Survey of gain-scheduling analysis and design

By Douglas J. Leith, William Edward Leithead. 2000

963 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/97990b60a6418eca213870fef3d04e717ec0567d

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In Control and Systems Engineering

Citations in the corpus

A Nonlinear Optimization Approach to H2-Optimal Modeling and Control (2013, background): “, Rugh and Shamma [2000], Leith and Leithead [2000], Tóth [2008], Lovera et al.”
Polynomial LPV synthesis applied to turbofan engines (2007, methodology): “mances over the whole operating range, see Leith and Leithead (2000) ; Rugh”
Reinforcement Learning for UAV Attitude Control (2018, methodology): “Gain scheduling [26] was developed allowing multiple linear controllers of different configurations to be used in designated operating regions.”
Parameter-dependent state observer design for affine LPV systems (2001, methodology): “For a review of the main theoretical results and design procedures related to gainscheduling, the reader can refer to Leith and Leithead (2000), Rugh and Shamma (2000) and the references therein. As far as interpolation based methods are concerned, some ad hoc approaches have been reported in the li”
Development of linear-parameter-varying models for aircraft (2004, methodology): “There are several approaches that can be used to obtain a reliable LPV/quasi-LPV model, reference [13, 14, 2, 18].”
Interpolating model identification for SISO linear parameter-varying systems (2009, background): “Introduction For more than a decade, linear parameter-varying (LPV) synthesis procedures have received a lot of attention from the control community (for instance, see [1–4]).”
Space-indexed dynamic programming: learning to follow trajectories (2008, background): “The idea of partitioning the state space into regions, and using different controllers in the different regions, is a common practice in control, and often is referred to as gain-scheduling ( Leith & Leithead, 2000 ).”
Machine learning of motor skills for robotics (2007, neutral): “, gain scheduling (Leithead, 2000) and backchaining (Lozano-Perez, Mason, & Taylor, 1984; Burridge, Rizzi, & Koditschek, 1999).”
A Survey of Linear Parameter-Varying Control Applications Validated by Experiments or High-Fidelity Simulations (2015, background): “suitability of the LPV framework for the control of general nonlinear systems arises from the fact that nonlinear state space models can be brought into the so-called quasi-LPV form [2], [9], [10], in which parameters can be functions of the states, inputs or outputs, instead of only exogenous signa”
The State of Charge Estimation of Lithium-Ion Batteries Based on a Proportional-Integral Observer (2014, methodology): “To deal with this problem and simplify the computation, a gain scheduling method [24] is introduced, which typically employs an approach whereby the nonlinear system is decomposed into a number of linear subsystems.”
Advanced control in factory automation: a survey (2017, background): “A review of gain scheduling techniques may be found in Leith and Leithead (2000) and Landau et al. (2011).”