Predictive Control With Constraints Maciejowski Pdf Download PORTABLE
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Predictive Control with Constraints: A Comprehensive Course on Model Predictive Control
Predictive control is a branch of control engineering that uses the future behavior of a system to optimize its current performance. Predictive control can handle complex systems with constraints, uncertainties, and nonlinearities, and is widely used in industrial applications such as chemical plants, power systems, robotics, and automotive engineering.
One of the most popular methods of predictive control is model predictive control (MPC), which uses a mathematical model of the system to predict its future states and outputs, and then solves an optimization problem to find the best control inputs that minimize a cost function while satisfying the constraints. MPC can improve the efficiency, safety, and robustness of a system, as well as reduce its environmental impact.
In this book, Jan Maciejowski provides a systematic and comprehensive course on predictive control suitable for senior undergraduate and graduate students and professional engineers. The book covers both the theory and practice of MPC, with emphasis on constrained predictive control, which reflects the true use of the topic in industry. The book also includes software, solutions manual, corrections, and predictive control links.
The book is divided into three parts: Part I introduces the basic concepts and principles of predictive control; Part II presents the methods and algorithms for constrained predictive control; and Part III discusses some advanced topics and applications of MPC. The book contains many examples, exercises, mini-tutorials, and case studies to illustrate the concepts and techniques of MPC.
The book is based on the author's extensive experience in teaching and research on predictive control. The author is a lecturer in the Department of Engineering at the University of Cambridge and an internationally recognized expert in the field. The book is published by Pearson Education under the Prentice Hall imprint.
If you are interested in learning more about predictive control with constraints, you can download a PDF version of the book from here [^3^], or visit the web-site [^2^] for the book for more information.In this section, we will continue the article by discussing some of the advantages and disadvantages of MPC.
Advantages and disadvantages of MPC
MPC has many advantages over other control methods, such as:
It can handle multivariable systems with complex interactions and couplings.
It can explicitly account for constraints on inputs, outputs, states, and disturbances.
It can optimize the performance of the system over a finite or infinite horizon.
It can incorporate feedback and feedforward information to improve the robustness and adaptability of the system.
It can deal with nonlinearities, uncertainties, and time-varying parameters by using online or offline model updates.
It can be easily implemented using standard optimization software and hardware.
However, MPC also has some disadvantages and challenges, such as:
It requires a reliable and accurate model of the system, which may be difficult or costly to obtain or validate.
It requires solving an optimization problem at each sampling time, which may be computationally demanding or infeasible for fast or large-scale systems.
It may suffer from stability and feasibility issues if the optimization problem is ill-posed or infeasible.
It may be sensitive to measurement noise, modeling errors, or disturbances if the model is not sufficiently accurate or robust.
It may require tuning of several parameters, such as the prediction horizon, the control horizon, the cost function weights, and the terminal constraints or costs.
Therefore, MPC requires careful design and implementation to ensure its effectiveness and reliability. The book provides several methods and techniques to address these issues and enhance the performance of MPC. ec8f644aee