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model predictive control  (MathWorks Inc)


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    MathWorks Inc model predictive control
    Model Predictive Control, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 95/100, based on 362 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/model+predictive+control/Model+Predictive+Control+Toolbox/pm41826393-1171-25-37
    Average 95 stars, based on 362 article reviews
    model predictive control - by Bioz Stars, 2026-09
    95/100 stars

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    Control:

    Article Title: Comparison of Fuzzy Logic Control and Model Predictive Control for a Smart Adaptive Cruise Control Vehicle System
    Article Snippet: Adaptive cruise control (ACC), cruise control (CC), and automatic emergency braking (AEB) serve as the basis of longitudinal automated driving, and as such have been the subject of much research.. Model predictive control (MPC) and fuzzy logic are often considered to be the next steps in improving the capability of these systems, but the two control strategies have not been compared to each other in the ACC, CC and AEB applications.. Also, the three features (ACC, CC and AEB) have never been compiled into a single fuzzy logic controller.

    Article Title: Model predictive control of a magnetic levitation system with prescribed output tracking performance
    Article Snippet: .. Model predictive control mit MATLAB und simulink. ..

    Article Title: MagnetoShield: A Novel Open-Source Magnetic Levitation Benchmark Device for Mechatronics Education and Research
    Article Snippet: .. Worked examples of the model predictive control of magnetic levitation are provided for the Arduino mega2560 and Due boards. demonstrates the EMPC tracking of a reference distance implemented in Simulink and deployed on the Arduino Due. ..

    Article Title: Towards a modelling, optimization and predictive control framework for smart irrigation
    Article Snippet: .. Schematic of model predictive control in Simulink. ..

    Article Title: Predictive temperature control of electric two wheeler hub motor using gradient aware neural regulation with degradation tracking and fault tolerant multi condition torque adaptation.
    Article Snippet: .. To quantify the relative efficacy of the proposed Hybrid GANR approach, it was benchmarked against three advanced control strategies—Adaptive PID, Fuzzy Logic Controller (FLC), and Model Predictive Control (MPC)—using identical thermal load and drive cycle datasets within MATLAB/Simulink. ..

    Article Title: Optimizing the position of photovoltaic solar tracker panels with artificial intelligence using MATLAB Simulink
    Article Snippet: Fahmida et al. [25] simulated the maximum power point position of PV using MATLAB simscape. .. Ikhwan et al. [26] applied model predictive control to dual-axis solar trackers using MATLAB Simulink simulations. ..

    Article Title: Optimal operation of a natural gas sweetening plant
    Article Snippet: The most common method of removing acid gases from natural gas is through the use of solvent-based gas sweetening plants, which are energy-intensive.. The present study aims to establish a plant-wide structure to operate natural gas sweetening plants optimally.. Thus, a steady-state simulation of the Khangiran plant was carried out, and a 17% reduction in energy usage was achieved as a result of the optimization by the application of surrogate models.



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    model predictive control toolkit  (MathWorks Inc)
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    MathWorks Inc model predictive control toolkit
    Open-top light-sheet microscope with electrically tunable lens (ETL) remote focusing under model <t>predictive</t> control. (a) Scan methods for volumetric light-sheet imaging. (i) In the simplest case, a sample is scanned through a static sheet. (ii) An actuated objective lens can follow a scanning light-sheet, which is faster than (i) but inertia limited and can cause problems with water immersion. (iii) Remote focusing optically shifts the focal plane using an active element like an ETL (b). This setup has less inertia and no moving parts at the sample. (c) Long-working distance open-top imaging is achieved using an asymmetric pair of objective lenses coupled with a water immersion fitting. This enables unobstructed imaging across a water-matched barrier such as FEP (above). (d) Maximum intensity projections along the Z (top) and Y (bottom) axes of a worm expressing a pan-neuronal nuclear-localized fluorescent protein positioned in a microfluidic channel as shown in (c). Scale bar 20 µ m. (e) Fast actuation of an ETL induces high frequency oscillation which slows response time. Performance is improved by using model predictive control to optimize drive signals. The controller iteratively optimizes the input signal to the ETL by minimizing simulated output error while obeying system constraints.
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    Image Search Results


    Open-top light-sheet microscope with electrically tunable lens (ETL) remote focusing under model predictive control. (a) Scan methods for volumetric light-sheet imaging. (i) In the simplest case, a sample is scanned through a static sheet. (ii) An actuated objective lens can follow a scanning light-sheet, which is faster than (i) but inertia limited and can cause problems with water immersion. (iii) Remote focusing optically shifts the focal plane using an active element like an ETL (b). This setup has less inertia and no moving parts at the sample. (c) Long-working distance open-top imaging is achieved using an asymmetric pair of objective lenses coupled with a water immersion fitting. This enables unobstructed imaging across a water-matched barrier such as FEP (above). (d) Maximum intensity projections along the Z (top) and Y (bottom) axes of a worm expressing a pan-neuronal nuclear-localized fluorescent protein positioned in a microfluidic channel as shown in (c). Scale bar 20 µ m. (e) Fast actuation of an ETL induces high frequency oscillation which slows response time. Performance is improved by using model predictive control to optimize drive signals. The controller iteratively optimizes the input signal to the ETL by minimizing simulated output error while obeying system constraints.

    Journal: bioRxiv

    Article Title: High speed functional imaging with a microfluidics-compatible open-top light-sheet microscope enabled by model predictive control of a tunable lens

    doi: 10.1101/2025.07.23.666439

    Figure Lengend Snippet: Open-top light-sheet microscope with electrically tunable lens (ETL) remote focusing under model predictive control. (a) Scan methods for volumetric light-sheet imaging. (i) In the simplest case, a sample is scanned through a static sheet. (ii) An actuated objective lens can follow a scanning light-sheet, which is faster than (i) but inertia limited and can cause problems with water immersion. (iii) Remote focusing optically shifts the focal plane using an active element like an ETL (b). This setup has less inertia and no moving parts at the sample. (c) Long-working distance open-top imaging is achieved using an asymmetric pair of objective lenses coupled with a water immersion fitting. This enables unobstructed imaging across a water-matched barrier such as FEP (above). (d) Maximum intensity projections along the Z (top) and Y (bottom) axes of a worm expressing a pan-neuronal nuclear-localized fluorescent protein positioned in a microfluidic channel as shown in (c). Scale bar 20 µ m. (e) Fast actuation of an ETL induces high frequency oscillation which slows response time. Performance is improved by using model predictive control to optimize drive signals. The controller iteratively optimizes the input signal to the ETL by minimizing simulated output error while obeying system constraints.

    Article Snippet: Finally, we used the generated model to construct a model predictive controller using the MATLAB Model Predictive Control toolkit.

    Techniques: Microscopy, Control, Imaging, Expressing