User-friendly cross-directional MPC tuning for uncertain multiple-array paper-making processes

He, Ning; Forbes, Michael G.; Backström, Johan U.; Chen, Tongwen

doi:10.1016/j.conengprac.2019.05.004

Cited by 17 publications

(10 citation statements)

References 16 publications

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“…in which G ij (s) demonstrates the transfer function between the i th output and the j th input. Take the common FOPDT model structure for each subsystem following the industrial experience [9,10], which can be described as follows:…”

Section: Preliminary and Problem Formulationmentioning

confidence: 99%

“…In [8], the authors reduced the number of effective tuning parameters by modifying the controller structure and redesigned the MPC cost function properly. In [9], two robust tuning strategies are put forward for SISO uncertain paper-making processes which incorporated the total variation specification to user-friendly performance indices. In [10], the authors further proposed a rapid tuning strategy based on the closed-loop system structure for MPC parameters for MIMO paper-making system with first-orderplus-dead-time subsystems and uncertain model parameters.…”

Section: Introductionmentioning

confidence: 99%

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An Improved Approach for Robust MPC Tuning Based on Machine Learning

Zhang

2021

Mathematical Problems in Engineering

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A robust tuning method based on an artificial neural network for model predictive control (MPC) of industrial systems with parametric uncertainties is put forward in this work. Firstly, an efficient approach to characterize the mapping relationship between the controller parameters and the robust performance indices is established. As there are normally multiple conflicted robust performance indices to be considered in MPC tuning, the neural network is further used to fuse the indices to produce a simple label representing the acceptable level of the robust performance. Finally, an automated algorithm is proposed to tune the MPC parameters for the considered uncertain system to achieve the desired robust performance. In addition, the regulation of the pH value of the sewage treatment system is used to verify the effectiveness of the robust tuning algorithm which is described in this paper.

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Section: Preliminary and Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Approach for Robust MPC Tuning Based on Machine Learning

Zhang

2021

Mathematical Problems in Engineering

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“…Model predictive control (MPC) is a feedback control technique to obtain control signals by solving an optimal control problem (OCP) online [1][2][3]. Benefited from the competitive advantages in dealing with the input/output coupling problems of multivariable systems and explicitly considering the physical constraints of system variables, it has been widely used in different kinds of industrial systems, such as intelligent transportation systems, paper-making processes, smart grid systems, intelligent building operations, and so on [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

An error gradient and accumulation‐type event‐driven model predictive control with relative thresholds for perturbed nonlinear systems

Shen

2022

IET Control Theory & Appl

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This work investigates the event-driven model predictive control problem for perturbed nonlinear systems with input and state constraints. Firstly, a new event-driven triggering condition, considering the gradient and accumulation of the error between the optimal and actual states at adjacent moments with a state-dependent relative threshold is designed with a guaranteed positive inter-event time to avoid Zeno behaviour. Secondly, an error gradient and accumulation type event-driven model predictive control framework is designed based on the new triggering condition and the dual-mode strategy, for effectively reducing the calculation and communication burden of the model predictive control controller while ensuring desired control performance. Furthermore, the feasibility and the input-to-state practical stability (ISpS) of gradient and accumulation-type event-driven model predictive control are rigorously guaranteed theoretically. At last, the simulation results are provided to validate the this algorithm.

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“…Model predictive control (MPC) has been applied in petroleum, electric power, aviation and other industries successfully and developed rapidly. This is mainly profit from its advantages in handling multiple input multiple output issues, especially those with control and input constraints [1][2][3][4]. MPC has a distinct advantage over other control methods, for example PID control in that it can more effectively handle constraints in real time while still taking into account the system future behaviour [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

A quasi‐differential type event‐triggered model predictive control for perturbed continuous linear systems with constraints

et al. 2021

IET Control Theory & Appl

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A quasi‐differential type event‐triggered model predictive control (ET‐MPC) framework for continuous‐time linear systems with additional disturbances is constructed. Different from the existing ET‐MPC, the triggering condition of the proposed method is focused on the differences of the errors between the actual states and the best prediction sequence at two consecutive sampling moments. Its advantage is that the dynamic characteristics of state changes can be better considered, which will achieve a more effective balance between communication load and system control performance. Then, to deal with persistent disturbances, a time‐varying tightening robustness state constraint is utilized in this scheme, instead of conventional state constraints. Finally, the new ET‐MPC algorithm is designed based on the dual‐mode control framework, and its feasibility, the system closed‐loop stability, as well as avoidance of Zeno behaviour are rigorously verified. Simulations and comparisons are provided to demonstrate the algorithm effectiveness.

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User-friendly cross-directional MPC tuning for uncertain multiple-array paper-making processes

Cited by 17 publications

References 16 publications

An Improved Approach for Robust MPC Tuning Based on Machine Learning

An Improved Approach for Robust MPC Tuning Based on Machine Learning

An error gradient and accumulation‐type event‐driven model predictive control with relative thresholds for perturbed nonlinear systems

A quasi‐differential type event‐triggered model predictive control for perturbed continuous linear systems with constraints

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