An LPV polytopic embedding with integral mean value representation and LMI synthesis yields constant-gain and gain-scheduled observers ensuring exponential convergence for nonlinear port-Hamiltonian systems with state-dependent inputs.
van der Schaft,L2-Gain and Passivity Techniques in Nonlinear Control, ser
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
math.OC 2verdicts
UNVERDICTED 2representative citing papers
Develops a dissipativity and contraction theory framework for convergence analysis of distributed optimization algorithms, producing LMI conditions for arbitrary network structures.
citing papers explorer
-
Observer design for classes of nonlinear port-Hamiltonian systems
An LPV polytopic embedding with integral mean value representation and LMI synthesis yields constant-gain and gain-scheduled observers ensuring exponential convergence for nonlinear port-Hamiltonian systems with state-dependent inputs.
-
Convergence Analysis of Distributed Optimization: A Dissipativity Framework
Develops a dissipativity and contraction theory framework for convergence analysis of distributed optimization algorithms, producing LMI conditions for arbitrary network structures.