Command governor-based model reference control

Gerardo De La Torre, Tansel Yucelen, Eric Johnson

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


In this paper we develop a new model reference control architecture for uncertain dynamical systems that can effectively suppress the system uncertainties and achieve a guaranteed system performance. The proposed approach neither resorts to nonlinear adaptive control laws nor relies on excessive modeling information as often done in traditional robust control frameworks. It only requires a parameterization of the system uncertainty given by unknown weights with known conservative bounds in order to stabilize the uncertain dynamical system. The proposed methodology is based on a recently developed command governor theory that minimizes the effect of system uncertainty and shapes the system's input through feedback in order to improve overall system performance. Specifically, we show the controlled uncertain dynamical system approximates a given ideal reference system by properly choosing the design parameter of the command governor. Unlike model reference adaptive control approaches, the proposed model reference controller preserves linearity of the controlled uncertain dynamical system since its control laws are linear, and hence, the closed-loop performance is predictable for different command spectrums. A numerical example is provided to illustrate the effectiveness of the proposed architecture.

Original languageEnglish (US)
Title of host publication2013 American Control Conference, ACC 2013
Number of pages6
Publication statusPublished - Sep 11 2013
Event2013 1st American Control Conference, ACC 2013 - Washington, DC, United States
Duration: Jun 17 2013Jun 19 2013


Other2013 1st American Control Conference, ACC 2013
CountryUnited States
CityWashington, DC


All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cite this

De La Torre, G., Yucelen, T., & Johnson, E. (2013). Command governor-based model reference control. In 2013 American Control Conference, ACC 2013 (pp. 4319-4324). [6580504]