Intelligent Controller Data Correction for Unknown System Parameters
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Solution Overview
Problem
Existing control systems face challenges in optimizing control over systems when little or no information about the system is known, leading to suboptimal performance, as they are typically designed for predetermined behaviors under normal operational conditions.
Innovation Solution
The development of computational methods and systems that process operational data from intelligent controllers to identify and correct information about unidentified systems and dynamical components, using a combination of 'crowdsourced' data and empirical run data to estimate unknown parameters, such as in the case of HVAC systems, through neural network-based techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional control systems are designed for predetermined behaviors under normal operational conditions, then the control system can operate reliably under known conditions, but the control performance becomes suboptimal when little or no information about the system is known
Solution Approach 1:
The system performs preliminary identification of system parameters and characteristics before optimal control can be implemented. By collecting operational data during normal operation and using it to build system models in advance, the controller prepares control strategies that will be optimal when applied, thus resolving the contradiction between reliability under known conditions and performance when information is unknown
Solution Approach 2:
The system continuously collects operational data from sensors and uses this feedback to update system models and refine control parameters. This closed-loop approach allows the controller to adapt to actual system behavior, improving performance when system information is initially unknown while maintaining reliability through continuous validation against actual operations
2Stability of the object's composition
If controllers output control signals based on control model and sensor feedback, then the controller can maintain predetermined system behavior, but the control over the system is less than optimal when little or no specific information regarding the system is known
Solution Approach 1:
The control system transitions from static predetermined behavior to dynamic adaptation by continuously updating system models based on operational data. The controller adjusts its control strategy in real-time based on identified system parameters, maintaining stability through controlled adaptation rather than rigid predetermined behavior, thus achieving both stability and optimization
Solution Approach 2:
The system identifies and adjusts control parameters based on operational data to optimize performance. By changing parameters such as control gains, setpoints, and timing based on actual system behavior observed during operation, the controller achieves optimal performance while maintaining stable operation through systematic parameter adjustment
Data Source
AI summary
Computational methods and systems that collect operational data from an intelligent controller to identify information, or correct information, about a device and system controlled by the intelligent controller are disclosed. Computational methods and systems use a set of operational data and information known about other devices and systems controlled by similar intelligent controllers to process the operational data and generate information, or correct information, about the device and system.