2 results about "Fir system" patented technology

This invention belongs to the field of indoor positioning technology and discloses a robust indoor positioning method and system based on the weighted LP norm. The method first constructs a linearized measurement model based on TOA ranging values; then, it constructs an outer weight matrix by calculating the covariance of noise propagation error, and combines this with an inner weight matrix constructed from the current residuals to form a weighted LP norm objective function; an iterative optimization algorithm is used to solve this objective function until convergence yields the final coordinates of the user device. This invention adaptively suppresses the influence of impulse noise and outliers through a two-layer weighting mechanism, maintaining high positioning accuracy and stability even in non-Gaussian noise environments. This method requires no additional hardware, has high computational efficiency, and is easy to deploy in engineering. It is not only applicable to UWB systems but can also be extended to other ranging-based wireless positioning scenarios.

This invention discloses an intelligent model predictive control method for intensive mixing processes based on reinforcement learning. Addressing the degradation of control performance caused by model mismatch due to modeling errors, abrupt state changes, and component degradation in traditional model predictive control, this invention introduces reinforcement learning into the model predictive control framework. The method designs an intelligent model predictive control approach based on reinforcement learning to solve a standard quadratic programming problem with respect to optimization variables. Reinforcement learning selects compensation terms to compensate for model deviations caused by model mismatch, combining this with the controller output and applying it to the system. Finally, the reward is calculated and updated to optimize the control compensation selection for future time steps. As the control cycle progresses, new information is continuously integrated, model predictions are updated, and control inputs are optimized to adapt to potential changes in system behavior or external disturbances. This improves system stability and control accuracy, ensuring that the intensive mixing process can track a preset trajectory.