Neural Network Circuitry for Autonomous Motor Control
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Solution Overview
Problem
Existing motor control systems rely on complex computing devices and human input to generate training vectors for neural networks, leading to increased complexity and inefficiency in motor regulation, particularly in adapting to mechanical and electrical changes over time.
Innovation Solution
The system configures neural network circuitry to generate training vectors in real-time and adapt based on errors between actual and ideal setups, allowing the motor controller to learn and optimize motor control autonomously, reducing reliance on human interaction and high-performance hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex computing devices and human input are used to generate training vectors, then training accuracy may be improved, but device complexity and procedural complexity increase significantly
Solution Approach 1:
The motor controller autonomously generates training vectors by utilizing its own operational data, eliminating the need for external complex computing devices and human intervention. The system performs self-training by collecting actual motor operation data and using it to train the neural network, thereby reducing system complexity while maintaining training effectiveness
Solution Approach 2:
The system implements a feedback mechanism where the motor controller collects actual operation data (speed, current, temperature) and uses this feedback to generate training vectors. This closed-loop approach allows the system to continuously improve its control accuracy by learning from its own performance without requiring external complex computing resources
2Device complexity
If static neural network training is used, then device complexity is reduced, but adaptability to mechanical and electrical changes deteriorates
Solution Approach 1:
The neural network transitions from a static pre-trained model to a dynamic system that continuously adapts during motor operation. The system dynamically generates training vectors based on real-time operational conditions and retrains the neural network, enabling it to adapt to changing mechanical loads, electrical parameters, and environmental conditions while maintaining manageable complexity through incremental learning
3Measurement precision
If external complex computing devices are used for training, then training quality may be improved, but loss of time and productivity decrease due to external dependencies
Solution Approach 1:
The training function is merged with the motor controller itself, combining the control and learning functions into a single integrated system. This eliminates the need for separate external computing devices and allows training to occur concurrently with motor operation, thereby improving training quality while reducing time loss through parallel execution of training and operation
Data Source
AI summary
An apparatus for driving a motor includes a plurality of neurons of neural network circuitry and motor circuitry. The plurality of neurons are configured to generate a cycle value based on a target speed, based on a speed value associated with the motor at a particular time, and based on a current value associated with the motor at the particular time. The plurality of neurons is configured to be trained to generate the cycle value to minimize an error between the cycle value and a training cycle value for each training vector of a plurality of training vectors. The apparatus is configured to have generated the plurality of training vectors. The motor circuitry is configured to control, based on the cycle value, a set of switching elements to drive the motor.