27 citations · 37 across the 6 of their papers we have counts for
7 papers
Convex Parameterizations and Fidelity Bounds for Nonlinear Identification and Reduced-Order Modelling
Mark M. Tobenkin, Ian R. Manchester, Alexandre Megretski
Model instability and poor prediction of long-term behavior are common problems when modeling dynamical systems using nonlinear "black-box" techniques. Direct optimization of the l…
Technical Report: Convex Optimization of Nonlinear Feedback Controllers via Occupation Measures
Anirudha Majumdar, Ram Vasudevan, Mark M. Tobenkin +1
In this paper, we present an approach for designing feedback controllers for polynomial systems that maximize the size of the time-limited backwards reachable set (BRS). We rely on…
Stable Nonlinear Identification From Noisy Repeated Experiments via Convex Optimization
Mark M. Tobenkin, Ian R. Manchester, Alexandre Megretski
This paper introduces new techniques for using convex optimization to fit input-output data to a class of stable nonlinear dynamical models. We present an algorithm that guarantees…
Identification of Nonlinear Systems with Stable Limit Cycles via Convex Optimization
Ian R. Manchester, Mark M. Tobenkin, Jennifer Wang
We propose a convex optimization procedure for black-box identification of nonlinear state-space models for systems that exhibit stable limit cycles (unforced periodic solutions).…
Invariant Funnels around Trajectories using Sum-of-Squares Programming
Mark M. Tobenkin, Ian R. Manchester, Russ Tedrake
This paper presents numerical methods for computing regions of finite-time invariance (funnels) around solutions of polynomial differential equations. First, we present a method wh…
Regions of Attraction for Hybrid Limit Cycles of Walking Robots
Ian R. Manchester, Mark M. Tobenkin, Michael Levashov +1
This paper illustrates the application of recent research in region-of-attraction analysis for nonlinear hybrid limit cycles. Three example systems are analyzed in detail: the van…
Convex Optimization In Identification Of Stable Non-Linear State Space Models
Mark M. Tobenkin, Ian R. Manchester, Jennifer Wang +2
A new framework for nonlinear system identification is presented in terms of optimal fitting of stable nonlinear state space equations to input/output/state data, with a performanc…