Underactuated Multi-DOF Control Using Energy-Based Inverse Dynamics
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Solution Overview
Problem
Existing control methods for underactuated mechanical systems, such as robotic manipulators, face challenges in accurately modeling and controlling inverse dynamics due to complex correlations between joint torques and limited data efficiency, especially when using black-box machine learning approaches like deep neural networks and Gaussian Process Regression (GPR).
Innovation Solution
The implementation of a multi-output Gaussian Process Regression (GPR) model with a Lagrangian Inspired Polynomial (LIP) kernel, which embeds physical properties from Lagrangian mechanics to model correlations between joint torques, improving data efficiency and generalization by estimating kinetic and potential energy, and using an energy-based controller for stabilization and control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If black-box machine learning approaches (deep neural networks, Gaussian Process Regression) are used to model inverse dynamics, then the control system can handle complex correlations between joint torques, but the data efficiency is limited and extensive training data is required
Solution Approach 1:
The patent transforms the modeling approach from directly modeling joint torques to modeling kinetic and potential energy parameters. By using energy-based parameters as intermediate representations, the system achieves more accurate inverse dynamics modeling with fewer data points, as energy parameters inherently capture the physical constraints and correlations in the system
Solution Approach 2:
The patent introduces kinetic energy and potential energy as intermediary variables between the physical system and the control model. These energy-based intermediaries serve as physically-informed features that bridge the gap between raw joint states and torque requirements, improving model accuracy while reducing data dependency
2Ease of manufacture
If conventional model-based approaches are used to derive parametric models from first principles of physics, then the control model has clear physical interpretation, but the performance is limited by parametric uncertainty and inability to describe complex dynamics
Solution Approach 1:
The patent replaces traditional mechanical modeling approaches with a machine learning-based energy modeling approach. Instead of deriving parametric models from first principles, the system uses Gaussian Process Regression to learn energy parameters from data, thereby capturing complex nonlinear dynamics while maintaining physical interpretability through the energy-based formulation
Solution Approach 2:
The patent creates a composite modeling approach that combines the physical interpretability of energy-based models with the data-driven flexibility of Gaussian Process Regression. This hybrid approach integrates the strengths of both conventional physics-based modeling and modern machine learning, achieving accurate representation of complex dynamics
3Adaptability or versatility
If the mechanical system has multiple degrees of freedom with different actuators, then the system can perform complex tasks, but the control complexity increases due to correlations between joint torques
Solution Approach 1:
The patent changes the control parameters from joint torques to energy parameters (kinetic and potential energy). This transformation simplifies the control of multi-DOF systems by using energy as a unified measure that naturally captures the interactions between different joints and actuators, reducing control complexity while maintaining task capability
Data Source
AI summary
A method for controlling a mechanical system utilizes an energy-based inverse dynamics model trained to map dynamic states of the mechanical system to corresponding torques for a plurality of actuators of the mechanical system. The method comprises collecting a feedback signal including current states of dynamics of the mechanical system. The method further comprises processing the current states of dynamics with the energy-based inverse dynamics model to produce values of the torques for the plurality of actuators and values of the potential and kinetic energy of the mechanical system. The method further comprises controlling the mechanical system based on the produced values of the torques for the plurality of actuators of the mechanical system and the values of the potential and kinetic energy of the mechanical system.