Signal processing algorithms
The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric. Find more information on the Altmetric Attention Score and how the score is calculated. It is believed that a fixed-parameter proportional-integral derivative PID may not do well for nonlinear, time-variant, or coupled processes.
It needs to be re-tuned adequately to retain robust control performance over a wide range of operating conditions.
Alternatively, nonlinear control algorithms can be employed. To avoid complexity introduced by such nonlinear controllers, modified PID algorithms that have the ability to adapt their tuning parameters on-line can be used instead to perform as well. An automatic on-line tuning strategy for PI controllers is proposed and compared with other existing adaptive PI algorithms such as fuzzy gain scheduling, model-based gain scheduling, a nonlinear version of PI, internal model control, and self-tuning adaptive control.
The proposed tuning methodology adapts the PI settings by direct utilization of explicit expressions for the gradients of the closed-loop response with respect to the PI settings. The adapted parameters are determined such that the resulting closed-loop response lies inside predefined time-domain constraints.
Application of the proposed technique as well as the other aforementioned systems to two nonlinear simulated continuously stirred tank reactor examples is demonstrated.
These examples present challenging control problems because of their interesting dynamics such as time-varying gain and gain with changing sign character. Simulation results indicated that the proposed tuning algorithm can provide comparable, if not superior, performance to those obtained by the other tested algorithms. View Author Information. Box , Riyadh , Saudi Arabia. Cite this: Ind. Article Views Departing, in part, from the Lyapunov-function approach of classical control, new algorithms are delivered for the construction of robust asymptotically-stabilising and adaptive control laws for nonlinear systems.
The methods proposed lead to modular schemes. These algorithms cater for nonlinear systems with both parametric and dynamic uncertainties. This innovative strategy is illustrated with several examples and case studies from real applications. Power converters, electrical machines, mechanical systems, autonomous aircraft and computer vision are among the practical systems dealt with.
Researchers working on adaptive and nonlinear control theory or on control applications will find this monograph of conspicuous interest while graduate students in control systems and control engineers working with electrical, mechanical or electromechanical systems can also gain much insight and assistance from the methods and algorithms detailed. He was a Senior Lecturer and subsequently Reader at the same institution until when he was appointed Professor in Nonlinear Control Theory.
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Professor Astolfi serves regularly on the program committees of control-related conferences and is an Associate Editor of the following journals: IEEE Trans. He examines for Ph. He also holds together with the other authors a patent for a power factor precompensator.
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Since he has supervised 6 Ph. He then joined the National University of Mexico, where he worked until His research interests are in the fields of nonlinear and adaptive control with special emphasis on applications.
Nonlinear And Adaptive Control: Tools and Algorithms for the User
He is a co-author of the Springer-Verlag book Passivity-based control of Euler—Lagrange Systems , and has published more than scientific papers in international journals. He has served as Associate Editor for various scientific journals.