2results about How to "Realize automatic tuning" patented technology

This invention relates to a method and apparatus for tuning control parameters of magnetic levitation bearings, a storage medium, and an electronic device, belonging to the technical field of magnetic levitation control parameters. The tuning method includes: S1, constructing a mathematical model of a PID parameter optimization problem; S2, generating a Bayesian optimization sample set, fitting a Gaussian process surrogate model, and globally exploring to generate a PID parameter subspace; S3, executing a dung beetle optimization algorithm within the subspace to obtain the optimal point (x) within the subspace. db y db S4, add the optimal point to the Bayesian optimization sample set and update the Gaussian process model; S5, repeat steps S2 to S4 until the condition is met, and output the optimal PID parameter x. db * By combining the global exploration capability of Bayesian algorithms with the local development capability of the dung beetle algorithm, the Bayesian optimization reduces the dependence on the initial sample size, while the dung beetle algorithm accelerates convergence within the subspace, thus balancing efficiency and accuracy.

A trajectory generation method based on deep reinforcement learning for training game coefficients. The advantages of this invention are as follows: By organically combining deep reinforcement learning and game theory trajectory planning, this invention achieves automated and intelligent optimization of game coefficients. It automatically learns the sensitivity coefficient α in the game objective function using algorithms such as DQN, completely replacing traditional manual parameter tuning methods and significantly reducing development costs and time. A reward function centered on the vehicle's forward distance guides the vehicle to pass quickly, while introducing penalty terms such as collision and acceleration to enhance safety and comfort. The generated trajectory outperforms manually tuned results in terms of traffic efficiency, safety, and smoothness. Simultaneously, it preserves the interpretability of the game theory model; the optimized α value has a clear physical meaning and can dynamically adjust the driving style according to the real-time environment, improving the system's scene adaptability and generalization robustness.