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.
N. Unnikrishnan and S. N. Balakrishnan, "Neuroadaptive Model Following Controller Design for a Nonaffine UAV Model," Proceedings of the 2006 American Control Conference, Institute of Electrical and Electronics Engineers (IEEE), Jan 2006.
The definitive version is available at https://doi.org/10.1109/ACC.2006.1657168
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
Article - Conference proceedings
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