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Binary decision variables and logic constraints based on an along-track state are proposed for modeling switches in the framework of Model Predictive Control (MPC) so that overshoots are avoided.
We present a framework of model identification and evaluation that includes exploratory data analysis and model diagnostics and evaluation.
Furthermore, with the design framework of model predictive control, hard constraints on the derivative and amplitude of the control signals are imposed as part of the performance specification.
An adaptive optimal approach in the framework of model predictive control (MPC) is designed to coordinate the energy flow on the electrical system.
In this paper, we develop a hybrid design framework of model predictive controller (MPC) for multivariable systems that simultaneously and explicitly addresses the actuator saturation and backlash.
Second, we address the problem of controller-enhanced FDI in an optimal fashion within the framework of model predictive control (MPC).
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We define and study a quantitative generalization of the traditional boolean framework of model-based specification and verification.
These two seemingly divergent approaches to area estimation are encompassed within the general framework of model-assisted estimation.
This paper deals with a first application of the new framework of model-free control to the promising technology of shape memory alloys actuators.
This paper deals with an application of the new framework of model-free control (MFC) of an SMA-spring based actuator.
The feasibility and effectiveness of incorporating the proposed spatial-based Wiener printer model into the framework of model-based digital halftoning is also investigated.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com