Neural Network Controller With Projection Mapping for Stability Guarantees
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Solution Overview
Problem
Deep reinforcement learning methods lack safety guarantees, making them unsuitable for safety-critical domains where incorrect actions can significantly impact the system being controlled.
Innovation Solution
A controller that applies a neural network to sensor signals and uses a projection function to map raw control signals into a stable control signal space, ensuring stability and safety by limiting control policies to predefined stable ones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If deep reinforcement learning is used to control the machine, then control performance is improved, but stability and safety guarantees are lost
Solution Approach 1:
The patent introduces a projection layer as an intermediary component between the neural network controller and the actual system. This projection layer maps the neural network's output to a set of pre-computed stabilizing control actions, ensuring that the control performance benefits of deep reinforcement learning are maintained while guaranteeing system stability through the intermediary projection mechanism.
Solution Approach 2:
The patent pre-computes a set of stabilizing control actions and their corresponding projection mappings before deployment. By preparing these stabilizing policies in advance and storing them in a lookup table, the system ensures that stability guarantees are established beforehand, allowing the neural network to learn high-performance control strategies without compromising safety during operation.
2Reliability
If robust control techniques are used to ensure stability, then stability guarantees are improved, but control performance is limited
Solution Approach 1:
The patent segments the control task into two distinct components: a neural network responsible for learning high-performance control strategies, and a projection layer responsible for ensuring stability. This segmentation allows each component to specialize - the neural network optimizes for performance while the projection layer guarantees stability, resolving the trade-off between performance and reliability.
Solution Approach 2:
The patent changes the parameter space by pre-computing and storing a set of stabilizing control actions with their corresponding state projections. By transforming the control problem into a parameter lookup and projection task rather than direct control, the system maintains stability guarantees while allowing the neural network to explore high-performance control strategies in the original parameter space.
3Adaptability or versatility
If the neural network learns complex control policies, then adaptability is improved, but safety guarantees are lost
Solution Approach 1:
The projection layer serves as a safety intermediary that filters the neural network's adaptive control policies. Even when the neural network learns complex and adaptive control strategies, the projection layer ensures that the final control actions remain within the pre-computed stabilizing policy set, thereby maintaining safety guarantees while preserving adaptability.
Solution Approach 2:
The patent applies partial action by using the neural network to generate control policies only within the region where stabilizing policies have been pre-computed. The projection layer ensures that even if the neural network proposes excessive or aggressive control actions, they are projected back to safe, stabilizing actions, thus maintaining safety while allowing adaptability within bounds.
Data Source
AI summary
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.