This paper proposes a new model-following adaptive control design technique for nonlinear systems that are nonaffine in control. The adaptive controller uses online neural networks that guarantee tracking in the presence of unmodeled dynamics and/or parameter uncertainties present in the system model through an online control adaptation procedure. The controller design is carried out in two steps: (i) synthesis of a set of neural networks which capture the unmodeled (neglected) dynamics or model uncertainties due to parametric variations and (ii) synthesis of a controller that drives the state of the actual plant to that of a reference model. This method is tested using a three degree of freedom model of a UAV. Numerical results which demonstrate these features and clearly bring out the potential of the proposed approach are presented in this paper.

Meeting Name

2006 American Control Conference


Mechanical and Aerospace Engineering

Keywords and Phrases

Adaptive Control; Adaptive Control Design; Aerospace Control; Control System Synthesis; Degree of Freedom Model; Neuroadaptive Model; Neurocontrollers; Nonaffine UAV Model; Nonlinear Control Systems; Nonlinear System; Online Neural Network; Parameter Uncertainty; Remotely Operated Vehicles; Unmanned Aerial Vehicle; Unmodeled Dynamics

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type





© 2006 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Jan 2006