Merging two Control Lyapunov Functions (CLFs) means creating a single “new-born” CLF by starting from two parents functions. Specifically, given a “father” function, shaped by the state constraints, and a “mother” function, designed with some optimality criterion, the merging CLF should be similar to the father close to the constraints and similar to the mother close to the origin. To successfully merge two CLFs, the control-sharing condition is crucial: the two functions must have a common control law that makes both Lyapunov derivatives simultaneously negative. Unfortunately, it is difficult to guarantee this property a-priori, i.e., while computing the two parents functions. To create a constraint-shaped “father” function that has the control-sharing property with the “mother” function, we introduce a partial control-sharing i.e., the control-sharing only in the regions where the constraints are active. We show that imposing partial control-sharing is a convex optimization problem. Finally, the partial control-sharing is used to merge constraint-shaped and the Riccati-optimal functions, thus generating a CLF with bounded complexity that solves the constrained linear-quadratic stabilization problem with local optimality.

### On merging constraint and optimal control-Lyapunov functions

#### Abstract

Merging two Control Lyapunov Functions (CLFs) means creating a single “new-born” CLF by starting from two parents functions. Specifically, given a “father” function, shaped by the state constraints, and a “mother” function, designed with some optimality criterion, the merging CLF should be similar to the father close to the constraints and similar to the mother close to the origin. To successfully merge two CLFs, the control-sharing condition is crucial: the two functions must have a common control law that makes both Lyapunov derivatives simultaneously negative. Unfortunately, it is difficult to guarantee this property a-priori, i.e., while computing the two parents functions. To create a constraint-shaped “father” function that has the control-sharing property with the “mother” function, we introduce a partial control-sharing i.e., the control-sharing only in the regions where the constraints are active. We show that imposing partial control-sharing is a convex optimization problem. Finally, the partial control-sharing is used to merge constraint-shaped and the Riccati-optimal functions, thus generating a CLF with bounded complexity that solves the constrained linear-quadratic stabilization problem with local optimality.
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2018
978-1-5386-1395-5
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Utilizza questo identificativo per citare o creare un link a questo documento: `https://hdl.handle.net/20.500.11771/25766`