AI Circuit Control for Adaptive Power Regulation
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Solution Overview
Problem
Existing electronic circuits with controllable components face challenges in maintaining precise control across various use cases, environmental conditions, and different power supplies, struggling with load transitions and parasitic inductances and capacitances.
Innovation Solution
Incorporating a system with a controller and an artificial intelligence component that cooperate to provide control signals, optionally as part of the same integrated circuit, which includes a neural network trained during startup and in-situ, using supplementary control elements and a digital twin for enhanced control and prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional controllers are used for electronic circuits, then the control is simple and device complexity is low, but the control precision deteriorates when operating in diverse environmental conditions or with different power supplies
Solution Approach 1:
An artificial intelligence component is introduced as an intermediary between the controller and the controllable components. This AI component processes control signals and adapts them based on environmental conditions and circuit parameters, thereby maintaining high control precision without requiring the traditional controller to become more complex. The AI component acts as a mediator that handles the complexity of adaptation while the traditional controller remains relatively simple.
Solution Approach 2:
The artificial intelligence component is configured to autonomously adapt control parameters based on environmental conditions and power supply variations. It performs self-learning and self-adjustment without requiring external intervention or complex reconfiguration of the traditional controller. The AI component serves itself by continuously monitoring and adjusting control signals to maintain optimal performance across diverse operating conditions.
2Adaptability or versatility
If the circuit is designed for a specific use case, then the control is optimized for that scenario, but the adaptability to different use cases and environmental conditions deteriorates
Solution Approach 1:
The control system transitions from a static, fixed configuration to a dynamic, adaptive configuration. The artificial intelligence component continuously adjusts control parameters in real-time based on environmental conditions and operational state, enabling the circuit to maintain optimal performance across diverse use cases and environmental conditions while preserving control precision.
Solution Approach 2:
The artificial intelligence component dynamically changes control parameters such as duty cycle, switching frequency, and voltage levels based on detected environmental conditions and power supply characteristics. This parameter adaptation enables the circuit to maintain precise control across different use cases without sacrificing control accuracy.
3Reliability
If the controller operates without artificial intelligence, then the device complexity is low, but the ability to handle load transitions and parasitic effects deteriorates
Solution Approach 1:
The artificial intelligence component implements advanced feedback mechanisms that continuously monitor circuit behavior, load transitions, and parasitic effects. It processes this feedback information and adjusts control signals in real-time to compensate for parasitic inductances and capacitances, thereby improving control stability and reliability without requiring the traditional controller to become more complex.
Data Source
AI summary
Circuit operation is improved through application of artificial intelligence to optimize circuit control. This can provide dynamic and intelligent supply regulation for power supplies which has particular advantages for the Internet of Things and other similar areas which require circuits to be used in different environments or with widely varying energy sources.