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Model Predictive Control- What Is It & Its Vital Role?

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    Industries like power plants, pharmaceuticals, chemical plants, and water treatment facilities, and many others rely on many things and the model predictive control is one of them. This is an application that has been implemented successfully in most major industries.

    What is Model Predictive Control (MPC)?

    Model predictive control (MPC) is a sophisticated approach to control that can be very powerful when it comes to complex, time-varying applications. Although an MPC controller looks like other controllers on the surface, there are some key distinctions that set it apart from other types of controllers. (Credit Information: PiControl Solutions LLC)

    To understand why you need MPC, it helps to take a look at what's involved in the process of traditional closed-loop feedback control and how different aspects of this type of system affect your ability to meet certain requirements. MPC systems typically do not utilize closed-loop feedback.

    How Does Model Predictive Control Work?

    Model predictive control works by first taking measurements about the present state of the controlled apparatus or dynamic system using sensors, then using these measurements in conjunction with historical data and a model of the system to predict future states.

    MPC applications do not rely on measurements in real-time. So, they can use any type of measurement including those from sensors that are difficult to access or maybe too expensive to place in dangerous locations.

    Understanding The Model Predictive Control System

    MPC systems evolved out of an interest in using computer models to simulate process conditions and quantify relationships between process variables before attempting real-time applications.

    In fact, the term "model predictive controller" was coined by researchers at Argonne National Laboratory who were developing a discrete-event simulator for control experiments. The same techniques used for simulating chemical reactors were later adapted for use in controlling physical processes.

    MPC systems can also be utilized with non-dynamic processes. But they are most useful in applications where the model is dynamic, i.e., it can change or be adjusted over time. MPC systems are most frequently applied to systems with significant time delays.

    MPC architectures allow you to reduce your use of feedback by taking advantage of these delays and no longer requiring that measurements be fed back into the controller for "online" adjustment.  

    This application system works well when there is inherent uncertainty or variability in real-world conditions, such as changing environmental factors, levels of staffing, varying load requirements, etc.

    Model-based predictive control offers some other benefits as well. Model-based design tools allow you to experiment with different operational scenarios using simulation, gradually tuning up the performance until you achieve the desired results.

    It can also support automated condition monitoring and fault diagnosis, which allows you to identify when a controller can no longer perform its task due to an equipment malfunction or environmental factor such as changing load conditions, weather events, etc.

    Model predictive controllers are especially effective for applications where efficient use of energy is critical such as electrical grids, power plants, thermal plants, and other industrial processes requiring large amounts of power. 

    MPC systems have been incorporated into a variety of process control system architectures, including model reference adaptive systems (MRAS), advanced process control (APC), advanced measurement and control systems (AMCS), and high-performance process management system (HPM).

    However, MPC systems can be more challenging to implement than traditional control strategies, mainly due to the need for a detailed process model describing system dynamics. Model verification and validation are also required to ensure that the model accurately represents reality, particularly when there are significant time delays between measurements and controlled events.

    One important thing about the MPC systems is that they can be applied to both linear and non-linear systems. However, the level of sophistication required for implementation increases as non-linearities become more complex. Also, MPC’s do not work well when disturbances cannot be reliably modeled or predicted.

    Model predictive control should only be used after other approaches such as PID, and conventional feedback have been thoroughly tested and proved ineffective under all operating conditions.

    Why Are Model Predictive Controllers Popularly Used?

    Model-based design using process dynamics has become increasingly popular even though it requires additional planning and effort during project development. MPC’s are now routinely used in all types of process control systems because they increase performance, reduce energy use, and support automated condition monitoring.

    Model-based design often results in increased process yields that are highly valued by manufacturers using continuous processes. Model predictive controllers are frequently incorporated into larger control system architectures such as Model Reference Adaptive Systems (MRAS), which offer considerable flexibility for adapting to changing conditions.

    Where Are Model Predictive Controllers Used?

    Model predictive controls have been successfully applied to a wide variety of industries, including chemical, refining, power generation, pharmaceuticals, pulp and paper processing, mining transportation systems, water treatment facilities, and many others.

    MPC’s can be implemented with traditional software packages or special-purpose hardware. Platforms designed specifically for the task Model-based tools allow you to simulate dynamic processes, tuning system performance for a specific set of operating conditions prior to deployment.

    Model-based tools also support online monitoring and control and advanced diagnostics that help prevent unscheduled downtime and eliminate unnecessary costs during plant operations. 

    The Bottom Line

    Model Predictive Control (MPC) is an efficient and robust approach to improve process operation. By using mathematical models of the controlled process, forecasts are developed to warn about future behavior.

    MPC’s are now routinely used in all process control systems because they increase performance, reduce energy use, and support automated condition monitoring. Model-based tools also support online monitoring and offer considerable flexibility for adapting. That’s how crucial the MPC system is for your industry.