Wireless MPC Control Using Internal Process Model Simulation
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Solution Overview
Problem
Current process control systems face challenges in maintaining robust and efficient control using non-periodic or intermittent wireless process signals, particularly for multiple-input/multiple-output control routines like model predictive control (MPC), due to irregular measurement updates which can lead to inaccurate control and increased power consumption.
Innovation Solution
A MPC control routine that uses an internal process model to simulate measured process parameter values during irregular updates, applying model bias correction only when new measurements are available, and employing a multi-rate control approach to manage different measurement rates for various controlled variables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If non-periodic or intermittent wireless process signals are used for MPC control, then power consumption is reduced and wireless communication efficiency is improved, but control accuracy and system reliability deteriorate due to irregular measurement updates
Solution Approach 1:
The system performs preliminary actions by predicting process variable values using an internal process model between measurement updates. The MPC controller uses the process model to simulate and predict future process states, allowing control calculations to proceed with predicted values rather than waiting for actual measurements, thus maintaining control continuity while reducing communication frequency
Solution Approach 2:
The system implements a dual-feedback mechanism: (1) Model bias correction feedback - when a new measurement is received, the difference between the measured value and the model-predicted value is calculated and used to correct the process model bias; (2) Control feedback - the corrected model predictions are used to generate control outputs. This feedback structure allows the system to maintain accuracy while operating with intermittent measurements
2Productivity
If non-periodic measurement updates are used, then wireless communication load is reduced, but control loop stability and performance worsen due to irregular feedback intervals
Solution Approach 1:
The process model acts as an intermediary between the intermittent measurements and the continuous control requirements. Instead of directly using irregular measurements for control calculations, the model interpolates and predicts process states between measurements, providing a continuous representation of process behavior that maintains control loop stability
Solution Approach 2:
The system dynamically changes the parameter update strategy based on measurement availability. When measurements are received, the model bias parameter is updated; when measurements are not available, the controller continues operating with the current bias value and model predictions, effectively adapting the control parameter update rate to the measurement rate
3Measurement precision
If model bias correction is applied at every controller scan, then control accuracy is improved, but power consumption and communication frequency increase
Solution Approach 1:
The system implements periodic model bias correction only when new measurements are received from wireless sensors, rather than at every controller scan. This creates an event-driven correction mechanism where the bias update action is triggered by measurement arrival, reducing the frequency of corrections and associated power consumption while maintaining accuracy when data is available
Data Source
AI summary
A multiple-input/multiple-output control routine in the form of a model predictive control (MPC) routine operates with wireless or other sensors that provide non-periodic, intermittent or otherwise delayed process variable measurement signals at an effective rate that is slower than the MPC controller scan or execution rate. The wireless MPC routine operates normally even when the measurement scan period for the controlled process variables is significantly larger than the operational scan period of the MPC controller routine, while providing control signals that enable control of the process in a robust and acceptable manner. During operation, the MPC routine uses an internal process model to simulate one or more measured process parameter values without performing model bias correction during the scan periods at which no new process parameter measurements are transmitted to the controller. When a new measurement for a particular process variable is available at the controller, the model prediction and simulated parameter values are updated with model bias correction based on the new measurement value, according to traditional MPC techniques.