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Found 4 papers, 0 papers with code
Concurrent learning in high-order tuners for parameter identification
High-order tuners are algorithms that show promise in achieving greater efficiency than classic gradient-based algorithms in identifying the parameters of parametric models and/or in facilitating the progress of a control or optimization algorithm whose adaptive behavior relies on such models.
Passive soft-reset controllers for nonlinear systems
Soft-reset controllers are introduced as a way to approximate hard-reset controllers.
Analyzing the Effect of Persistent Asset Switches on a Class of Hybrid-Inspired Optimization Algorithms
Convex optimization challenges are currently pervasive in many science and engineering domains.
Hybrid Heavy-Ball Systems: Reset Methods for Optimization with Uncertainty
Momentum methods for convex optimization often rely on precise choices of algorithmic parameters, based on knowledge of problem parameters, in order to achieve fast convergence, as well as to prevent oscillations that could severely restrict applications of these algorithms to cyber-physical systems.
