Linear model predictive control for transport‐reaction processes

Xu, Qi; Dubljević, Stevan

doi:10.1002/aic.15592

Cited by 44 publications

(30 citation statements)

References 32 publications

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“…The control vector reduces a defined cost function in excess of the prediction horizon in the occurrence of constraints and disturbances. 35 The first part of the considered control vector at each sampling instant is supplied to the input of the system, and the rest is refused. The whole procedure is repetitive in the next time instant.…”

Section: Mpcmentioning

confidence: 99%

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Design of model predictive force control for hydraulic servo system based on cuckoo search and genetic algorithms

Essa¹,

Aboelela

Hassan

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article discusses a system identification based on a black-box state-space model for an experimental electro-hydraulic servo system. Furthermore, it presents force-tracking control for the electro-hydraulic servo system based on model predictive control. The parameters of model predictive controls have been tuned by cuckoo search algorithm as well as genetic algorithm. The realization of model predictive controls depends on using a data acquisition card (NI-6014) and Simulink/MATLAB as the core of the electro-hydraulic servo system control system. In this research, the combination of model predictive control tuned by cuckoo search algorithm and genetic algorithm has been introduced in the form of switching model predictive controls. This combination collects the advantages of two model predictive controls in one model predictive control by switching model predictive controls. The simulation and experimental results display that the suggested switching of model predictive controls introduces a good tracking performance in terms of settling time, rise time, and system overshoots as compared to the two separated model predictive controls. In addition, the experimental evaluation has shown that the proposed switching model predictive controls achieved a stable and robust control system even facing to a different reference command signals (step, multistep, and sinusoidal signals). Moreover, its behavior is more robust for system parameters perturbation and small or large perturbation of disturbances in the working environment. It also achieves the necessitated physical limits of the actuator. As a general conclusion and a deep study of electro-hydraulic servo system, one can conclude that the switching strategy between model predictive control tuned by cuckoo search algorithm and by genetic algorithm has the priority of applying it on the field of electro-hydraulic servo system. The proposed new strategy (switching of model predictive control) is promising in experimental applications.

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Section: Mpcmentioning

confidence: 99%

“…The whole procedure is repetitive in the next time instant. 34,35 The cost function takes the shape of the error of path, effort of control and cost of energy, or a group of these factors.…”

Section: Mpcmentioning

confidence: 99%

Design of model predictive force control for hydraulic servo system based on cuckoo search and genetic algorithms

Essa¹,

Aboelela

Hassan

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part I:

View full text Add to dashboard Cite

show abstract

“…Along this line of late lumping, this work utilizes the Crank-Nicolson time discretization framework for late lumping of DPSs, which can ensure a symmetric and symplectic numerical integration of linear systems without spatial approximation or model reduction [69,[72][73][74].…”

Section: Model Discretizationmentioning

confidence: 99%

Discrete-Time Kalman Filter Design for Linear Infinite-Dimensional Systems

Xie

Dubljević

2019

Processes

Self Cite

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As the optimal linear filter and estimator, the Kalman filter has been extensively utilized for state estimation and prediction in the realm of lumped parameter systems. However, the dynamics of complex industrial systems often vary in both spatial and temporal domains, which take the forms of partial differential equations (PDEs) and/or delay equations. State estimation for these systems is quite challenging due to the mathematical complexity. This work addresses discrete-time Kalman filter design and realization for linear distributed parameter systems. In particular, the structural- and energy-preserving Crank–Nicolson framework is applied for model time discretization without spatial approximation or model order reduction. In order to ensure the time instance consistency in Kalman filter design, a new discrete model configuration is derived. To verify the feasibility of the proposed design, two widely-used PDEs models are considered, i.e., a pipeline hydraulic model and a 1D boundary damped wave equation.

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“…MPC is an advanced technique of process control in industries such as chemical plants and oil refineries since the 1980s (Dotoli et al, 2017). It depends on capturing the dynamic models of the process that are most often linear experimental models built by system identification (Xu and Stevan, 2017).…”

Section: Control Strategiesmentioning

confidence: 99%

Model predictive force control of hardware implementation for electro-hydraulic servo system

Essa¹,

Aboelela

Hassan

et al. 2018

Transactions of the Institute of Measurement and Control

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This manuscript introduces the application of Model Predictive Control (MPC) for high force control precision in a real industrial electro-hydraulic servo system (EHSS). Moreover, it presents a fractional order control (FOC) and conventional controllers (CC) based on genetic algorithm (GA). The GA technique has been used to tune the parameters of FOC and CC approach. In order to verify the ability of the proposed controller applied to the hydraulic press machines emulator using EHSS, a hardware implementation of a test press system is also suggested and setup to be used in this research. As a result, the study has been investigated using a simulation model then verified via the experimental implementation. In fact, the EHSS plays an important role in many industrial applications, especially in flight simulators, aircraft landing gear system, material testing machine and hydraulic press machines for which the high accuracy and fast response of the force or pressure control are exceedingly necessary. Real-time experiments on the EHSS are carried out to evaluate the proposed control approach in a large system parameters variation of working environments. Considerable improvement in the performance generated by the designed MPC controller is compared with the traditional and fractional order controllers. Moreover, the results show that the performance criteria in terms of settling, rise times, system overshoots, system parameters variation and applying different test signals are good values in case of applying MPC over using FOC and CC in this study. As a general conclusion, one can conclude that the MPC has the priority of applying it in the field of the industrial EHSS. The obtained results are promising in the field of mechatronic.

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Linear model predictive control for transport‐reaction processes

Cited by 44 publications

References 32 publications

Design of model predictive force control for hydraulic servo system based on cuckoo search and genetic algorithms

Design of model predictive force control for hydraulic servo system based on cuckoo search and genetic algorithms

Discrete-Time Kalman Filter Design for Linear Infinite-Dimensional Systems

Model predictive force control of hardware implementation for electro-hydraulic servo system

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