A filtering process is adapted for eliminating the need of prediscretizing
a continuous-time differential model into a discrete-time difference
model. It provides a universal robust solution to the most general
formulation, in the sense that the system dynamics are described by
nonlinear continuous-time differential equations, and the nonlinear
measurements are taken at intermittent discrete times randomly spaced. The
filtering process includes the procedures of validating the measurement
using fuzzy logic, and incorporating factorized forward filtering and
backward smoothing to guarantee numerical stability. It provides users a
reliable and convenient solution to extracting internal dynamic system
state estimates from noisy measurements, with wider applications, better
accuracy, better stability, easier design, and easier implementation.
