2results about How to "Eliminate mutual interference" patented technology

This invention discloses a joint module parameter identification method based on modulation function DREM and active excitation, belonging to the field of robot joint control technology. The method includes: constructing a linear regression model containing the electrical parameters to be identified; using multiple modulation functions with zero boundary conditions and linear independence to perform weighted integral transformations on the linear regression model, obtaining multiple integral algebraic equations without differential terms, and combining them to form an extended regression matrix; constructing a decoupled determinant based on the adjoint matrix of the extended regression matrix, and decoupling the extended regression matrix into multiple independent scalar equations; injecting a disturbance signal into the control loop of the permanent magnet synchronous motor when the amplitude of the decoupled determinant is lower than a preset excitation threshold; updating the corresponding electrical parameters to be identified based on each scalar equation and outputting the identification result. This method solves the problems of low parameter identification accuracy and slow convergence of joint modules under high noise and low excitation conditions, significantly improving the control performance of robot joints.

The application provides a multi-modal large language model instruction federal fine-tuning system, method and product, and relates to the technical field of electronic information. The system comprises: a server issuing a plurality of task parameters of a current round to a client; the client modulates image tokens using the semantics of local samples to obtain multi-modal representation, and then processes the multi-modal representation using local individualized parameters of the current round to obtain multi-modal individualized representation; and the server uploads the parameters of the current round obtained by fine-tuning the local model based on the representation to obtain individualized parameters of the current round which are left in the local; the server aggregates the fine-tuned parameters of the current round of the same task according to the fine-tuned parameters of the current round uploaded by each client to obtain and issue the next round parameters of the task; after fine-tuning is completed, the client obtains a final local model based on the local final individualized parameters and the final parameters corresponding to the task issued by the server, and uses the final local model for local task execution. The purpose is to improve the generalization performance of collaborative fine-tuning.