The present invention discloses a maximum likelihood gradient iterative parameter
estimation algorithm based on a generalized time-varying
system identification model. Due to the influence of factors such as disturbances, the
model parameters of a
motor speed control system are difficult to accurately estimate.
DC motor control systems are characterized by randomness and the influence of disturbances. Furthermore, measuring the internal parameters of the motor during operation is extremely difficult. Furthermore, in actual operation, due to factors such as measurement
noise, the
system is contaminated by
colored noise, which significantly affects the
algorithm's identification accuracy. To improve the accuracy of motor parameter
estimation, the present invention, based on a gradient
search algorithm, transforms the
system parameter identification problem into a probability density function extremum problem based on the maximum likelihood principle, and utilizes the maximum likelihood principle to directly process system
noise to improve the
algorithm's identification accuracy. The present invention includes the steps of constructing an identification model of a generalized time-varying system affected by
colored noise, constructing a maximum likelihood gradient iterative parameter
estimation algorithm flow, and constructing a maximum likelihood gradient iterative parameter estimation algorithm. The
present method is simple, reliable, and has high identification accuracy, and can be applied to parameter estimation in
DC motor control systems.