Sigmoid function based integral-derivative observer and application to autopilot design. (1st February 2017) - Shao, Xingling
Abstract: To handle problems of accurate signal reconstruction and controller implementation with integral and derivative components in the presence of noisy measurement, motivated by the design principle of sigmoid function based tracking differentiator and nonlinear continuous integral-derivative observer, a novel integral-derivative observer (SIDO) using sigmoid function is developed. The key merit of the proposed SIDO is that it can simultaneously provide continuous integral and differential estimates with almost no drift phenomena and chattering effect, as well as acceptable noise-tolerance performance from output measurement, and the stability is established based on exponential stability and singular perturbation theory. In addition, the effectiveness of SIDO in suppressing drift phenomena and high frequency noises is firstly revealed using describing function and confirmed through simulation comparisons. Finally, the theoretical results on SIDO are demonstrated with application to autopilot design: 1) the integral and tracking estimates are extracted from the sensed pitch angular rate contaminated by nonwhite noises in feedback loop, 2) the PID(proportional-integral-derivative) based attitude controller is realized by adopting the error estimates offered by SIDO instead of using the ideal integral and derivative operator to achieve satisfactory tracking performance under control constraint. Highlights: A novel integral-derivative observer (SIDO) based on sigmoid function is developed. The stability of SIDO is well established based on exponential stability and singular perturbation theory. The effectiveness of SIDO in suppressing drift phenomena and high frequency noises is firstly revealed. The theoretical results on SIDO are demonstrated through application to autopilot design.