Tuning Guidelines for Model-Predictive Control

Alhajeri, Mohammed S.; Soroush, Masoud

doi:10.1021/acs.iecr.9b05931

Cited by 49 publications

(18 citation statements)

References 80 publications

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“…The following terms were part of the cost function: control variable reference tracking, manipulated variable tracking, manipulated variable movement suppression, and constraint violation. The tuning of the controller is, despite available performance measures 34 and tuning guidelines, 35 an iterative process. The finally applied parameter can be found in Table S2.…”

Section: Resultsmentioning

confidence: 99%

Contributions towards variable temperature shielding for compact NMR instruments

Bornemann‐Pfeiffer

Meyer

Lademann

et al. 2023

Magnetic Reson in Chemistry

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The application of compact NMR instruments to hot flowing samples or exothermically reacting mixtures is limited by the temperature sensitivity of permanent magnets. Typically, such temperature effects directly influence the achievable magnetic field homogeneity and hence measurement quality. The internal‐temperature control loop of the magnet and instruments is not designed for such temperature compensation. Passive insulation is restricted by the small dimensions within the magnet borehole. Here, we present a design approach for active heat shielding with the aim of variable temperature control of NMR samples for benchtop NMR instruments using a compressed airstream which is variable in flow and temperature. Based on the system identification and surface temperature measurements through thermography, a model predictive control was set up to minimise any disturbance effect on the permanent magnet from the probe or sample temperature. This methodology will facilitate the application of variable‐temperature shielding and, therefore, extend the application of compact NMR instruments to flowing sample temperatures that differ from the magnet temperature.

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Section: Resultsmentioning

confidence: 99%

Contributions towards variable temperature shielding for compact NMR instruments

Bornemann‐Pfeiffer

Meyer

Lademann

et al. 2023

Magnetic Reson in Chemistry

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“…Compared to PID control, MPC is more flexible but also more complex, in particular regarding the number of tuning variables. Depending on the MPC formulation, the list of tuning variables include: prediction horizon, control horizon, weights on the output error, weights on the rates of change of manipulated variables, weights on the magnitudes of manipulated variables, reference trajectory parameters, and soft constraint weights [14]. In the following, we summarize applications of tuning strategies for MPC in grinding circuits reported in the literature: trial and error, expert knowledge, heuristics, and multiobjective optimization.…”

Section: Literature Reviewmentioning

confidence: 99%

Multiobjective Tuning Technique for MPC in Grinding Circuits

Yamashita

Martins²,

Pinto

et al. 2023

IEEE Access

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We investigate the control challenges in grinding circuits-slow dynamics, long dead times, variable coupling-and the controller tuning challenge, that is, the difficulty in translating operating goals into tuning goals and closed-loop performance. A tuning algorithm for DMC (dynamic matrix control), suitable for the mineral processing industry, is proposed. The tuning problem is posed as a multiobjective optimization problem, in which the tuning goals are directly related to the desired closed-loop performance of process variables. The problem is solved using a compromise optimization, which minimizes the Euclidian distance between a feasible solution and the Utopia solution. Three case studies are presented, which validate the tuning algorithm for DMC in linear and non-linear grinding circuit models. The closedloop performance obtained with the proposed tuning algorithm is compared to the one obtained through a benchmark tuning technique from the literature. The proposed tuning method has the following features: i) it shapes the closed-loop response according to the goal definitions for linear systems; ii) it requires tailored initial guesses and search spaces to converge to a stabilizing solution in non-linear applications; and iii) it allows the user to specify the desired closed-loop performance behavior in the tuning procedure, allowing the implementation of an adequate controller for each situation.INDEX TERMS grinding circuit, model predictive control, dynamic matrix control, controller tuning, multiobjective optimization, compromise optimization.

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“…. , 7} in the cost functional in (15) are tuned, according to the trail and error procedure [49,50], as: ω 1 = 10; ω 2 = 10; ω 3 = 20; ω 4 = 20; ω 5 = 0.1; ω 6 = 0.1; ω 7 = 10. Note that this choice of selecting the same weighting factors for both the appraised scenario guarantees the fairness of the comparison analysis.…”

Section: Case Studymentioning

confidence: 99%

Distributed Nonlinear Model Predictive Control for Connected Autonomous Electric Vehicles Platoon with Distance-Dependent Air Drag Formulation

et al. 2021

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This paper addresses the leader tracking problem for a platoon of heterogeneous autonomous connected fully electric vehicles where the selection of the inter-vehicle distance between adjacent vehicles plays a crucial role in energy consumption reduction. In this framework, we focused on the design of a cooperative driving control strategy able to let electric vehicles move as a convoy while keeping a variable energy-oriented inter-vehicle distance between adjacent vehicles which, depending on the driving situation, was reduced as much as possible to guarantee air-drag reduction, energy saving and collision avoidance. To this aim, by exploiting a distance-dependent air drag coefficient formulation, we propose a novel distributed nonlinear model predictive control (DNMPC) where the cost function was designed to ensure leader tracking performances, as well as to optimise the inter-vehicle distance with the aim of reducing energy consumption. Extensive simulation analyses, involving a comparative analysis with respect to the classical constant time headway (CTH) spacing policy, were performed to confirm the capability of the DNMPC in guaranteeing energy saving.

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Tuning Guidelines for Model-Predictive Control

Cited by 49 publications

References 80 publications

Contributions towards variable temperature shielding for compact NMR instruments

Contributions towards variable temperature shielding for compact NMR instruments

Multiobjective Tuning Technique for MPC in Grinding Circuits

Distributed Nonlinear Model Predictive Control for Connected Autonomous Electric Vehicles Platoon with Distance-Dependent Air Drag Formulation

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