Neural Network Controller with Projection Layer for Stable Control
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Solution Overview
Problem
Deep reinforcement learning methods lack safety guarantees and stability in safety-critical domains, leading to unexpected system failures when confronted with slight sensor variations or disturbances, while traditional robust control techniques are overly conservative and limit performance.
Innovation Solution
A controller that applies a neural network to generate control signals for computer-controlled machines, combined with a projection function to ensure stability, allowing for stable control policies that are trained using reinforcement learning while adhering to predefined stability criteria, such as a decreasing Lyapunov function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If deep reinforcement learning is used to control computer-controlled machines, then control performance is improved, but stability and safety are compromised
Solution Approach 1:
A projection layer is introduced as an intermediary component between the neural network controller and the controlled system. This projection layer maps the neural network's output to a stable control policy space, ensuring that the control actions maintain system stability while preserving the performance benefits of deep reinforcement learning.
2Reliability
If traditional robust control techniques are used to ensure stability, then reliability is improved, but control performance is limited
Solution Approach 1:
The control system is segmented into two distinct functional components: a neural network module that optimizes control performance and a projection layer module that ensures stability. This segmentation allows each component to specialize in its strength while working together to achieve both high performance and reliability.
3Adaptability or versatility
If neural network-based control policies are applied to handle complex dynamical systems, then adaptability is improved, but robustness to perturbations deteriorates
Solution Approach 1:
The projection layer serves as a protective intermediary that filters the neural network's adaptive control actions through stability constraints. This allows the system to maintain adaptability to complex dynamics while being protected from perturbations that could cause instability.
Data Source
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AI summary
Some embodiments are directed to a controller for generating a control signal for a computer-controlled machine. A neural network may be applied to a current sensor signal, the neural network being configured to map the sensor signal to a raw control signal. A projection function may be applied to the raw control signal to obtain a stable control signal to control the computer-controllable machine.