Soft Robot Control via Fusion Prediction and Koopman Modeling
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Solution Overview
Problem
Existing soft robot modeling and control methods face challenges due to complex hysteresis nonlinearity in flexible materials, difficulties in fluid dynamics modeling, and the lack of a universal method for designing observation functions for Koopman modeling, leading to poor robustness and accuracy in control systems.
Innovation Solution
An optimization modeling and robust control method for soft robots based on a fusion prediction equation, which involves deriving measurement coordinates, designing an observation function, identifying a Koopman model, and designing a robust model predictive controller to improve accuracy and robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional analysis modeling methods are used for soft robots, then the model can capture complex hysteresis nonlinearity and fluid dynamics, but the computational complexity increases and real-time control becomes difficult
Solution Approach 1:
The patent replaces traditional mechanical analysis modeling with a data-driven Koopman operator framework. Instead of solving complex fluid dynamics equations and material hysteresis models, the system uses measured state trajectories to construct a linear observer that predicts future states. This substitution transforms an intractable nonlinear control problem into a manageable linear prediction problem while maintaining modeling accuracy.
Solution Approach 2:
The patent changes the parameter representation from physical material properties and geometric structures to empirical state variables and Koopman eigenfunctions. By transforming the modeling approach from first-principles physics to data-driven parameter estimation, the system achieves real-time computational efficiency without sacrificing the ability to capture complex soft robot behavior.
2Adaptability or versatility
If Koopman operator theory is applied to soft robot modeling, then data-driven control can be achieved, but the design of observation functions becomes complex and accuracy cannot be guaranteed
Solution Approach 1:
The patent incorporates feedback mechanisms in the observation function design process. The Koopman observer uses measured state trajectories to continuously refine the observation function selection, ensuring that the chosen functions accurately capture the system dynamics. This feedback loop between measurement and model refinement guarantees both accuracy and adaptability to customized soft robot designs.
Solution Approach 2:
The patent performs preliminary action by pre-selecting candidate observation functions based on the specific soft robot configuration before control execution. This advance preparation allows the system to adapt to customized designs while maintaining computational efficiency during real-time operation, as the most relevant observation functions are identified beforehand.
3Ease of operation
If standard linear control methods are used based on Koopman model, then control implementation is simplified, but robustness against parameter uncertainties and disturbances deteriorates
Solution Approach 1:
The patent introduces dynamic adaptation into the control system by making the Koopman observer gain matrices time-varying rather than fixed. This allows the controller to adjust its behavior in real-time based on current system conditions, maintaining simplicity of implementation while significantly improving robustness against parameter uncertainties and external disturbances through adaptive response.
Data Source
AI summary
Disclosed is an optimization modeling and robust control method for a soft robot based on a fusion prediction equation, including the following steps: deriving measurement coordinates based on the fusion prediction equation; designing an observation function based on the measurement coordinates; identifying a Koopman model based on the observation function; and designing a robust model predictive controller based on the Koopman model. Further disclosed are a fusion prediction equation and a derivation method thereof, which can derive correct, abundant but non-redundant measurement coordinates, overcoming the problem of single measurement coordinates in a soft robot system, thereby being conducive to simplifying a design process of the observation function and further improving the accuracy of the Koopman model for the soft robot.