HILS Feedback Control Using Inverse Transfer Function Correction
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Solution Overview
Problem
Existing Hardware In the Loop Simulation (HILS) systems experience response lags due to actuator feedback, leading to inaccurate real-time simulations between the model and real machine interactions, as they rely on offline correction methods that cannot handle real-time input signal changes.
Innovation Solution
A test system incorporating a test piece, actuator, virtual model, controller, and inverse transfer function correcting unit to correct signals in real-time, using a proper inverse transfer function based on system transfer functions to eliminate response lags.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If offline correction methods are used to correct errors between model behavior and real machine, then calculation complexity is reduced, but real-time simulation accuracy deteriorates due to response lags
Solution Approach 1:
The system pre-calculates and stores transfer functions and inverse transfer functions offline before real-time simulation. These pre-computed functions are then used during real-time operation to correct signals without performing complex calculations on-the-fly, thus maintaining both low real-time computational burden and high simulation accuracy.
Solution Approach 2:
The system dynamically adapts the correction approach by using different transfer functions for different operating conditions. The transfer functions are updated based on current system state, allowing the correction mechanism to remain accurate across varying real-time conditions while maintaining computational efficiency through selective application.
2Stability of the object's composition
If constant input waveforms are used in offline correction, then system stability is improved, but adaptability to real-time changing input signals deteriorates
Solution Approach 1:
The system performs preliminary identification of system characteristics using constant or slowly varying test signals to establish baseline transfer functions. These baseline functions capture the essential system dynamics and provide a stable foundation for correction, while still being applicable to more complex real-time signals through their mathematical generality.
Solution Approach 2:
The system adjusts the parameters of the transfer function model based on operating conditions. By changing parameters such as gain, time constants, or polynomial coefficients according to the current signal characteristics, the system maintains stability from the baseline model while adapting to real-time variations in input signals.
3Device complexity
If feedback signals from load device are used directly, then system simplicity is maintained, but simulation accuracy deteriorates due to response lags from actuator frequency characteristics
Solution Approach 1:
The system introduces transfer function correction as an intermediary processing step between the raw feedback signals from the load device and the model input. This intermediary layer compensates for the actuator's frequency response characteristics and time delays, improving feedback accuracy without fundamentally changing the overall system architecture or requiring complex real-time control modifications.
Data Source
AI summary
A test system is provided which includes an actuator that actuates a test piece, a virtual model that operates in association with the test piece, a controller that controls the actuator, a computing device that computes the virtual model, and an inverse transfer function correcting unit that corrects a signal from the virtual model with a proper inverse transfer function that is determined based on a transfer function of a system including communication units between the test piece and the actuator and between the actuator and the controller and a communication unit between the computing device and the controller. The controller controls the actuator based on the signal corrected by the inverse transfer function correcting unit.