NDLI: Efficient Model Predictive Algorithms for Tracking of Periodic Signals

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Efficient Model Predictive Algorithms for Tracking of Periodic Signals

Content Provider	Semantic Scholar
Author	Chu, Yun-Chung Chen, Michael Z. Q.
Copyright Year	2012
Abstract	This paper studies the design of efficient model predictive controllers for fast-sampling linear time-invariant systems subject to input constraints to track a set of periodic references. The problem is decomposed into a steady-state subproblem that determines the optimal asymptotic operating point and a transient subproblem that drives the given plant to this operating point. While the transient subproblem is a small-sized quadratic program, the steady-state subproblem can easily involve hundreds of variables and constraints. The decomposition allows these two subproblems of very different computational complexities to be solved in parallel with different sampling rates. Moreover, a receding horizon approach is adopted for the steady-state subproblem to spread the optimization over time in an efficient manner, making its solution possible for fast-sampling systems. Besides the conventional formulation based on the control inputs as variables, a parameterization using a dynamic policy on the inputs is introduced, which further reduces the online computational requirements. Both proposed algorithms possess nice convergence properties, which are also verified with computer simulations.
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://web.hku.hk/~mzqchen/JCSE_729748.pdf
Language	English
Access Restriction	Open
Subject Keyword	Algorithm Analysis of algorithms Bibliographic Reference Computation Computer simulation Convergence (action) Entity Name Part Qualifier - adopted Intrinsic drive Linear time-invariant theory Mathematical optimization Mesenchymal Stem Cells Operating point Quadratic programming Requirement Sampling (signal processing) Sampling - Surgical action Steady state Time complexity Time-invariant system
Content Type	Text
Resource Type	Article

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