title: Neural Feedback Passivity of Unknown Nonlinear Systems via Sliding Mode Technique.
abstract: Passivity method is very effective to analyze large-scale nonlinear systems with strong nonlinearities. However, when most parts of the nonlinear system are unknown, the published neural passivity methods are not suitable for feedback stability. In this brief, we propose a novel sliding mode learning algorithm and sliding mode feedback passivity control. We prove that for a wide class of unknown nonlinear systems, this neural sliding mode control can passify and stabilize them. This passivity method is validated with a simulation and real experiment tests.
authors.forename: Wen
authors.initials: W
authors.lastname: Yu
mesh_terms: D000465:Algorithms; D019985:Benchmarking; D005246:Feedback; D000069550:Machine Learning; D015233:Models, Statistical; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D012984:Software