The application provides a high-precision control method for an auxiliary
robot, and relates to the field of
medical robot motion control. The method is used for an auxiliary
robot system with strong nonlinearity, unobservable mode and complex disturbance. Trajectory sample data is collected first, and an original high-dimensional
linearization state space model is constructed through a dynamic state
feature extraction algorithm. Then, an unobservable subsystem is extracted and
stability constraints are applied to obtain an optimized high-dimensional
linearization model. A low-dimensional reduced linear
state space model is constructed by using a state energy truncation reduction method, and a nominal controller and a
state observer are designed based on the model. At the same time, an adaptive
estimation learner is constructed to estimate complex disturbance in real time, and a disturbance compensation
signal is combined to form an overall
robust control system. The application effectively improves the suppression and
processing capacity of the auxiliary
robot system on strong nonlinearity, unobservable mode and complex disturbance, and realizes high-precision trajectory tracking control.