Flatness and Backstepping Designs
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- Control of Higher–Dimensional PDEs.
- Control of Higher-Dimensional PDEs?
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Review From the reviews: "This research monograph is designed for graduate students in applied mathematics and control theory and also as a reference for control engineers and mathematics researchers working in the control of PDEs. The text is divided into five parts featuring: - a literature survey of paradigms and control design methods for PDE systems - the first principle mathematical modeling of applications arising in heat and mass transfer, interconnected multi-agent systems, and piezo-actuated smart elastic structures - the generalization of flatness-based trajectory planning and feedforward control to parabolic and biharmonic PDE systems defined on general higher-dimensional domains - an extension of the backstepping approach to the feedback control and observer design for parabolic PDEs with parallelepiped domain and spatially and time varying parameters - the development of design techniques to realize exponentially stabilizing tracking control - the evaluation in simulations and experiments Control of Higher-Dimensional PDEs -- Flatness and Backstepping Designs is an advanced research monograph for graduate students in applied mathematics, control theory, and related fields.
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Control of Higher-Dimensional PDEs: Flatness and Backstepping Designs by Thomas Meurer - farseturra.tk
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Term since In view of this, automation, control and optimization can be identified as key ingredients to achieve energy efficient building operation. Since buildings inherently evolve along multiple time and spatial scales, mathematical modeling typically leads to a system representation in terms of partial differential equations PDEs. Based on this distributed-parameter system description, this project aims at the development of flatness-based optimal and model predictive control MPC design methods for PDEs.
This includes the design of state observers to enable the realization of the MPC schemes as well as the efficient numerical implementation using appropriate reduced-order models.