Tomographic State Modeling for Automated Control Under Uncertainty
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Solution Overview
Problem
Existing automated control systems face challenges in managing uncertainty and effectively controlling physical systems with incomplete information, particularly in systems like batteries where internal states and external conditions are dynamic and uncertain.
Innovation Solution
The implementation of tomographic techniques to model the internal operations of target systems using sensor data, allowing for the generation of improved state models that can correct conflicts in state information and enable better decision-making through Collaborative Distributed Decision (CDD) systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional automated control systems are used to manage physical systems with incomplete information, then the system structure remains simple, but the measurement precision and reliability of state information deteriorate
Solution Approach 1:
The patent introduces tomographic reconstruction algorithms as an intermediary computational layer that processes sensor data to generate accurate 3D models of internal system states. This mediator transforms incomplete sensor measurements into comprehensive state information without requiring direct physical access to all system parameters, thereby improving measurement precision while managing complexity through software-based reconstruction rather than hardware expansion
Solution Approach 2:
The patent replaces traditional mechanical or direct physical measurement approaches with computational tomographic methods. Instead of using multiple physical sensors throughout the system to directly measure internal states, the system uses external sensors combined with tomographic reconstruction algorithms to infer internal conditions, substituting computational processing for physical measurement infrastructure
2Measurement precision
If more sensors are added to capture complete system state information, then measurement precision improves, but device complexity and cost increase
Solution Approach 1:
The patent segments the measurement problem into multiple projection views from different sensor locations, where each sensor captures a partial projection of the internal system state. The tomographic reconstruction algorithm then integrates these segmented measurements to reconstruct the complete 3D state model, achieving comprehensive knowledge through distributed simple measurements rather than a single complex sensor array
Solution Approach 2:
The patent transforms the measurement problem from direct spatial measurement into a mathematical reconstruction problem in a different dimension. By measuring system properties along multiple projection directions and reconstructing the 3D state space mathematically, the system achieves complete state knowledge using fewer physical sensors, effectively solving the measurement problem in a higher-dimensional mathematical space rather than physical space
3Speed
If real-time control decisions are made with partial information, then response speed improves, but reliability of control actions deteriorates
Solution Approach 1:
The patent performs preliminary tomographic reconstruction of the system state model using available sensor data before making control decisions. This pre-processing step creates an accurate 3D representation of internal system conditions that informs subsequent control actions, ensuring reliability is established before the control decision is executed rather than attempting correction after the fact
Solution Approach 2:
The patent implements a feedback mechanism where control actions are continuously adjusted based on updated tomographic reconstructions of system state. The system monitors changes in reconstructed internal states and modifies control decisions accordingly, creating a closed-loop system that maintains reliability through continuous verification and adjustment based on actual system conditions
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 enhances the accuracy and efficiency of control actions by providing a comprehensive understanding of the system's state, improving uncertainty management and optimizing the operation of batteries and other physical systems, leading to better performance and longevity.
Implementation Method 1
a tomograph component that uses sensor data about operations of the target system to generate an improved model of a current state and operational characteristics of the target system
Data Source
AI summary
Techniques are described for implementing automated control systems to control operations of specified physical target systems. In some situations, the described techniques include obtaining and analyzing sensor data about operations of a target system in order to generate an improved model of a current state of the target system, and using the modeled state information as part of determining further current and/or future automated control actions to take for the target system, such as to generate a function and/or other structure that models internal operations of the target system, rather than merely attempting to estimate target system output from input without understanding the internal structure and operations of the target system.