Nonlinear tracking control in the presence of state and control constraints: a generalized reference governor

By Elmer G. Gilbert, Ilya V. Kolmanovsky. at - Automatisierungstechnik, 2002

395 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/a45fc6624fb9ec10d7878f4d7bd15d9a74528acb

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In Control and Systems Engineering

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Citations in the corpus

• Barrier Lyapunov functions-based adaptive control for nonlinear pure-feedback systems with time-varying full state constraints (2017, background): “Many fruitful results on constraint-handling methods have been generated, such as the model predictive control [2, 6], the set invariance notions [7, 8] and reference governors [9, 10].”
• Barrier Lyapunov functions-based dynamic surface control for pure-feedback systems with full state constraints (2017, methodology): “There exist various methods to handle constraints including model predictive control [1], the set invariance notions [2, 3], reference governors [4, 5] and so on.”
• Reference and command governors for systems with constraints: A survey on theory and applications (2017, background): “For such generalized nonlinear reference governor, recursive feasibility and finite-time convergence properties hold under suitable assumptions, see [100].”
• Barrier Lyapunov Functions for the control of output-constrained nonlinear systems (2009, methodology): “Existing methods to handle constraints include model predictive control (Allgöwer, Findeisen, & Ebenbauer, 2003; Mayne, Rawlings, Rao, & Scokaert, 2000), reference governors (Bemporad, 1998; Gilbert & Kolmanovsky, 2002), and the use of set invariance notions (Hu & Lin, 2001; Liu & Michel, 1994).”
• Control of nonlinear systems with time-varying output constraints (2009, positive): “Reference governors, which modulate the reference signal to avoid any violation of system constraints, have also been employed [6], [7].”
• Control of nonlinear systems with partial state constraints using a barrier Lyapunov function (2011, methodology): “On the other hand, RG-based control works by modulating the reference signal, using online optimisation algorithms, to avoid any violation of system constraints (Bemporad 1998; Gilbert and Kolmanovsky 2002; Gilbert and Ong 2009).”
• Removing the Feasibility Conditions Imposed on Tracking Control Designs for State-Constrained Strict-Feedback Systems (2019, methodology): “There are numerous methods for dealing with state constraints in the literature, including model predictive control [20], reference governors [1], [3], and set invariance notions [2], [7], [8].”
• Offset‐free receding horizon control of constrained linear systems (2005, methodology): “Existing control algorithms, which address the problem of robust control of constrained s ystems, are usually based on ideas from set invariance [8, 9], reference governors [10, 11, 12, 13] or re ceding horizon control [14, 15, 16, 17, 18, 19, 20, 21, 22].”