On Quadratic Programming Based Iterative Learning Control for Systems with Actuator Saturation Constraints

Fei Gao () and Richard W. Longman ()
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Fei Gao: Columbia University
Richard W. Longman: Columbia University, Department of Mechanical Engineering

A chapter in Modeling, Simulation and Optimization of Complex Processes - HPSC 2012, 2014, pp 37-49 from Springer

Abstract: Abstract When feedback control systems are given a commanded desired trajectory to perform, they produce a somewhat different trajectory. The concept of bandwidth is used to indicate what frequency components of the trajectory are executed reasonably well. Iterative Learning Control (ILC) iteratively changes the command, aiming to make the control system output match the desired output. The theory of linear ILC is reasonably well developed, but in hardware applications the nonlinear effects from hitting actuator saturation limits during the process of convergence of ILC could be detrimental to performance. Building on previous work by the authors and coworkers, this paper investigates the conversion of effective ILC laws into a quadratic cost optimization. And then it develops the modeling needed to impose actuator saturation constraints during the ILC learning process producing Quadratic Programming based ILC, or QP-ILC. The benefits and the need for ILC laws that acknowledge saturation constraints are investigated.

Keywords: Root Mean Square Error; Quadratic Programming; Inequality Constraint; Partial Isometry; Iterative Learn Control (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-09063-4_4

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DOI: 10.1007/978-3-319-09063-4_4

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