Why Use Model Predictive Control in Your Facility?

Model Predictive Control application comes with many benefits!

If you operate any manufacturing facility, it is crucial to consider using an advanced application system. You need the right process control system to improve efficiency and production.

Model Predictive Control is the best application you can use in your manufacturing facility. It can help by modeling the best possible future output from a system, given known inputs.

In many process operations, variables are constantly changing. MPC accounts for this by creating a process model to determine future states/outputs. A model is created using historical data, trend information, and other known variables affecting the system.

How Does Model Predictive Control Work?

Model predictive control is a technique that makes businesses more efficient. Most manufacturing industries use this application to increase production and profitability.

Modeling the process is the secret sauce in MPC; you can’t just use an equation because it won’t be accurate. MPC uses factors that affect the process to determine future outputs.

Modeling helps processes work more proficiently. It helps achieve goals while keeping an eye toward the future. This particular process takes advantage of historical data by modifying decisions based on past performance and incorporating new information. So, you don’t always have to guess what might happen.

MPC is particularly useful when optimizing any system where the variables are not constant. This application helps with biology, chemistry, medicine, petroleum recovery, electricity generation/transmission, and telecommunications.

Therefore, MPC is a good option for many systems because it would be challenging to create an equation that always results in the same output. The process accounts for future variables that cannot be factored into equations like differential calculus. This helps manage more complex operations and provides better results than ordinary predictive control (OPC).

The Popularity Of Model Predictive Control

Model predictive control has applications in everyday life and for large organizations. For daily use, it can help you manage your home heating and cooling systems by taking into account the weather.

For example, you can also use MPC to help optimize your personal finances or help control traffic lights in a city. It is helpful in many situations where it would be difficult to guess what might happen in the future accurately.

MPC cannot only capture these variables but can also change its behavior based on new information. It uses a process model coupled with the dynamic inputs from the plant to produce optimal results.

This makes it ideal for normal OPC processes to have difficulty determining output given dynamic conditions. NASA has used MPC to help control the orbit of satellites.

The best thing about MPC is that it can help multiple systems, whether on land, at sea, or in the air. It has so many applications; there are literally hundreds of possibilities! Because MPC gives a competitive advantage, it is wildly popular.

How To Use Model Predictive Control?

In the most general sense, Model Predictive Control (MPC) is a control strategy. The process uses a model of the system being controlled to generate an optimal control signal. The signal drives the system from its initial state to a desired final state.

MPC works by first predicting what the system will do over a predicted time. This prediction is then compared with our goal and from this information. MPC determines what you should do to generate the optimal result.

In effect, it fits an arrow into a windy path between start and finish that accounts for forces acting on us along the way. The process is iterative, so it starts by guessing where the system will be at some time in the future and comparing this guess with our goal. If they don’t match up, we try again.

This process continues until we predict that we will be exactly where we want to be. Remember, MPC does not take into account past error information and is therefore described as Model-Based.

The following eight steps outline the Model Predictive Control cycle.

Eight Steps Of Model Predictive Control Cycle In A Nutshell

1) Modeling Systems

The first step is to make a physical model of the system we want to control. You can make this with simple mathematics or by using off-the-shelf plant models designed for Model Predictive Control. The model must take into account all forces acting on the plant under command. These include but are not limited to friction, gravity, and inertia.

2) Modeling Control Reactions

A Model Predictive Control strategy only works if we know how the system responds to a unit step change in control input. The first time that MPC is run on a particular model, it takes this response into account and adds them into the model as what are called Reactions. A reaction represents the value of the application.

3) Modeling Controller Input

Once we know how the model reacts to changes in input, we need to translate this information into a form. The form should allow an MPC controller to keep on outputting those same values as commands at some time in the future.

4) Simulation & Measurement

Initially, we need to calibrate and test the MPC system using Simulated Model Identification (SMI). To carry out this process, you will need skilled operate.

5) Filter & Improve

When the initial Multivariable Linear Quadratic Regulator (MLQR) simulation has been completed, you must compare it with the original plant behavior. You should do this by subtracting one from the other. Putting all of these results into a graph or spreadsheet then drawing a line through the middle of them as seen in the MPC Identification to MPC Model Graph.

6) Model Predictive Control Model to Controller Mapping

There are many MPC mapping methods that only experienced operators know. We suggest you use highly experienced experts when running such applications. If anything goes wrong in the process, you won’t like the result.

7) Create Model Productive Control Model & Optimize It

Before starting this step, we need a Model Predictive Control Model. Most importantly, you should understand the concept well before implementing anything. Again, that’s why you need a highly-skilled professional to handle this process.

8) Model Control Model Implementation

MPC Model implementation is the final step. In this step, you need to make the development and control the process properly. That’s why you advanced technology system and a skilled professional to consult the final step.

To conclude this article, we would like to say that you should consider using the MPC system in your facility. Model predictive control is for long-term optimization of energy use through improved thermodynamic efficiency, reduced emissions, or increased product yield.

It is an ideal control method that can make the process relatively simple and benefit you in many ways. MPC also has the advantage of not requiring frequent interaction with the plant. In fact, in most cases, MPC allows modeling and control to occur completely off-line.

Hence, there is no need to interact with the system at all until it comes time to apply the command that will bring us to our goal. It’s time you implement model predictive control in your facility if you still haven’t.

Do you want more information about MPC applications? Browse the website Also, if you are considering using MPC, Pi Control Solutions welcomes you to collaborate with us.

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