Using sensor data to assist in controlling a target system by modeling the functionality of the target system
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Solution Overview
Problem
Existing automated control systems face challenges in managing uncertainty and making timely decisions with partial information, particularly in controlling physical systems like batteries with varying internal states and loads, where existing architectures and technologies struggle to accurately model and optimize operations.
Innovation Solution
The implementation of tomographic techniques to model the current state and operational characteristics of target systems, using data from sensors to generate improved models and soft rules, enabling better decision-making through Collaborative Distributed Decision (CDD) systems that incorporate data tomograph components for state estimation and control action determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing automated control systems are used to manage physical systems like batteries, then basic control functionality is provided, but accuracy in modeling internal state and operational characteristics deteriorates due to uncertainty and partial information
Solution Approach 1:
The patent introduces tomographic techniques as an intermediary method to reconstruct internal system states from external sensor measurements. The tomograph component acts as a mediator that transforms partial external observations into comprehensive internal state estimates, enabling accurate modeling of battery temperature, charge state, and other internal characteristics without direct internal sensors
Solution Approach 2:
The patent replaces direct physical measurement mechanisms (internal sensors) with computational modeling approaches. Instead of mechanically placing sensors inside the battery, the system uses mathematical tomographic reconstruction algorithms to infer internal states from external measurements, substituting physical measurement with computational analysis
2Productivity
If existing control architectures are used, then system operation is maintained, but decision-making speed deteriorates due to computational complexity and information processing requirements
Solution Approach 1:
The patent performs preliminary computational work by pre-computing system models and operational characteristics during system initialization or offline phases. The tomographic models and control policies are prepared in advance, allowing the control system to make rapid decisions during operation by querying pre-computed models rather than performing complex real-time calculations
Solution Approach 2:
The patent divides the control system into modular components: the tomograph component for state estimation, the control policy component for decision-making, and the execution component for actuation. This segmentation allows each component to operate independently and efficiently, with the tomograph providing pre-processed state information that the control policy can quickly utilize
3Adaptability or versatility
If traditional control methods are used, then simple operations are controlled, but adaptability to varying system conditions and loads deteriorates
Solution Approach 1:
The patent implements dynamic control by continuously updating the tomographic model of the system state and adjusting control policies based on current conditions. The control system transitions from static pre-programmed responses to dynamic adaptive control where the tomograph continuously reconstructs system state and the control policy adapts in real-time to varying loads, temperatures, and operational conditions
Solution Approach 2:
The patent changes control parameters dynamically based on tomographic state estimation. Instead of fixed control parameters, the system adjusts voltage, current, and other operational parameters based on the reconstructed internal state, enabling adaptive control that responds to changing system conditions while managing complexity through model-based parameter adjustment
Data Source
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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.