Consolidating Rate-of-Change Constraints in Model Predictive Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional engine control systems for motor vehicles do not accurately control engine output torque and fail to rapidly respond to control signals, especially when considering fuel economy and coordinating torque control among various devices affecting engine output.
Innovation Solution
A method and system that consolidate upper and lower constraints with rate-of-change constraints in a Model Predictive Control (MPC) system, allowing for the selection of command values that minimize computational time and computing power by using either upper or lower constraints and their respective rate-of-change constraints for each controlled variable.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If MPC control system uses multiple controlled variables with several constraints including upper and lower limits as well as rate-of-change limits, then control accuracy and response are improved, but computational time and computing power requirements increase extensively
Solution Approach 1:
The patent extracts and separates rate-of-change constraints from the main constraint set, handling them differently from upper and lower limits. This selective extraction reduces the complexity of the optimization problem by treating rate constraints as secondary considerations rather than primary constraints, thereby reducing computational time while maintaining control accuracy.
Solution Approach 2:
The patent transforms the control problem by changing parameters from continuous optimization with multiple constraints to a discrete selection problem. By formulating the solution as selecting the minimum cost command value from a finite set of possible values that satisfy consolidated constraints, the computational complexity is significantly reduced while preserving control precision.
2Manufacturing precision
If MPC control system uses multiple controlled variables with several constraints including upper and lower limits as well as rate-of-change limits, then control accuracy and response are improved, but computing power requirements increase extensively
Solution Approach 1:
The patent extracts rate-of-change constraints from the main constraint set and handles them separately. This separation reduces the dimensionality of the optimization problem, lowering computing power requirements while maintaining the ability to enforce all constraints including rate limits.
Solution Approach 2:
The patent changes the problem formulation from continuous optimization requiring extensive computing power to a discrete selection problem. By consolidating constraints and selecting from a finite set of possible command values, the computing power requirement is dramatically reduced while control accuracy is preserved.
3Device complexity
If traditional engine control systems are used, then system complexity is reduced, but torque control accuracy and response speed deteriorate
Solution Approach 1:
The patent transforms the complex continuous optimization problem into a discrete selection problem by consolidating constraints and selecting from a finite set of possible command values. This parameter transformation maintains torque control accuracy and response speed while significantly reducing system complexity and computational requirements.
Data Source
AI summary
A method, control system, and propulsion system use model predictive control to control and track several parameters for improved performance of the propulsion system. Numerous sets of possible command values for a set of controlled variables are determined. Initial constraints for the controlled variables are determined, which include upper and lower limits for each controlled variable and upper and lower rate-of-change limits for each controlled variable. A set of consolidated constraint limits for the controlled variables is then determined. Each consolidated constraint limit is determined by consolidating one of the upper and lower limits with one of the upper and lower rate-of-change limits. A cost for each set of possible command values is determined, and the set of possible command values that has the lowest cost and falls within the set of consolidated constraint limits is selected for use in controlling the propulsion system.