Active disturbance cancellation in nonlinear dynamical systems using neural networks

A proposal for the 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

Adaptive filtering algorithms for noise cancellation

Tese de mestrado. Mestrado Integrado em Engenharia Electrotécnica e de Computadores - Major Automação. Faculdade de Engenharia. Universidade do Porto. 201

Doctor of Philosophy

dissertationThe dissertation is concerned with the development and analysis of adaptive algorithms for the rejection of unknown periodic disturbances acting on an unknown system. The rejection of periodic disturbances is a problem frequently encountered in control engineering, and in active noise and vibration control in particular. A new adaptive algorithm is presented for situations where the plant is unknown and may be time-varying. Known as the adaptive harmonic steady-state or ADHSS algorithm, the approach consists in obtaining on-line estimates of the plant frequency response and of the disturbance parameters. The estimates are used to continuously update control parameters and cancel or minimize the effect of the disturbance. The dynamic behavior of the algorithm is analyzed using averaging theory. Averaging theory allows the nonlinear time-varying closed-loop system to be approximated by a nonlinear time-invariant system. Extensions of the algorithm to systems with multiple inputs/outputs and disturbances consisting of multiple frequency components are provided. After considering the rejection of sinusoidal disturbances of known frequency, the rejection of disturbances of unknown frequency acting on an unknown and time-varying plant is considered. This involves the addition of frequency estimation to the ADHSS algorithm. It is shown that when magnitude phase-locked loop (MPLL) frequency estimation is integrated with the ADHSS algorithm, the two components work together in such a way that the control input does not prevent frequency tracking by the frequency estimator and so that the order of the ADHSS can be reduced. While MPLL frequency estimation can be combined favorably with ADHSS disturbance rejection, stability is limited due to the local convergence properties of the MPLL. Thus, a new frequency estimation algorithm with semiglobal stability properties is introduced. Based on the theory of asynchronous electric machines, the induction motor frequency estimator, or IMFE, is shown to be appropriate for disturbance cancellation and, with modification, is shown to increase stability of the combined ADHSS/MPLL algorithm. Extensive active noise control experiments demonstrate the performance of the algorithms presented in the dissertation when disturbance and plant parameters are changing

Efficient time delay estimation and compensation applied to the cancellation of acoustic echo

The system identification problem is notably dealt with using adaptive filtering approaches. In many applications the unknown system response consists of an initial sequence of zero-valued coefficients that precedes the active part of the response. The presence of these coefficients introduces a flat delay in the incoming signals which can take significantly large values. When most adaptive approaches attempt to model such a system, the presence of flat delay impairs their operation and performance. The approach introduced in this thesis aims to model the flat delay and active part of the unknown system separately. An efficient system for time delay estimation (TDE) is introduced to estimate the flat delay of an unknown system. The estimated delay is then compensated within the adaptive system thus allowing the latter to cover the active part ofthe unknown system. The proposed system is applied to the Acoustic Echo Cancellation (ABC) problem