Model Predictive Control with Asymmetric Best-Value Targeting
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Solution Overview
Problem
Existing model predictive controllers face challenges in achieving superior dynamic performance due to infeasible steady-state targets and asymmetric dynamic responses, particularly when dealing with multiple control objectives and dynamic speed of response, which leads to suboptimal economic benefits and transient losses.
Innovation Solution
The system computes best performance values (BPVs) for each controlled variable, adjusting them based on dynamic tuning weights and steady-state targets to ensure consistent dynamic and static control objectives, slowing down when moving away from setpoints and speeding up when moving towards economic profit, thereby optimizing control performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If steady-state targets are used for control, then control stability is improved, but dynamic performance and economic benefits deteriorate due to infeasibility and asymmetric response
Solution Approach 1:
The patent applies dynamics by making the control target adaptive rather than static. The controller dynamically switches between steady-state targets and high-potential operating points based on feasibility assessment. When steady-state targets become infeasible, the system transitions to alternative operating points that maintain economic optimality, thereby preserving dynamic performance while maintaining control stability through structured adaptation.
Solution Approach 2:
The patent changes the control target parameter from fixed steady-state values to variable high-potential operating points. The system evaluates the feasibility of steady-state targets and, when infeasible, computes alternative operating points that satisfy constraints while maximizing economic benefits. This parameter change enables the controller to achieve superior dynamic performance without sacrificing stability.
2Measurement precision
If asymmetric dynamic response is used to handle infeasible setpoints, then setpoint tracking is improved, but economic performance deteriorates due to transient losses
Solution Approach 1:
The patent applies asymmetry by treating the approach to and departure from setpoints differently based on economic optimality. When the steady-state target is infeasible, the controller asymmetrically prioritizes maintaining economic benefits over precise setpoint tracking. The system allows temporary deviations from setpoints when doing so preserves operation at high-potential economically optimal points, thereby reducing transient economic losses while maintaining adequate tracking performance.
3Ease of operation
If static targets are chased instead of meeting original control objectives, then control simplicity is improved, but cumulative economic benefits deteriorate
Solution Approach 1:
The patent applies preliminary action by pre-computing high-potential operating points that satisfy constraints and maximize economic benefits. These pre-computed targets are stored and readily available for immediate use when steady-state targets become infeasible. This preliminary preparation maintains control simplicity by providing ready-made alternative targets while ensuring cumulative economic benefits are preserved through proactive identification of economically optimal operating points.
4Speed
If controller follows tuned dynamic speed always, then response speed is improved, but economic optimality deteriorates when moving away from setpoints
Solution Approach 1:
The patent applies dynamics by making the controller speed adaptive rather than fixed. The system adjusts the dynamic response speed based on the direction of movement relative to economic optimality. When moving towards economically optimal high-potential operating points, the controller follows the tuned dynamic speed for fast response. When moving away from setpoints in directions that reduce economic profit, the controller slows down to prioritize economic optimality, thereby balancing speed and economic performance dynamically.
Data Source
AI summary
Model predictive control is used to obtain the best performance value for an objective in a dynamic environment. One or more best performance values for a model predictive application are obtained by utilizing asymmetric dynamic behavior that pushes the process to the edges of the operative window. When a setpoint becomes infeasible, a controller slows down when moving away from a specified setpoint. When the infeasibility clears and the controller starts moving back towards the setpoint, the controller follows a tuned speed. When the controller moves in a direction against economic profit, the controller slows down and when moving in the direction of economic profit, the controller follows the tuned dynamic speed. The controller computes the best performance value for each controlled variable and is equal to the setpoint for the controlled variables with a setpoint or the highest profit value between limits for controlled values affected by economic functions.