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Embedded Model Predictive Control with Explicit Computational Constraints


C.N. Jones

UCSD, San Diego, CA

One of the major challenges in modern embedded controller synthesis is the ability to specify available computational power and storage as input design parameters. Our current work develops new systematic methods for making design tradeoffs amongst online processing time, storage space and controller performance for high-speed applications. This talk will focus on approximate solutions to 'explicit model-predictive controllers', in which the control laws are pre-computed offline, making on-line computation fast and simple. The proposed framework allows the design engineer to specify the available online computational power and storage resources. It then synthesizes an approximate controller that guarantees both system stability and feasibility while maximizing performance within the allocated complexity without recourse to tweaking or manual tuning.


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M. Morari

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