AI Predictive Control for Tendon-Driven Catheter Accuracy
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Solution Overview
Problem
Existing control methods for tendon-driven continuum mechanisms (TDCMs) in medical devices, such as catheters, struggle with non-linear behavior caused by elasticity, slack, backlash, hysteresis, and friction, leading to inaccurate control and difficulty in adapting to dynamic environmental changes within a patient.
Innovation Solution
The implementation of a machine-learned model, such as a recurrent neural network, that predicts future motor controls based on past states and user commands, allowing for adaptive control and accounting for unknown environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional control methods are used for TDCM, then device complexity is reduced, but control accuracy deteriorates due to non-linear behavior and environmental changes
Solution Approach 1:
The patent replaces traditional mechanical control systems with an artificial intelligence-based predictive control system. The AI model learns and compensates for non-linear behaviors (elasticity, slack, backlash, hysteresis, friction) and environmental changes, achieving high control accuracy without requiring complex mechanical compensation mechanisms or precise physical modeling of the TDCM system.
Solution Approach 2:
The control system uses the TDCM's own operational data (past states, motor commands, actual movements) to train and refine the AI model. The system learns from its own performance and continuously improves control accuracy through adaptive learning, eliminating the need for external calibration or manual adjustment of control parameters.
2Adaptability or versatility
If pre-defined control is used, then ease of operation is improved, but adaptability to dynamic environmental changes deteriorates
Solution Approach 1:
The control system transitions from static pre-defined control to dynamic adaptive control. The AI model continuously updates its predictions based on real-time feedback from the TDCM's actual performance and environmental conditions, allowing the system to adapt to changing patient anatomy, friction conditions, and catheter configurations while maintaining ease of operation through automated adjustment.
Solution Approach 2:
The system implements closed-loop feedback by monitoring the actual position and state of the TDCM, comparing it with predicted positions, and using the discrepancies to refine future control predictions. This feedback mechanism enables automatic adaptation to environmental changes without requiring manual intervention or re-calibration by the operator.
3Measurement precision
If analytical models with many hyperparameters are used, then measurement precision is improved, but device complexity and difficulty of calibration increase
Solution Approach 1:
The patent replaces complex analytical models with numerous hyperparameters with a data-driven AI model. Instead of requiring detailed physical modeling of each non-linear effect and manual tuning of multiple parameters, the system uses neural networks or other machine learning algorithms that automatically learn the system's behavior from operational data, achieving high accuracy with simpler model structure and automated parameter optimization.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables more precise control of TDCMs by predicting and compensating for environmental factors, reducing the need for precise calibration and improving the accuracy of catheter movement in complex patient environments.
Implementation Method 1
The TDCM has non-linear behavior caused by elasticity, slack, backlash hysteresis, and non-linear friction between the sheath and the wire
Implementation Method 2
The TDCM has non-linear behavior caused by elasticity, slack, backlash hysteresis, and non-linear friction between the sheath and the wire
Implementation Method 3
The TDCM has non-linear behavior caused by elasticity, slack, backlash hysteresis, and non-linear friction between the sheath and the wire
Data Source
AI summary
For predictive control of tendon-driven continuum mechanisms (TDCMs), a machine-learned model predicts future control of the motor or robot based on user commands to move the catheter. For example, a robotically operated catheter includes a TDCM. The machine-learned model, such as a recurrent neural network or another artificial intelligence, predicts future control. This prediction may account for the unknown environment using input of past states of the motor and/or position of the tip of the catheter or other steered device.