Basepoint Estimator for Multivariable Control Systems
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Solution Overview
Problem
In multivariable control systems, it is challenging to estimate a basepoint that represents a state of equilibrium where the rate of change is zero, while meeting predetermined limits and fulfilling a quantity of goals, especially in cross-coupled systems where changes in one effector can affect multiple goals and limits with varying dynamics.
Innovation Solution
A method is developed to estimate a basepoint by receiving goals and sensor feedback signals, using a mathematical model to predict output values, and calculating desired and actual changes in basepoint values based on goal weights and limits, while employing quadratic programming to manage active limits and prioritize goals, ensuring smooth transitions and adherence to system constraints.
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 control simplicity is improved, but in a cross-coupled system this approach fails to account for the interdependencies where one effector change affects multiple goals and limits
Solution Approach 1:
The control problem is segmented into two distinct phases: basepoint estimation (solving for equilibrium values satisfying limits and goals) and control calculation (determining effector changes from the basepoint). This segmentation allows complex cross-coupled systems to be handled systematically by first finding a valid equilibrium state, then computing the control actions needed to reach it.
Solution Approach 2:
The basepoint estimator acts as an intermediary component between the system model and the control calculator. It receives the system model, active limits, and goals, then produces basepoint values that serve as a foundation for subsequent control calculations. This intermediary handles the complexity of cross-coupling by explicitly solving for equilibrium conditions before control actions are determined.
2Adaptability or versatility
If multiple goals are pursued simultaneously in a cross-coupled system, then the comprehensiveness of control is improved, but the complexity of determining equilibrium basepoint values increases
Solution Approach 1:
The basepoint estimator is designed to dynamically adapt to changing system conditions. When limits become active or goals change, the estimator recalculates basepoint values to reflect the new equilibrium state. This dynamic approach allows the system to handle multiple goals and changing constraints without requiring a complete redesign of the control architecture.
Solution Approach 2:
The system manages complexity by changing parameters systematically: first determining which limits are active, then using those active limits along with goal specifications to solve for basepoint values. The goal weights parameter allows prioritization among multiple goals, and the method systematically adjusts basepoint values to satisfy both hard limits and weighted goals in a structured manner.
3Reliability
If the system strictly enforces all limits, then the safety and constraint satisfaction are improved, but the ability to achieve multiple goals may be compromised due to conflicting constraints
Solution Approach 1:
The system applies partial action to goals by using goal weights to determine the degree to which each goal is pursued. When limits conflict with goals, the basepoint estimator finds equilibrium values that partially satisfy goals while strictly satisfying active limits. The goal weights allow the system to prioritize among competing goals, achieving the most important objectives within the constraints imposed by active limits.
Data Source
AI summary
A method of estimating a basepoint includes receiving a plurality of goals, wherein each goal has a desired value, and receiving a plurality of sensor feedback signals from a controlled system. A plurality of predicted output values of the controlled system are received 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 output 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 according to a plurality of goal weights while holding 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.