Integrating robustness, optimality and constraints in control of nonlinear processes

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Robust predictive control of switched systems: Satisfying uncertain schedules subject to state and control constraints (2008, unknown): “control law [26] (see also [27]) can be constructed: ubc(x)= {−k(x)(LGV )′(x), ‖(LGV )T(x)‖ = 0 0, ‖(LGV )T(x)‖= 0 } (7)”
Stabilization of nonlinear systems with state and control constraints using Lyapunov-based predictive control (2005, methodology): “…of their stability properties has motivated significant work on the design of stabilizing control laws, using Lyapunov techniques (e.g., see [14], [4], [5]; the reader may refer to [12] for a survey of results in this area) that provide an explicit characterization of the region of guaranteed…”
Hybrid predictive control of nonlinear systems: method and applications to chemical processes (2004, methodology): “The class of Lyapunov-based bounded controllers in Reference [13] was extended in References [16, 17] to handle input constraints and model uncertainty within an inverse optimal control setting, and provide at the same time an explicit characterization of the constrained stability region.”