The Potential of Fractional Order Distributed MPC Applied to Steam/Water Loop in Large Scale Ships

Zhao, Shiquan; Cajo, Ricardo; Keyser, Robain De; Ionescu, Clara-Mihaela

doi:10.3390/pr8040451

Cited by 12 publications

(7 citation statements)

References 23 publications

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“…It follows the concept behind all model based predictive algorithms. This idea has been used by some authors to design their control strategies; without the intention of being exhaustive; such as [31] where a fractional predictive controller is applied to Steam/Water Loop in Large Scale Ships in a distributed scheme, [32] where this fractional control strategy has been used to achieve the optimal frequency control of an islanded microgrid or [33] where the fractional predictive method has been tested with an industrial heating furnace.…”

Section: Fractional Predictive Control Strategymentioning

confidence: 99%

Fractional Generalized Predictive Control Strategy With Fractional Constraints Handling

Romero

Mañoso

2022

IEEE Access

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The control strategies based on the methodology known as Model-based Predictive Control (MPC) have been developed and widely adopted to control real plants. This is mainly due to their intrinsic ability to handle constrains and their capacity to predict and optimize the future behavior of the process using a dynamical model of the plant. On the other hand, the mathematical tool known as fractional calculus has been currently used for reformulating the predictive control strategies to reach a better performance adding new control parameters. This work extends the use of fractional operators for the constraints in one type of fractional predictive control strategy known as Fractional-order Generalized Predictive Control (FGPC), interpreting and discussing the results. In addition, a new method to soften constraints using fractional operator is proposed and illustrated with examples, even to adjust the final response of the system. A practical tunning of the rest of controller parameters with the help of a wellknown mathematical software is also included to make use of the beneficial characteristics of this fractional predictive formulation.

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Section: Fractional Predictive Control Strategymentioning

confidence: 99%

Fractional Generalized Predictive Control Strategy With Fractional Constraints Handling

Romero

Mañoso

2022

IEEE Access

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“…Note that the systems in eqs and are both typical first-order plus dead time (FOPDT) systems and the generalization of the FOPDT system with the form of should be considered in future work . Fractional-order controller based on PR will be another interesting topic . The control requirements of the WB and VL systems are listed in Table .…”

Section: Illustrative Examplesmentioning

confidence: 99%

“…52 Fractionalorder controller based on PR will be another interesting topic. 53 The control requirements of the WB and VL systems are listed in Table 1. The parameters of ADRC-PR are listed in Table 2 and are obtained based on the flowchart in Figure 3 and other analyses from the previous sections.…”

Section: Illustrative Examplesmentioning

confidence: 99%

Active Disturbance Rejection Control Design Based on Probabilistic Robustness for Uncertain Systems

Chen

2020

Ind. Eng. Chem. Res.

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Uncertainties always exist in practical industrial processes which result in many difficulties for controller design. To enhance the ability of closed-loop systems to handle uncertainties, an active disturbance rejection control (ADRC) design based on probabilistic robustness (PR) is proposed in this paper. The necessity of the proposed design method is explained by introducing the problem formulation. Then the principle and parameter stability region of ADRC are introduced. On the basis of these fundamentals, a design method of ADRC based on PR is proposed for uncertain systems and the corresponding design procedure is summarized, which is applicable for uncertain single-input single-output (SISO), multi-input multi-output (MIMO) and nonlinear systems. The simulation results for SISO, MIMO, and nonlinear systems illustrate that the proposed ADRC always has a larger estimated value of the probability than other comparative controllers which means that the proposed ADRC design method can not only ensure the satisfactory control performance for nominal systems, but also meet control requirements for all uncertain systems with the largest probability. In addition, the superiority of the proposed ADRC design is also verified by comparative experiments based on the Peltier temperature control platform. The proposed ADRC design method can offer a practical solution and shows a promising future for uncertain systems in practical industrial processes.

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“…The optimal control law is obtained for each sampling time, and the first element is applied to the physical systems. MPC has been successfully applied in a diversity of systems, such as synchronous motor drives [7][8][9][10], steam power plants [11][12][13][14], building energy control [15,16], autonomous underwater vehicles [17,18], and so on.…”

Section: Introductionmentioning

confidence: 99%

An Advanced Fractional Order Method for Temperature Control

et al. 2023

Self Cite

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Temperature control in buildings has been a highly studied area of research and interest since it affects the comfort of occupants. Commonly, temperature systems like centralized air conditioning or heating systems work with a fixed set point locally set at the thermostat, but users turn on or turn off the system when they feel it is too hot or too cold. This configuration is clearly not optimal in terms of energy consumption or even thermal comfort for users. Model predictive control (MPC) has been widely used for temperature control systems. In MPC design, the objective function involves the selection of constant weighting factors. In this study, a fractional-order objective function is implemented, so the weighting factors are time-varying. Furthermore, we compared the performance and disturbance rejection of MPC and Fractional-order MPC (FOMPC) controllers. To this end, we have chosen a building model from an EnergyPlus repository. The weather data needed for the EnergyPlus calculations has been obtained as a licensed file from the ASHRAE Handbook. Furthermore, we acquired a mathematical model by employing the Matlab system identification toolbox with the data obtained from the building model simulation in EnergyPlus. Next, we designed several FOMPC controllers, including the classical MPC controllers. Subsequently, we ran co-simulations in Matlab for the FOMPC controllers and EnergyPlus for the building model. Finally, through numerical analysis of several performance indexes, the FOMPC controller showed its superiority against the classical MPC in both reference tracking and disturbance rejection scenarios.

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The Potential of Fractional Order Distributed MPC Applied to Steam/Water Loop in Large Scale Ships

Cited by 12 publications

References 23 publications

Fractional Generalized Predictive Control Strategy With Fractional Constraints Handling

Fractional Generalized Predictive Control Strategy With Fractional Constraints Handling

Active Disturbance Rejection Control Design Based on Probabilistic Robustness for Uncertain Systems

An Advanced Fractional Order Method for Temperature Control

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