Active Disturbance Cancellation
In Nonlinear Dynamical Systems Using Neural Networks
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This book begins with a brief background on the field of active disturbance cancellation with a particular focus on the challenges of acoustic cancellation environments. Traditional linear feedforward and feedback approaches are discussed and a novel method for use of a time delay CMAC neural network for disturbance cancellation in nonlinear dynamical systems is presented. Appropriate modifications to the CMAC training algorithm are derived which allow convergent adaptation for a variety of secondary signal paths. Analytical bounds on the maximum learning gain are presented which guarantee con...
This book begins with a brief background on the
field of active disturbance cancellation with a
particular focus on the challenges of acoustic
cancellation environments. Traditional linear
feedforward and feedback approaches are discussed
and
a novel method for use of a time delay CMAC neural
network for disturbance cancellation in nonlinear
dynamical systems is presented. Appropriate
modifications to the CMAC training algorithm are
derived which allow convergent adaptation for a
variety of secondary signal paths. Analytical bounds
on the maximum learning gain are presented which
guarantee convergence of the algorithm and provide
insight into the necessary reduction in learning
gain as a function of the system parameters.
Effectiveness of the algorithm is evaluated through
mathematical analysis, simulation studies and
experimental application of the technique on an
acoustic duct laboratory model.
field of active disturbance cancellation with a
particular focus on the challenges of acoustic
cancellation environments. Traditional linear
feedforward and feedback approaches are discussed
and
a novel method for use of a time delay CMAC neural
network for disturbance cancellation in nonlinear
dynamical systems is presented. Appropriate
modifications to the CMAC training algorithm are
derived which allow convergent adaptation for a
variety of secondary signal paths. Analytical bounds
on the maximum learning gain are presented which
guarantee convergence of the algorithm and provide
insight into the necessary reduction in learning
gain as a function of the system parameters.
Effectiveness of the algorithm is evaluated through
mathematical analysis, simulation studies and
experimental application of the technique on an
acoustic duct laboratory model.
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