Nonlinear Predictive Control Using Wiener Models: Computationally Efficient Approaches for Polynomial and Neural Structures

This book presents an efficient MPC solution for controlling dynamical systems with Wiener models, addressing limitations in traditional MPC and offering improved control quality with online nonlinear optimization and parameterization using Laguerre functions.

Format: Hardback
Length: 343 pages
Publication date: 22 September 2021
Publisher: Springer Nature Switzerland AG

This book delves into the realm of computationally efficient model predictive control (MPC) solutions, offering a novel approach to controlling dynamical systems modeled by the Wiener model. While the traditional MPC method employs an inverse static block to counteract the effects of process nonlinearity, it possesses certain limitations. Firstly, the model's structure is constrained, leading to suboptimal control performance when dealing with imperfect models and external disturbances. To address this challenge, an alternative solution is proposed: the computationally demanding MPC scheme, which involves online nonlinear optimization repeated at each sampling instant.

Within this framework, a linear approximation of the Wiener model or the predicted trajectory is derived online. This approximation simplifies the optimization tasks into quadratic forms, enabling the use of efficient optimization algorithms. Furthermore, parameterization using Laguerre functions is explored, allowing for the reduction of the number of decision variables, thereby enhancing the computational efficiency of the MPC solution.

To substantiate the effectiveness of the proposed MPC algorithms, comprehensive simulation results are presented for ten benchmark processes. These results demonstrate the remarkable control quality achieved by the discussed MPC algorithms, highlighting their potential for various industrial applications.

In particular, the book highlights the advantages of neural Wiener models in neutralization reactors and fuel cells, showcasing their significance in improving control performance and achieving optimal operating conditions.

Overall, this book provides a valuable resource for researchers, engineers, and practitioners seeking to enhance the control of dynamical systems using computationally efficient MPC solutions, particularly in the context of complex and challenging processes.

Weight: 717g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030838140
Edition number: 1st ed. 2022