Absolute Gravimeter PID Tuning Across Free-Fall and Catch Phases

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Solution Overview

Problem

Conventional methods for adjusting motor control parameters in absolute gravimeters are empirically set, leading to inadequate smoothness and precision in the motion of the main drag-free cart and the falling object, resulting in reduced accuracy of gravitational acceleration measurements.

Innovation Solution

A method and apparatus utilizing a reinforcement learning framework to dynamically adjust proportional-integral-derivative (PID) controller parameters through agents and reward functions, accumulating experience data to optimize motor control strategies for each phase of the gravimeter's operation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of manufacture

If PID controller parameters are set empirically, then the control system is simple to implement, but the motion smoothness and measurement precision deteriorate

Engineering Contradiction:

Improveease of implementationVSAvoidmeasurement precision

Core Design Contradiction:

Ease of manufactureVSMeasurement precision

Solution Approach 1:

The patent changes the PID controller parameters dynamically based on the control phase and state data. Instead of fixed empirical parameters, the system adjusts proportional, integral, and derivative parameters adaptively during separation, free-falling, and catching phases to optimize both implementation simplicity and measurement precision.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent introduces dynamic parameter adjustment where the PID controller parameters are not static but change according to the current control phase and system state. This dynamic approach allows the system to maintain optimal performance across different operational phases while keeping the overall control structure relatively simple.

Inventive Principle:

Principle #15Dynamics

2Ease of manufacture

If PID controller parameters are set empirically, then the control system is simple to implement, but the motion smoothness deteriorates

Engineering Contradiction:

Improveease of implementationVSAvoidmotion smoothness

Core Design Contradiction:

Ease of manufactureVSStability of the object's composition

Solution Approach 1:

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent implements feedback mechanisms where the controller receives state data from sensors and adjusts parameters accordingly. The feedback loop ensures that the system responds to actual motion conditions, maintaining smooth operation while keeping the control structure implementable.

Inventive Principle:

Principle #23Feedback

3Measurement precision

If reinforcement learning is used to adjust parameters, then measurement precision and motion smoothness are improved, but the system complexity increases

Engineering Contradiction:

Improvemeasurement precisionVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent segments the control process into distinct phases (separation, free-falling, catching) with dedicated agents for each phase. This segmentation allows the reinforcement learning system to be broken down into manageable components, reducing overall system complexity while maintaining high measurement precision through phase-specific optimization.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent implements self-service through autonomous agents that automatically adjust parameters without extensive human intervention. The reinforcement learning agents learn optimal control strategies independently, reducing the need for complex manual configuration and system management while achieving high measurement precision.

Inventive Principle:

Principle #25Self-service

4Measurement precision

If reinforcement learning is used to adjust parameters, then motion smoothness and measurement precision are improved, but the computational requirements and training time increase

Engineering Contradiction:

Improvemeasurement precisionVSAvoidtraining time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent performs preliminary training actions by pre-training agents for each control phase separately before deployment. This preliminary action allows the system to accumulate experience data and learn optimal strategies in advance, reducing real-time computational requirements and training time during actual operation.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20260023190A1Method for adjusting motor control parameters of absolute gravimeter

Publication Date: 2026.01.22 NATIONAL INSTITUTE OF METROLOGY CHINA

AI summary

A method for adjusting motor control parameters of an absolute gravimeter is provided, in which a current control phase is determined based on position information, motion information and motion duration of a main drag-free cart and a falling object; a state data is processed using agents to obtain a corresponding action data for adjusting motor control parameters; a reward value is calculated using a reward function; experience data is stored in a replay buffer; the above processes are repeated until a catching phase is reached and the main drag-free cart and the falling object have equal velocities and zero distance; if all agents have completed training, a series of action data is generated using the agents; and if there is an agent has not completed training, corresponding experience data is extracted to continue training, and the absolute gravimeter is reset for iterative execution.