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William Krylov
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Predictive Control With Constraints: A Comprehensive Textbook by Jan Maciejowski

Predictive Control With Constraints: A Comprehensive Textbook by Jan Maciejowski

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 multiple inputs and outputs, nonlinear dynamics, time delays, and uncertainties. Predictive control can also deal with constraints on the system variables, such as physical limitations, safety requirements, or environmental regulations.

One of the most authoritative and comprehensive textbooks on predictive control is Predictive Control With Constraints by Jan Maciejowski, a professor of engineering at the University of Cambridge. The book was published in 2002 by Pearson Education under the Prentice Hall imprint. The book covers the theory and practice of predictive control, with a focus on constrained predictive control, which reflects the true use of the topic in industry.

Predictive Control With Constraints Maciejowski Pdf Download

The book is suitable for senior undergraduate and graduate students, as well as practicing engineers who want to learn more about predictive control. The book provides a systematic and rigorous course on predictive control, with many examples, exercises, and mini-tutorials on specialist topics. The book also includes software for implementing predictive control algorithms and solving optimization problems.

The book has 10 chapters, covering the following topics:

  • Introduction to predictive control

  • Linear time-invariant systems

  • Optimal control

  • Constrained optimal control

  • Model predictive control

  • Stability and robustness

  • Nonlinear systems

  • Hybrid systems

  • Implementation issues

  • Applications and case studies

The book can be downloaded as a pdf file from various online sources, such as Google Books[^1^], University of Cambridge[^2^], or Vdoc[^3^]. However, readers are encouraged to purchase the book from Pearson Education or other authorized sellers to support the author and publisher.

Predictive control offers several important benefits for various applications, such as industrial processes, power systems, robotics, and automotive systems. Some of these benefits are:

  • Predictive control can capture the dynamic and static interactions between input, output, and disturbance variables, using a process model that reflects the physical behavior of the system. This can improve the accuracy and robustness of the control performance.

  • Predictive control can consider constraints on inputs and outputs in a systematic manner, using optimization techniques that minimize a cost function subject to linear or nonlinear inequalities. This can ensure the feasibility and safety of the control actions.

  • Predictive control can coordinate the control calculations with the calculation of optimum set points, using a multi-objective optimization framework that balances different performance criteria. This can enhance the efficiency and quality of the system operation.

  • Predictive control can provide early warnings of potential problems, using accurate model predictions that anticipate future events and scenarios. This can enable proactive and preventive actions to avoid or mitigate undesired outcomes.

Predictive control is a powerful and versatile control method that can handle complex and nonlinear systems with multiple objectives and constraints. However, predictive control also has some challenges and limitations, such as:

  • Predictive control requires a reliable and accurate model of the system, which may be difficult or costly to obtain or update. Model errors or uncertainties can degrade the control performance or cause instability.

  • Predictive control requires a high computational power and a fast sampling rate, which may be challenging for some applications or platforms. The optimization problem may be nonlinear, nonconvex, or large-scale, which can increase the computational complexity and time.

  • Predictive control requires a careful tuning of the parameters and weights in the cost function and the constraints, which may be subjective or dependent on the operating conditions. The choice of these parameters can affect the trade-off between performance and robustness.

Therefore, predictive control is a promising but challenging control method that requires a good understanding of the system dynamics, the optimization techniques, and the performance objectives. Predictive control can offer significant advantages over traditional control methods such as PID, but it also requires more advanced tools and skills to implement and operate. 0efd9a6b88


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