Gaussian Process Training with Safe Dynamic Exploration
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Solution Overview
Problem
Existing methods for active learning in time series models of physical systems lack a comprehensive approach to combine dynamic exploration, active exploration, and safety considerations, potentially leading to system damage during dynamic stimulation.
Innovation Solution
The proposed procedure integrates dynamic exploration, active exploration, and safe exploration by using a Gaussian process with a non-linear exogenous structure to generate input and output curves, while incorporating a safety criterion to ensure the system is not damaged during exploration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dynamic exploration is performed to maximize information gain, then modeling accuracy is improved, but system safety deteriorates due to potential damage during dynamic stimulation
Solution Approach 1:
A safety criterion acts as an intermediary between the dynamic exploration process and the physical system. This criterion evaluates potential excitation inputs before they are applied, filtering out those that would cause system damage while allowing informative excitations to proceed. The safety criterion mediates the conflict by translating safety requirements into constraints on the exploration process.
Solution Approach 2:
The safety criterion is established and evaluated before dynamic exploration is performed. By预先 defining safe operating boundaries and evaluating potential excitations against these boundaries in advance, the system can plan exploration trajectories that are both informative and safe, avoiding damage-prone regions before they are encountered.
2Productivity
If active exploration is performed to collect information quickly, then information gathering efficiency is improved, but safety control deteriorates due to rapid system excitation
Solution Approach 1:
The exploration process is made dynamic and adaptive through the safety criterion. Rather than using fixed, pre-planned excitation sequences, the system dynamically adjusts exploration trajectories based on real-time safety evaluations. The safety criterion enables the exploration to be both rapid (maintaining productivity) and safe (maintaining reliability) by allowing quick adjustments to the exploration pace and direction.
Solution Approach 2:
The safety criterion provides continuous feedback during the active exploration process. As the system rapidly collects information, the safety criterion monitors the current state and provides feedback that guides subsequent excitation choices, ensuring that rapid information gathering does not compromise safety control.
3Loss of information
If extensive dynamic stimulation is applied to capture relevant dynamic processes, then model completeness is improved, but resource consumption deteriorates
Solution Approach 1:
The safety criterion enables parameter changes in the exploration strategy. By dynamically adjusting exploration parameters (such as excitation amplitude, frequency, and duration) based on safety evaluations, the system can capture comprehensive dynamic behavior (improving model completeness) while avoiding excessive or redundant excitations (reducing resource consumption).
Data Source
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AI summary
A computer-implemented method for the safe, active training of a computer-based model for modeling time series of a physical system with Gaussian processes, comprising the steps of setting a safety threshold a; initializing by executing safe initial curves as input values on the system, creating an initial regression model and an initial safety model; repeatedly performing the steps of updating the regression model; updating the safety model; determining a new curve segment; executing the determined new curve segment on the physical system and measuring output values; incorporating the new output values into the regression and safety models; until N iterations have been performed; and outputting the regression and safety models.