AI Power Converter Regulation for Self-Tuning Error Prediction
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Solution Overview
Problem
The complexity of power conversion circuits, including switched-mode DC-DC converters, requires careful tuning and is sensitive to design changes and aging components, making it challenging to achieve optimal performance across varying operating conditions, which is typically only feasible for expert engineers and results in high costs due to the need for high-quality components.
Innovation Solution
Incorporating a machine-learning-based power conversion regulator circuit with an artificial neural network that computes predictions and generates error signals to adjust output parameters, combining these predictions with conventional control methods to ensure stability and performance across a range of conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional control schemes with careful design and parameter tuning are used, then performance can be optimized for specific applications, but the tuning process becomes time-consuming and requires highly skilled engineers
Solution Approach 1:
The system uses machine learning models to automatically tune regulator parameters without requiring expert engineers. The ML model learns optimal control strategies from training data and autonomously adjusts parameters, making the system self-tuning and eliminating the need for manual expert intervention.
Solution Approach 2:
The invention changes the approach from manual parameter tuning to automated parameter optimization using machine learning. The system transforms the complex tuning process into a data-driven parameter selection process where the ML model predicts optimal parameters based on operating conditions.
2Reliability
If high-quality components with small aging effects are chosen, then longtime-stability and high performance are ensured, but circuit costs are dramatically impacted
Solution Approach 1:
The system incorporates feedback mechanisms where machine learning models continuously monitor operating conditions and component behavior over time. This feedback enables the system to adapt to aging effects and maintain stability without requiring premium components, as the ML controller compensates for degradation dynamically.
Solution Approach 2:
The invention replaces the mechanical approach of ensuring reliability through expensive high-quality components with an intelligent software-based ML control system. The ML model substitutes for the need for premium hardware by providing adaptive compensation for component aging and variations.
3Reliability
If flexible non-linear control algorithms are used to improve system behavior, then performance can be enhanced, but careful tuning for specific applications is required and unwanted side effects may occur under different operating conditions
Solution Approach 1:
The machine learning model is trained on diverse operating conditions and scenarios, enabling it to provide robust control across a wide range of applications and conditions. The ML system learns universal control strategies that adapt to different operating scenarios without requiring application-specific tuning, making the control algorithm universally applicable.
Solution Approach 2:
The invention transitions from static control algorithms to dynamic machine learning-based control that adapts in real-time to changing operating conditions. The ML model continuously adjusts control parameters based on current system state and learned patterns, providing dynamic adaptability that handles varying conditions without unwanted side effects.
Data Source
AI summary
A power conversion regulator circuit comprises a regulator input dynamically supplied with a feedback signal representative of an output parameter of a power converter circuit, a regulator output configured to provide a control signal to the power converter circuit, for making adjustments to the output of the power converter circuit, and regulator circuitry configured to generate the control signal for outputting via the regulator output, based on the error signal. The circuit further comprises a processing circuit configured to (a) implement an artificial neural network comprising a plurality of artificial neurons, where the artificial neural network is configured to compute a machine-learning-based (ML-based) prediction of the output parameter, based on the feedback signal, and (b) generate the error signal based at least in part on the ML-based prediction of the output parameter and based on a target level for the output parameter.