily focused on external uncertainties related to weather prediction and did not account for parametric uncertainties. Another approach presented in Hamza et al (2019) considers parametric uncertainties through fuzzy MPC design with simplified bounded constraints, but it lacked proper formulation and propagation of these uncertainties. Similarly, a PSO-based robust MPC is proposed in Xu and van Willegenburg (2018) for greenhouse climate control to address various uncertainties using an additive bounded disturbance, while explicit formulation of parametric uncertainty was not included.…”

Section: Introductionmentioning

confidence: 99%