Advanced Energy Industries had 17 patents in future of work during Q1 2024. Advanced Energy Industries Inc has filed patents for adaptive engines and methods of adaptive control, including fuzzy control systems. These patents focus on receiving input regressors, estimating model parameter tensors for nonlinear models, generating control signals, and selecting preferred control signals. The adaptive engine described uses a bifurcated nonlinear model with a control portion and an estimation portion, allowing for real-time adaptation and improved performance in controlling systems. GlobalData’s report on Advanced Energy Industries gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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Advanced Energy Industries grant share with future of work as a theme is 17% in Q1 2024. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Adaptive engine for tracking and regulation control (Patent ID: US20240019819A1)
The patent filed by Advanced Energy Industries Inc. discloses an adaptive engine and method of adaptive control. The method involves receiving an input regressor containing a reference signal, system output measurement, and control output. Estimation laws are applied to the input regressor to estimate model parameter tensors for actuators or power systems. Possible control signals are received, estimated system outputs are generated, and a control signal is selected based on minimizing estimation or prediction error. The adaptive engine includes modules for adaptive inverse control, eigen control, and penalty control, which are used in parallel to optimize control signals based on estimated model parameters.
The adaptive engine further comprises a control law selector and combiner to choose the best control signal or a combination of signals based on minimizing error. The method of adaptive control involves applying estimation laws to estimate model parameters, receiving possible control signals, generating estimated system outputs, and selecting the best control signal or combination. The system is designed to achieve rapid convergence in stable and unstable zero-dynamics situations, with modules for adaptive inverse control, eigen control, and penalty control working together to optimize control signals. The method can be executed by processors using a non-transient computer-readable storage medium, with estimation portions of the nonlinear model being functions of the estimated model parameter tensors and a structure of the time-varying linear system.
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