Basepoint Estimator for Multivariable Control Equilibrium
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Solution Overview
Problem
In multivariable control systems, it is challenging to isolate changes in a single effector to achieve specific goals while adhering to multiple limits, as changes can cross-couple and affect various goals and limits with varying dynamics, making it difficult to maintain equilibrium.
Innovation Solution
A method is developed to estimate a basepoint by receiving goals, sensor feedback signals, and predicted output values from a mathematical model, calculating desired changes, and modifying them to adhere to limits, then combining these changes with previous values to produce an updated basepoint estimate, using a basepoint estimator that handles active limits and prioritizes goals to maintain system equilibrium.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a single effector is changed in isolation to affect a single goal, then the goal achievement is simplified, but in cross-coupled systems this is not possible as effector changes affect multiple goals and limits simultaneously
Solution Approach 1:
The control problem is segmented by separating the basepoint estimation (equilibrium calculation) from the control adjustments. The basepoint estimator independently calculates the equilibrium state by solving the system of equations representing all goals and limits, while the controller makes adjustments relative to this basepoint. This segmentation allows handling cross-coupled effects systematically without requiring complex coordination between multiple effectors.
Solution Approach 2:
The basepoint estimator serves as an intermediary between the multiple goals/limits and the effector controls. It receives information about system state, goals, and limits, then computes the basepoint that satisfies all constraints. This intermediary abstracts the complexity of cross-coupled interactions, presenting a simplified interface to the control system.
2Reliability
If multiple goals and limits are simultaneously enforced, then system constraints are better satisfied, but the complexity of determining equilibrium increases
Solution Approach 1:
The basepoint estimator uses parameter changes by adjusting the basepoint values (equilibrium points) based on the current system state and active constraints. When limits become active or goals change, the estimator recalculates the basepoint by solving modified system equations, effectively changing the operating parameters to maintain reliability while managing complexity through systematic recalculation.
Solution Approach 2:
The system performs preliminary action by pre-calculating the basepoint (equilibrium state) before making control adjustments. The basepoint estimator solves the system of equations in advance to determine the target state that satisfies all goals and limits, then the controller adjusts effectors to reach this pre-determined basepoint. This preliminary calculation organizes the complexity into a structured process.
3Adaptability or versatility
If the basepoint is frequently updated to respond to changing conditions, then adaptability improves, but system stability may be compromised due to abrupt changes
Solution Approach 1:
The basepoint estimator implements dynamics by continuously monitoring system state changes and actively updating the basepoint calculation when conditions change. The estimator dynamically adjusts the equilibrium point based on current sensor feedback and active constraints, allowing the system to adapt to changing conditions while maintaining stability through controlled, calculated transitions rather than abrupt changes.
Solution Approach 2:
The system uses feedback by incorporating sensor measurements of the current system state into the basepoint estimation process. The estimator receives feedback about actual system performance and constraint satisfaction, then recalculates the basepoint to reflect the updated state. This feedback loop ensures adaptability to changing conditions while maintaining stability through systematic recalculation based on actual system behavior.
Data Source
AI summary
A method of estimating a basepoint includes a plurality of goals, wherein each goal has a desired value, receiving a plurality of sensor feedback signals from a controlled system, and receiving a plurality of predicted output values of the controlled system from a mathematical model. A desired change for a plurality of basepoint values is estimated in response to the goals, the feedback, and the predicted ooutput values. An actual change in basepoint values is calculated in response to a plurality of limits and the desired change for the plurality of basepoint values. The desired change is modified as necessary to hold the limits. The actual change in basepoint values is combined with last pass values of the plurality of basepoint values to produce an updated basepoint estimate.