English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper develops a data-based approach to the closed-loop output feedbackcontrol of nonlinear dynamical systems with a partial nonlinear observationmodel. We propose an information state based approach to rigorously transformthe partially observed problem into a fully observed problem where theinformation state consists of the past several observations and control inputs.We further show the equivalence of the transformed and the initial partiallyobserved optimal control problems and provide the conditions to solve for thedeterministic optimal solution. We develop a data based generalization of theiterative Linear Quadratic Regulator (iLQR) to partially observed systems usinga local linear time varying model of the information state dynamicsapproximated by an Autoregressive moving average (ARMA) model, that isgenerated using only the input-output data. This open-loop trajectoryoptimization solution is then used to design a local feedback control law, andthe composite law then provides an optimum solution to the partially observedfeedback design problem. The efficacy of the developed method is shown bycontrolling complex high dimensional nonlinear dynamical systems in thepresence of model and sensing uncertainty.

An Information-state based Approach to the Optimal Output Feedback Control of Nonlinear Systems