The effects of anti-aliasing filters on system identification : a thesis presented in partial fulfilment of the requirements for the degree of Master of Technology in Production Technology Department at Massey University

Abstract

Research was conducted to determine the effect of anti-aliasing filters on the identification of dynamic systems. Systems were simulated in the continuous simulation package ESL. The system response to a PRBS (Pseudo Random Binary Sequence) was recorded. Simulated noise was added and passed through a number of simulated analog filters. The systems were identified using the MATLAB identification toolbox. Two standard filters (Butterworth and Chebychev) were used with cut-off frequencies between ffis (natural frequency of the system) and 20 times ffis. Results showed that carefully designed filters could improve the performance of the identification algorithm in the presence of non-white high frequency additive noise. However for noise free measurements the filters degraded the performance of identification algorithms. This performance could be observed in the identified models steady state error, overshoot and settling time when subject to a step input. In the experiments performed, the lowest order (and in one case second order) filters with cut-off frequency of ffin= 5ros, gave the best results. [From Summary]