An economic model predictive control approach to integrated production management and process operation

Alanqar, Anas; Durand, Helen; Albalawi, Fahad; Christofides, Panagiotis D.

doi:10.1002/aic.15553

Cited by 10 publications

(5 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One of the key goals to design and implement new controllers is to achieve a faster response during the transient [27]. For the proposed AFN-FTSC scheme, the larger values of these user-defined parameters will ensure fast-transient performance.…”

Section: Selection Of the User-defined Parametersmentioning

confidence: 99%

Experimental Investigation of an Adaptive Fuzzy-Neural Fast Terminal Synergetic Controller for Buck DC/DC Converters

et al. 2022

Self Cite

View full text Add to dashboard Cite

This study proposes a way of designing a reliable voltage controller for buck DC/DC converter in which the terminal attractor approach is combined with an enhanced reaching law-based Fast Terminal Synergetic Controller (FTSC). The proposed scheme will overcome the chattering phenomena constraint of existing Sliding Mode Controllers (SMCs) and the issue related to the indefinite time convergence of traditional Synergetic Controllers (SCs). In this approach, the FTSC algorithm will ensure the proper tracking of the voltage while the enhanced reaching law will guarantee finite-time convergence. A Fuzzy Neural Network (FNN) structure is exploited here to approximate the unknown converter nonlinear dynamics due to changes in the input voltage and loads. The Fuzzy Neural Network (FNN) weights are adjusted according to the adaptive law in real-time to respond to changes in system uncertainties, enhancing the increasing the system’s robustness. The applicability of the proposed controller, i.e., the Adaptive Fuzzy-Neural Fast Terminal Synergetic Controller (AFN-FTSC), is evaluated through comprehensive analyses in real-time platforms, along with rigorous comparative studies with an existing FTSC. A dSPACE ds1103 platform is used for the implementation of the proposed scheme. All results confirm fast reference tracking capability with low overshoots and robustness against disturbances while comparing with the FTSC.

show abstract

Section: Selection Of the User-defined Parametersmentioning

confidence: 99%

Experimental Investigation of an Adaptive Fuzzy-Neural Fast Terminal Synergetic Controller for Buck DC/DC Converters

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…Moreover, in [56] HVAC from a commercial building is controlled thanks to EMPC. In [57] the scheduling of a chemical stirred tank reactor is performed with EMPC. In [3], a supermarket refrigeration, a heat pump, a heat tank with a solar collector and an electric vehicle are controlled thanks to EMPC.…”

Section: Economic Model Predictive Controlmentioning

confidence: 99%

Modelling and control of multi-energy systems through Multi-Prosumer Node and Economic Model Predictive Control

Blaud

Haurant

Claveau

et al. 2020

International Journal of Electrical Power & Energy Systems

View full text Add to dashboard Cite

The present study deals with Multi-Energy Systems (MES) modelling and advanced control with Economic Model Predictive Control (EMPC). MES provide energy flexibility, efficiency, and adaptability thanks to several energy carriers. MES are identified as a lever for integrating renewable energy. A MES novel formulation technique called Multi-Prosumer Node (MPN) is developed in this paper. MPN makes possible the modeling of MES, considering MES dynamics, several energy carriers, converters, on-grid, and off-grid. In addition, this MES modeling approach is compatible with predictive control strategies like the EMPC. In fact, EMPC is able to take into account loads, weather, renewable power and energy grid cost predictions to minimise economic costs. A real case study is implemented to examine MPN capabilities, which it is composed of renewable generators, loads, storages from two-energy carriers. Two real scenarios have been developed in order to represent realistic winter and summer cases. Simulation results, thanks to modelling with MPN and EMPC advanced control, demonstrate that the node is optimally controlled, devices dynamics are considered on a minute scale, and energy conversion from one carrier to another one is taken into account while economic cost minimisation is performed. The gained results indicate that the presented MPN modelling and optimisation approach reduces economic cost by 8.21% in winter case and 84.24% in summer case compared to the benchmarks which are composed of rule-based control.

show abstract

“…The difference between setting δ 5 = 1 and having t k ≥ t 0 is that it is assumed that after t 0 , the constraint of Eq 45 is not again inactivated, whereas it is not necessary to maintain δ 5 = 1 for all times, such that the constraint of Eq 45 can be activated for finite periods of time when xðt k Þ ∈ Ωρ e by this condition. The term containing δ 4 is also used to activate Eq 45 regardless of the location of xðt k Þ in state-space, but unlike δ 5 , it is used to activate Eq 45 only when the magnitudes of the n f components of the measured state vector are not within the corresponding γ i of their steady-state value [51]. Because the γ i , i = 1, : : : , n f , are taken to be positive constants, when δ 4 = 0, then δ 4 jx i ðt k Þj = 0 cannot be greater than or equal to γ i , which means that the condition δ 4 jx i ðt k Þj ≥ γ i cannot be used to activate Eq 45.…”

Section: Data-gathering Lyapunov-based Empc Formulationmentioning

confidence: 99%

Data-Based Nonlinear Model Identification in Economic Model Predictive Control

Giuliani

Durand

2018

Smart Sustain. Manuf. Syst.

Self Cite

View full text Add to dashboard Cite

Many chemical/petrochemical processes in industry are not completely modeled from a first-principles perspective because of the complexity of the underlying physico-chemical phenomena and the cost of obtaining more accurate, physically relevant models. System identification methods have been utilized successfully for developing empirical, though not necessarily physical, models for advanced model-based control designs such as model predictive control (MPC) for decades. However, a fairly recent development in MPC is economic model predictive control (EMPC), which is an MPC formulated with an economics-based objective function that may operate a process in a dynamic (i.e., off steady-state) fashion, in which case the details of the process model become important for obtaining sufficiently accurate state predictions away from the steady-state, and the physics and chemistry of the process become important for developing meaningful profit-based objective functions and safety-critical constraints. Therefore, methods must be developed for obtaining physically relevant models from data for EMPC design. While the literature regarding developing models from data has rapidly expanded in recent years, many new techniques require a model structure to be assumed a priori, to which the data is then fit. However, from the perspective of developing a physically meaningful model for a chemical process, it is often not obvious what structure to assume for the model, especially considering the often complex nonlinearities characteristic of chemical processes (e.g., in reaction rate laws). In this work, we suggest that the controller itself may facilitate the identification of physically relevant models online from process operating data by forcing the process state to nonroutine operating conditions

show abstract

An economic model predictive control approach to integrated production management and process operation

Cited by 10 publications

References 45 publications

Experimental Investigation of an Adaptive Fuzzy-Neural Fast Terminal Synergetic Controller for Buck DC/DC Converters

Experimental Investigation of an Adaptive Fuzzy-Neural Fast Terminal Synergetic Controller for Buck DC/DC Converters

Modelling and control of multi-energy systems through Multi-Prosumer Node and Economic Model Predictive Control

Data-Based Nonlinear Model Identification in Economic Model Predictive Control

Contact Info

Product

Resources

About