Control Signal Offset Strategy for Disturbance Threshold Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Control systems are susceptible to disturbances that cause signal characteristics to deviate beyond predefined thresholds, adversely impacting their operation and service.
Innovation Solution
A system and method that adds a target offset signal to control systems to minimize disturbances, maintaining signal characteristics within defined thresholds by iteratively transferring units from a first data bucket to a second bucket until the holistic feature converges within a target volume defined by constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If control systems operate without disturbance minimization, then system complexity remains low, but signal characteristics deviate beyond thresholds causing operational failures
Solution Approach 1:
The system pre-calculates target offset signals based on historical disturbance patterns and system characteristics before disturbances occur. These pre-computed offset signals are stored and applied when disturbances are detected, eliminating the need for complex real-time disturbance analysis while maintaining reliability.
Solution Approach 2:
A target offset signal acts as an intermediary between the disturbance source and the control system. This offset signal mediates the effect of disturbances by providing pre-computed compensation values that adjust the control output, simplifying the overall control architecture while improving signal characteristic stability.
2Reliability
If target offset signals are applied to minimize disturbances, then signal characteristics remain within thresholds, but system complexity increases due to additional processing
Solution Approach 1:
Target offset signals are pre-calculated and stored in lookup tables based on system characteristics and typical disturbance scenarios. During operation, the system simply retrieves and applies the appropriate pre-computed offset signal, avoiding complex real-time calculations and reducing processing complexity while maintaining operational reliability.
Solution Approach 2:
The system automatically selects and applies appropriate target offset signals based on detected disturbance conditions without requiring complex external control or intervention. The pre-computed offset signals self-adjust to compensate for disturbances, reducing the need for complex processing logic.
3Measurement precision
If real-time disturbance compensation is implemented, then control accuracy improves, but computational load increases
Solution Approach 1:
Disturbance compensation values are pre-calculated and stored during system setup or offline analysis. During real-time operation, the system retrieves these pre-computed values based on detected disturbance conditions, achieving accurate real-time compensation without the computational burden of real-time calculations, thus reducing energy consumption.
Solution Approach 2:
Instead of performing complex real-time disturbance analysis and calculation, the system uses pre-computed copies of disturbance compensation data stored in lookup tables. These copies provide accurate compensation values without requiring expensive real-time computational resources, reducing energy consumption while maintaining control accuracy.
Data Source
AI summary
A method includes receiving a data population and a plurality of constraints. The method includes representing a plurality of units corresponding to the data population by a first set of vectors, determining a holistic feature, and representing the holistic feature by a second vector. The method further includes defining a target volume based on a subset of the plurality of constraints, and for each unit, subtracting a vector representing a respective unit from a vector representing a current holistic feature to determine a third vector. The method thus includes instantiating a first data bucket and a second data bucket based on the plurality of units and the units corresponding to the current data population, and for each third vector, transferring units from the first data bucket to the second data bucket so as to cause the holistic feature to converge to the current holistic feature defined by the target volume.