Last updated on Dec 10, 2024

How do you incorporate feedback and learning in model predictive control?

Model predictive control (MPC) is a powerful technique for designing control systems that can optimize the performance of complex and uncertain processes. However, MPC also faces some challenges, such as dealing with nonlinearities, disturbances, constraints, and model mismatch. In this article, you will learn how to incorporate feedback and learning in MPC to overcome these challenges and improve your control system design.

1 Feedback in MPC

Feedback is essential for any control system, but especially for MPC, which relies on a model of the process to predict and optimize the future behavior. Feedback allows the controller to adjust the control actions based on the actual measurements of the process output, and to correct any errors or deviations from the desired setpoint. Feedback also helps to reduce the effects of disturbances and uncertainties that are not captured by the model. To implement feedback in MPC, you need to design a feedback loop that connects the process output to the controller input, and to update the model predictions and optimization at each sampling time.

Add your perspective

Dhruvrajsinh Vansia

Instrumentation and Control Engineer | Ex-President, PRAKALPA LDCE | IBM’24 Intern | Robotics Honours Degree | AI/ML Enthusiast | LDCE25 | E-Cell IIT Bombay Ambassador | OpenCV | MediaPipe | YOLOv8 | Matplot | 🧠
Report contribution
Feedback is vital for Model Predictive Control (MPC) to ensure accurate performance. It adjusts control actions using real-time process measurements, correcting errors and reducing disturbances not covered by the model. Feedback connects the process output to the controller input and updates predictions and optimizations at every step. This keeps the system close to the desired setpoint, improving reliability and handling uncertainties effectively.

Like

2 Learning in MPC

Learning is another important aspect of MPC, which enables the controller to adapt to changes in the process dynamics, parameters, or operating conditions. Learning can improve the accuracy and reliability of the model, and thus enhance the performance and robustness of the control system. Learning can be done in different ways, such as online identification, adaptive control, or reinforcement learning. To incorporate learning in MPC, you need to design a learning algorithm that updates the model parameters or structure based on the feedback data, and to integrate the learning algorithm with the MPC controller.

Add your perspective

Dhruvrajsinh Vansia

Instrumentation and Control Engineer | Ex-President, PRAKALPA LDCE | IBM’24 Intern | Robotics Honours Degree | AI/ML Enthusiast | LDCE25 | E-Cell IIT Bombay Ambassador | OpenCV | MediaPipe | YOLOv8 | Matplot | 🧠
Report contribution
Learning helps MPC adapt to changes in process behavior, improving accuracy and control performance. It updates the model based on feedback to handle new conditions. Learning methods like online identification, adaptive control, or reinforcement learning can be used. To add learning to MPC, design an algorithm that adjusts model parameters using feedback and connect it to the MPC controller. This makes the system more reliable and robust over time.

Like

3 Online identification

Online identification is a method of learning that estimates the model parameters from the feedback data using recursive estimation techniques, such as least squares or Kalman filter. Online identification can handle linear or nonlinear models, and can track the variations of the model parameters over time. Online identification can improve the model fidelity and reduce the model mismatch, which can lead to better control performance and stability. To incorporate online identification in MPC, you need to define a suitable model structure and estimation algorithm, and to update the model parameters at each sampling time before solving the MPC optimization problem.

Add your perspective

4 Adaptive control

Adaptive control is a method of learning that adjusts the controller parameters or structure based on the feedback data using adaptation rules or laws, such as gradient descent or Lyapunov stability. Adaptive control can handle linear or nonlinear models, and can cope with uncertainties or disturbances that are unknown or time-varying. Adaptive control can improve the control performance and robustness, and can guarantee the stability and convergence of the closed-loop system. To incorporate adaptive control in MPC, you need to define a suitable controller structure and adaptation rule, and to update the controller parameters or structure at each sampling time after solving the MPC optimization problem.

Add your perspective

5 Reinforcement learning

Reinforcement learning is a method of learning that learns the optimal control policy from the feedback data using trial-and-error interactions with the process, and by maximizing a reward or minimizing a cost function. Reinforcement learning can handle nonlinear or stochastic models, and can deal with complex or constrained optimization problems that are difficult to solve analytically. Reinforcement learning can improve the control performance and efficiency, and can discover novel or optimal control strategies that are not obvious or intuitive. To incorporate reinforcement learning in MPC, you need to define a suitable reward or cost function and a learning algorithm, such as Q-learning or policy gradient, and to update the control policy at each sampling time based on the feedback data and the learning algorithm.

Add your perspective

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

Add your perspective

How do you incorporate feedback and learning in model predictive control?

1

2

3

4

5

6

1 Feedback in MPC

2 Learning in MPC

3 Online identification

4 Adaptive control

5 Reinforcement learning

6 Here’s what else to consider

Control Systems Design

Rate this article

Thanks for your feedback

More articles on Control Systems Design

More relevant reading