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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+toolbox/Model+Predictive+Control+Toolbox/pm41826393-1171-25-37
    Average 95 stars, based on 362 article reviews
    model predictive control - by Bioz Stars, 2026-10
    95/100 stars

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    Related Articles

    Control:

    Article Title: Impact of thermal stores on multi-energy microgrids with multi-layer dynamic control architecture
    Article Snippet: Thermal energy storage systems (TESSs) enhance multi-energy microgrids (MEMGs) operation by optimizing energy management.. While previous research primarily focused on optimizing the MEMG operation using static MEMG models, this paper analyzes the dynamic impact of TESS on a grid-connected residential MEMG.. This includes a photovoltaic plant, an electrical battery, and a hydrogen system with an electrolyzer, a fuel cell, and hydrogen tank.

    Article Title: Robust Platoon Control of Mixed Autonomous and Human-Driven Vehicles for Obstacle Collision Avoidance: A Cooperative Sensing-Based Adaptive Model Predictive Control Approach
    Article Snippet: .. This type of problem can be efficiently solved using various existing quadratic programming methods, such as active-set methods and interior-point methods, along with high-performance optimization solvers or toolboxes such as CPLEX (IBM, USA), Gurobi (Gurobi Optimization, USA), MOSEK (MOSEK ApS, Denmark), and the Model Predictive Control toolbox in MATLAB. ..

    Article Title: A Simulation-Based Comparative Study of Advanced Control Strategies for Residential Air Conditioning Systems
    Article Snippet: .. MPC Implementation and Real-Time Feasibility The MPC optimization was implemented using MATLAB’s (2013b) Model Predictive Control Toolbox within Simulink, employing the built-in ‘MPC Controller’ block. ..

    Article Title: Conditional adaptive time series compensation and control design for multi-axial real-time hybrid simulation
    Article Snippet: .. CATS-MPC was implemented using the MATLAB Model Predictive Control Toolbox. ..

    Article Title: Improvement of Drone Gimbal System Performance Using a Parallel Structure of Explicit Model Predictive Control and Adaptive Neuro-Fuzzy Inference System
    Article Snippet: .. The designed controller operates independently without relying on the Model Predictive Control Toolbox of Matlab/Simulink® and is applied to the drone gimbal system. ..

    Article Title: Automated administration of medical oxygen using model predictive control incorporating real-time monitoring of breathing parameters.
    Article Snippet: .. The MPC controller implements standard finite-horizon state-space MPC using MATLAB's Model Predictive Control Toolbox (R2022b), which solves the optimal control problem via numerical quadratic programming (QP) with active-set methods. ..

    Article Title: Improvement of Drone Gimbal System Performance Using a Parallel Structure of Explicit Model Predictive Control and Adaptive Neuro-Fuzzy Inference System
    Article Snippet: .. In this study, the F , G, H , and K matrices are computed using the Model Predictive Control Toolbox in Matlab/Simulink® [46]. ..

    Article Title: Workpiece temperature control in friction stir welding of Inconel 718 through integrated numerical analysis and process control
    Article Snippet: .. The MPC are designed using Simulink Model Predictive Control Toolbox. ..

    Blocking Assay:

    Article Title: A Simulation-Based Comparative Study of Advanced Control Strategies for Residential Air Conditioning Systems
    Article Snippet: .. MPC Implementation and Real-Time Feasibility The MPC optimization was implemented using MATLAB’s (2013b) Model Predictive Control Toolbox within Simulink, employing the built-in ‘MPC Controller’ block. ..



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    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