How Can Integral Feedback Improve Model Predictive Control in Boiler Systems?

In summary, the individual is facing difficulty in handling constant disturbance in their application of model predictive control to the ASTROM & BELL BOILER model. Despite trying to overcome this issue with integral + MPC and feedforward + MPC, the output drum level is still not regulating back to the reference value of zero. They are seeking help with coding or any other solutions, and also welcome suggestions for relevant papers on this topic. They also mention the importance of a feedback component in achieving zero steady state error.
  • #1
syed334
1
0
Hi All,

I am applying model predictive control to the ASTROM & BELL BOILER model, I am facing difficulty in handling constant disturbance, I am unbale to reject constant input disturbance, due to which my output drum level is having steady state error, I have applied INtegral + MPC to over come STEADY STATE ERROR, but still DRUM LEVEL is not regulating back to zero
( reference value) , then I have applied feedforward + Mpc, still iam not able to track my output, can anyone help me in solving this problem, by FEEDFORWARD+MPC coding or any other way.
U can suggest any paper referring this as well.

Thanking You,
Syed
 
Last edited:
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  • #2
Where is the feedback part of the control? If you want zero stead state error, then there must be some integral feedback component.
 

Related to How Can Integral Feedback Improve Model Predictive Control in Boiler Systems?

What is model predictive control?

Model predictive control (MPC) is a control method used in engineering and industrial processes to control a system by using a mathematical model to predict its future behavior. It is a type of advanced control that takes into account the current state of the system, its predicted future behavior, and a defined set of constraints to calculate the optimal control action.

How does model predictive control work?

MPC works by using a mathematical model of the system, which includes its dynamics, constraints, and objectives. The model is continuously updated with new measurements from the system, and the MPC controller uses this model to predict the future behavior of the system. Based on this prediction, the controller calculates the optimal control action that will minimize a predefined cost function while satisfying the system constraints.

What are the advantages of using model predictive control?

Some advantages of model predictive control include its ability to handle constraints and disturbances, its ability to handle systems with multiple inputs and outputs, and its ability to optimize the control action over a finite time horizon. Additionally, MPC does not require a precise mathematical model of the system and can adapt to changes in the system's behavior.

What are the limitations of model predictive control?

One of the main limitations of model predictive control is its computational complexity, which can be a challenge for real-time implementation in some applications. Additionally, MPC relies on an accurate mathematical model of the system, so any uncertainties or errors in the model can affect its performance. It also requires frequent updates of the model, which can be a challenge in systems with fast dynamics.

What are some applications of model predictive control?

Model predictive control is commonly used in industrial processes such as chemical plants, power plants, and manufacturing systems. It is also used in transportation systems, such as autonomous vehicles, and in building energy management systems. MPC has also been applied in biomedical systems, aerospace systems, and many other fields.

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