Member-only story

Recent Advances in Model Predictive Control: A Comprehensive Guide

Franklin Bell

·12.9k Followers· Follow

Published in Recent Advances In Model Predictive Control: Theory Algorithms And Applications (Lecture Notes In Control And Information Sciences 485)

4 min read · 3 weeks before

404 View Claps

25 Respond

Save

Listen

Model Predictive Control (MPC) is a powerful control technique that has gained widespread adoption in various industries, ranging from process control to industrial automation. MPC relies on a model of the system being controlled to predict future outputs and optimize the control inputs accordingly. This approach enables the design of controllers that can handle complex dynamics, constraints, and multi-variable systems effectively.

Recent Advances in Model Predictive Control: Theory Algorithms and Applications (Lecture Notes in Control and Information Sciences 485)

Recent Advances in Model Predictive Control: Theory, Algorithms, and Applications (Lecture Notes in Control and Information Sciences Book 485)

5 out of 5

Language	:	English
File size	:	52773 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Word Wise	:	Enabled
Print length	:	471 pages

In recent years, there have been significant advancements in MPC theory and applications. This guidebook aims to provide a comprehensive overview of these recent advances, empowering readers with the knowledge and tools to implement and optimize MPC systems. We will explore the fundamental principles of MPC, discuss state-of-the-art techniques, and delve into practical applications across various domains.

Fundamentals of Model Predictive Control

MPC is based on the concept of using a model of the system to predict future outputs over a finite horizon. The controller then calculates the optimal control inputs that minimize a cost function over the prediction horizon, subject to system constraints. This optimization problem is typically solved online at each sampling instant.

The key components of an MPC system include:

System Model: A mathematical representation of the system's dynamics, constraints, and inputs/outputs.
Prediction Horizon: The number of future time steps over which the output is predicted.
Cost Function: A mathematical expression that quantifies the desired system behavior and penalizes deviations from the desired trajectory.
Optimization Algorithm: A numerical method used to solve the optimization problem and determine the optimal control inputs.

Advanced MPC Techniques

Recent advancements in MPC have led to the development of several advanced techniques that enhance its performance and applicability. These techniques include:

Nonlinear MPC: Extends MPC to nonlinear systems, where the system model is represented by nonlinear equations.
Robust MPC: Designs controllers that are robust to uncertainties and disturbances in the system model and environment.
Stochastic MPC: Incorporates probabilistic models to handle systems with stochastic behavior and random disturbances.
Economic MPC: Optimizes the control inputs based on economic objectives, such as minimizing operating costs or maximizing profit.
Multi-Agent MPC: Coordinates control actions among multiple agents or subsystems in a distributed or cooperative control system.

Applications of MPC

MPC has found widespread applications in various industries, including:

Process Control: Optimizing chemical processes, oil refineries, and power plants.
Industrial Automation: Controlling robots, machine tools, and manufacturing processes.
Automotive: Designing advanced driver-assistance systems (ADAS) and autonomous vehicles.
Aerospace: Controlling aircraft and spacecraft.
Energy Management: Optimizing energy consumption in buildings and smart grids.

Model Predictive Control is a powerful and versatile control technique that has revolutionized the design and implementation of control systems. With the advancements discussed in this guidebook, MPC is now more accessible and applicable than ever before. By understanding the fundamentals and leveraging the latest techniques, engineers and practitioners can harness the full potential of MPC to optimize system performance, enhance efficiency, and drive innovation across a wide range of applications.

Applications Of Model Predictive Control In Various Industries Recent Advances In Model Predictive Control: Theory Algorithms And Applications (Lecture Notes In Control And Information Sciences 485)