Classical/neural synthesis of nonlinear control systems

Silvia Ferrari, Robert F. Stengel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Classical/neural synthesis of control systems combines the most effective elements of old and new design concepts with the promise of producing better control systems. There is considerable precedent for applying gain-scheduled linear controllers to nonlinear systems, especially those that can be locally approximated as linear- parameter-varying systems; however, a means for transferring the insights gained from these linear controllers to nonlinear controllers remains to be identified. The approach taken here is to design nonlinear control systems that take advantage of prior knowledge and experience gained from linear controllers, while capitalizing on the broader capabilities of adaptive, nonlinear control theory and computational neural networks. Central to this novel approach is the recognition that the gradients of a nonlinear control law must represent the gain matrices of an equivalent, locally linearized controller. In this paper we focus on the initial specification for the control law, which consists of a set of hypersurfaces expressed as neural networks that represent satisfactory linear controllers designed over the plant's operating range. Along the way, a new neural network training method consisting solely of solving algebraic linear systems of equations is developed, and its effectiveness is demonstrated on a case study.

Original languageEnglish (US)
Title of host publicationAIAA Guidance, Navigation, and Control Conference and Exhibit
PublisherAmerican Institute of Aeronautics and Astronautics Inc.
ISBN (Print)9781563479786
DOIs
StatePublished - Jan 1 2000
EventAIAA Guidance, Navigation, and Control Conference and Exhibit 2000 - Dever, CO, United States
Duration: Aug 14 2000Aug 17 2000

Publication series

NameAIAA Guidance, Navigation, and Control Conference and Exhibit

Other

OtherAIAA Guidance, Navigation, and Control Conference and Exhibit 2000
CountryUnited States
CityDever, CO
Period8/14/008/17/00

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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    Ferrari, S., & Stengel, R. F. (2000). Classical/neural synthesis of nonlinear control systems. In AIAA Guidance, Navigation, and Control Conference and Exhibit (AIAA Guidance, Navigation, and Control Conference and Exhibit). American Institute of Aeronautics and Astronautics Inc.. https://doi.org/10.2514/6.2000-4552