Adaptive Surrogate Control for Uncertain Electro-Mechanical Systems
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Solution Overview
Problem
Existing control methods for high-dimensional physical systems face challenges due to model-based techniques' computational expense and data-driven techniques' lack of performance guarantees under parametric uncertainties, leading to inaccurate and non-robust control policies.
Innovation Solution
A computer-implemented method using an adaptive surrogate model with a weighted combination of neural ODEs in latent space, allowing online tuning of weights to address uncertainties, combined with a polytopic representation and online adaptation laws for robust control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If model-based techniques are used for control policy design, then control accuracy is improved, but computational cost increases significantly
Solution Approach 1:
The patent creates a surrogate model that copies the essential dynamics of the high-dimensional physical system in a reduced-dimensional space. This surrogate model reproduces the system behavior with much lower computational cost while maintaining sufficient accuracy for control applications. The copying approach allows the system to use a simplified representation instead of the full complex model.
Solution Approach 2:
The patent extracts the most critical dynamic characteristics of the physical system and represents them in a reduced-order surrogate model. By taking out only the essential dynamics needed for control and discarding redundant computational complexity, the system achieves acceptable control accuracy with significantly reduced computational burden.
2Use of energy by stationary object
If data-driven techniques are used for control policy construction, then computational cost is reduced, but performance reliability deteriorates under parametric uncertainties
Solution Approach 1:
The patent introduces physics-informed constraints as an intermediary between purely data-driven approaches and full physical models. These constraints act as a mediator that guides the surrogate model to respect fundamental physical laws while still using operational data for training. This intermediary mechanism ensures performance reliability under parametric uncertainties by anchoring the data-driven model to physical reality.
Solution Approach 2:
The patent changes the parameter representation by using reduced-order variables and latent space representations instead of full high-dimensional parameters. This parameter transformation allows the system to work with fewer, more meaningful variables that capture essential system behavior, improving both computational efficiency and robustness to uncertainties.
3Use of energy by stationary object
If reduced-order models are constructed using projection frameworks, then computational cost is reduced, but model accuracy deteriorates for general nonlinear problems
Solution Approach 1:
The patent makes the reduced-order surrogate model adaptive and dynamic by training it on operational data that captures varying system conditions. Instead of a static projection framework, the model dynamically adjusts its parameters and representations based on the actual operating regime, maintaining accuracy across different nonlinear scenarios while keeping computational costs low.
Solution Approach 2:
The patent performs preliminary training of the surrogate model using operational data collected under various conditions before deployment. This preliminary action prepares the model to handle general nonlinear problems by pre-learning the system's behavior patterns, so that during actual operation, the model can provide accurate predictions without requiring complex real-time computations.
Data Source
AI summary
A control method for controlling an electro-mechanical system according to a task estimates the state of the system using an adaptive surrogate model of the system to produce an estimation of the state of the system. The adaptive surrogate model includes a neural network employing a weighted combination of neural ODEs of dynamics of the system in latent space, such that weights of the weighted combination of neural ODEs represent the uncertainty. The method controls the system according to the task based on the estimation of the state of the system and tunes the weights of the weighted combination of neural ODEs based on the controlling.