Task-Specific Robot Model Reduction via Minimum Stable Balanced
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Solution Overview
Problem
Existing robot control methods rely on manually formulated simplified dynamics models, which are not well validated against full dynamics models, and lack automation in selecting state and control input mappings, making it difficult to design effective controllers for robots with many degrees of freedom.
Innovation Solution
A computer-implemented method for automatically simplifying robot models through task-specific model reduction, using a minimum stable balanced reduction technique to find a reduced-order model and stabilizing controller that matches the full model's stability, and formulating the model with task-specific outputs to suit complex robot tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual model simplification is used, then controller design becomes easier, but model accuracy and validation against full dynamics models deteriorates
Solution Approach 1:
The system performs self-validation by automatically comparing the reduced-order model's stability properties against the full dynamics model. The validation process is embedded within the model reduction workflow, allowing the system to self-verify accuracy without external manual intervention, thus maintaining both ease of operation and model reliability.
Solution Approach 2:
The invention implements a feedback mechanism where the stability characteristics of the reduced-order model are continuously validated against the full dynamics model. This feedback loop ensures that the simplified model maintains adequate accuracy by comparing eigenvalue placements and stability margins, allowing iterative refinement if validation fails.
2Reliability
If full dynamics models are used, then model accuracy is maintained, but controller design complexity increases
Solution Approach 1:
The invention extracts only the essential dynamic characteristics from the full dynamics model by performing eigenvalue analysis and identifying dominant modes. This extraction process creates a reduced-order model that captures the critical stability properties while eliminating unnecessary complexity, achieving both accuracy and simplicity.
Solution Approach 2:
The reduced-order model applies local quality by focusing computational effort on the most critical dynamic modes and stability characteristics rather than treating all system states uniformly. The model reduction technique identifies and retains only the locally important dynamics that affect overall system stability, discarding less significant degrees of freedom.
3Measurement precision
If automated model reduction is implemented, then model validation improves, but computational complexity increases
Solution Approach 1:
The validation process applies partial action by performing eigenvalue analysis and stability comparison only on the critical modes identified during model reduction, rather than exhaustively validating all possible aspects. This selective validation approach achieves sufficient precision without requiring excessive computational resources for complete system analysis.
Data Source
AI summary
The disclosure provides an approach for automatically determining task-specific robot model reductions. In one embodiment, a simplification application determines a smallest order statespace model whose stabilizing controller also stabilizes a full-order robot model. The simplification application may determine such a model via an iterative procedure in which the reduced order is initialized to the number of unstable poles of the open-loop full-order system and, while the closed loop full-order system with the balanced reduced order system's stabilizing controller is unstable, fractional balanced reduction is applied to generate a balanced reduced system. If one or more unstable closed-loop poles exist in the full-order system with the stabilizing controller of the newly-generated balanced reduced system, the reduced order is incremented by one, and fractional balanced reduction repeated, until no unstable closed-loop poles remain. In another embodiment, the model reduction is made task-specific by formulating the full model with task-specific outputs.