Model Predictive Control Steady State Error Reduction
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Solution Overview
Problem
Model predictive control systems often maintain sustained steady state errors due to their design to avoid rapid changes, leading to constant errors between actual and desired variable levels, which existing techniques like offset corrections only temporarily address.
Innovation Solution
A technique that detects steady state errors over a persistence time and modifies the model predictive control algorithm's cost function by increasing coefficients for variables not at the desired level, using the integral of the difference between the controlled variable and its desired level over a forward-looking control horizon to drive the variable to the desired level.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If model predictive control systems are designed to avoid rapid changes in variable levels, then system stability is improved, but steady state error increases
Solution Approach 1:
The cost function coefficients are made dynamic rather than static. The coefficient for the controlled variable in the cost function is adjusted based on the magnitude of the predicted steady state error, allowing the system to adapt its control aggressiveness to the current error conditions while maintaining overall stability.
Solution Approach 2:
The patent changes the parameter values in the cost function based on the predicted error. Specifically, the coefficient weighting the controlled variable is modified according to the integral of the predicted error over the prediction horizon, enabling the system to eliminate steady state error without causing excessive oscillations.
2Manufacturing precision
If offset corrections are used to drive the controlled variable to the desired level, then steady state error is reduced, but system complexity increases
Solution Approach 1:
The model predictive control system performs self-adjustment by dynamically modifying its own cost function based on predicted performance. The system uses its internal model to predict future errors and automatically adjusts the cost function coefficients to eliminate these predictions, eliminating the need for external offset corrections or additional control components.
3Manufacturing precision
If cost function coefficients are increased to drive the controlled variable to the desired level, then steady state error is reduced, but system response becomes overly aggressive
Solution Approach 1:
The coefficient adjustment is dynamic and proportional to the predicted error magnitude. When the predicted error is large, the coefficient is increased to drive the system toward the setpoint more aggressively. When the error is small or the system is close to the setpoint, the coefficient returns to its normal value, preventing excessive oscillations and maintaining smooth response.
Solution Approach 2:
The system uses the predictive model to anticipate future steady state errors before they occur. By modifying the cost function coefficients based on predicted rather than actual errors, the system takes preliminary action to prevent error accumulation, adjusting control aggressiveness in advance based on forecasted performance.
Data Source
AI summary
A technique is disclosed for reducing an error in a controlled variable via model predictive control. A predicted error in the controlled variable is determined for a forward-looking control horizon based upon measured or computed variables. The integral of the predicted error is computed. If the error or the integral exceed a tolerance for a determined time period, the model predictive control algorithm is modified to drive the error or the integral to within a tolerance. The modifications to the control algorithm may include changes to coefficients for terms based upon the error and/or the integral of the error.