Integrated Quadratic Control for Multivariable Load Coordination
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Solution Overview
Problem
Optimizing industrial process control systems with multiple operational constraints is complex, especially in multivariable environments, where existing controllers like MPC and PID struggle to determine optimal profit while managing variables across loads.
Innovation Solution
The implementation of an integrated quadratic controller (IQC) that determines target process values, calculates quadratic programming (QP) optimized proportional action gains, and applies these gains to control systems to manage loads effectively, maximizing profit by allocating proportional gain weights within defined value ranges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional PID or MPC controllers are used to manage multivariable industrial processes, then basic control functions are maintained, but the ability to optimize overall profit while managing multiple load variables simultaneously is insufficient
Solution Approach 1:
The patent combines PID control with quadratic programming optimization into a unified controller architecture. The PID controller manages individual load variables while the quadratic programming component optimizes the overall process output by coordinating multiple manipulated variables, resolving the contradiction between maintaining basic control functions and achieving profit optimization.
Solution Approach 2:
The controller dynamically adjusts the weighting factors in the quadratic programming objective function based on current process conditions and operational constraints. This allows the system to adaptively prioritize different manipulated variables at different times, enabling profit optimization while managing the complexity of multivariable control through dynamic reconfiguration.
2Productivity
If multiple manipulated variables are adjusted to maximize process output, then overall profit increases, but coordinating the interaction between loads becomes increasingly complex
Solution Approach 1:
The quadratic programming component continuously monitors the process value and manipulated variable outputs, using this feedback to adjust the optimal distribution of control actions among multiple loads. This feedback mechanism automates the coordination complexity, allowing the system to maximize output without manual intervention in the complex interactions between multiple manipulated variables.
Solution Approach 2:
The controller changes the weighting parameters in the quadratic programming objective function based on current process conditions, allowing dynamic re prioritization of different manipulated variables. This parameter adaptation simplifies coordination by automatically adjusting which loads receive more control attention based on real-time process state and operational constraints.
3Productivity
If quadratic programming optimized proportional action is integrated into the control system, then profit optimization is achieved, but computational complexity increases
Solution Approach 1:
The controller is segmented into distinct functional components: the PID control layer that handles individual variable regulation and the quadratic programming layer that handles profit optimization. This segmentation allows each component to perform its specialized function with appropriate computational complexity, preventing the entire system from becoming computationally intractable while still achieving profit optimization.
Data Source
AI summary
A method and system for operating an industrial process using an integrated quadratic controller (IQC) is provided for a single input multiple output system (SIMO). A target process value (PV) associated with an output of the industrial process is used to define an optimal output of the industrial process. Manipulated-variables (MVs) associated with a plurality of loads within the industrial process are utilized for controlling a rate of operation associated with the load impacting the PV of the industrial process. Quadratic programming (QP) is defined to optimize proportional action gains for the determined MVs given an update control variable PV. QP gain-modified proportional action are integrated into a control system of the industrial process and a modified control system to update MV output state map defining the multiple output to control respective loads in the industrial control process for achieving the target PV is generated.