Hybrid MPC-PID Control for Predictive Parameter Tuning
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Solution Overview
Problem
Model-less control strategies, such as PID controllers, lack predictive capabilities, while model-based optimization strategies, like MPC, are complex and computationally intensive, necessitating a solution that combines the strengths of both to enhance performance without introducing their respective drawbacks.
Innovation Solution
Incorporating a model-based MPC controller to modify parameters of a model-less PID controller, using parametric hybrid models for computational efficiency and facilitating model building and maintenance, allowing for predictive adjustments of PID coefficients during operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If model-based MPC controller is used, then predictive capabilities are improved, but device complexity increases
Solution Approach 1:
The patent merges model-based MPC and model-less PID controllers into a unified hybrid control system. The MPC controller and PID controller are integrated such that MPC modifies PID parameters online, combining the predictive capabilities of model-based control with the simplicity of model-less control in a single coordinated system.
Solution Approach 2:
The control system is segmented into distinct functional components: the MPC controller handles predictive optimization at a higher level, while the PID controller executes real-time control at a lower level. This segmentation allows each component to specialize in specific tasks, reducing overall system complexity while maintaining predictive capabilities.
2Reliability
If model-based MPC controller is used, then predictive information is incorporated, but computational load increases
Solution Approach 1:
The MPC controller applies partial optimization by modifying only the PID parameters that most significantly impact predictive performance, rather than optimizing all control parameters. This partial action reduces computational load while still incorporating essential predictive information into the control decisions.
Solution Approach 2:
The MPC controller performs preliminary optimization of PID parameters based on predictive models before the actual control execution. By pre-calculating optimal parameter adjustments using the process model, the system incorporates predictive information without requiring heavy computational resources during real-time control operations.
3Productivity
If PID controller parameters are modified online, then performance is improved, but ease of operation decreases
Solution Approach 1:
The hybrid control system provides self-service by automatically modifying PID parameters through the MPC controller based on real-time process conditions and predictive models. This eliminates the need for manual parameter tuning by operators, maintaining ease of operation while continuously improving controller performance through automated adaptive adjustments.
Solution Approach 2:
The system implements feedback mechanisms where the MPC controller continuously monitors process performance and automatically adjusts PID parameters accordingly. This closed-loop feedback ensures optimal performance is maintained without requiring operator intervention, making the system both high-performing and easy to operate.
Data Source
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AI summary
In certain embodiments, a control system includes a model-less controller configured to control operation of a plant or process. The control system also includes an automation controller operatively connected for access to a model of the plant or process being controlled by the model-less controller. The automation controller is configured to modify parameters of the model-less controller via an explicit optimization procedure.