Hybrid L1 Adaptive Control: Applications of Fuzzy Modeling, Stochastic Optimization and Metaheuristics

Maiti, Roshni/ Sharma, Kaushik Das/ Sarkar, Gautam

ISBN 10: 303097104X / ISBN 13: 9783030971045

Edité par Springer Nature, 2023

A propos de cet article

276 pages. 9.25x6.10x0.58 inches. In Stock.

N° de réf. du vendeur x-303097104X

Détails bibliographiques

• Titre: Hybrid L1 Adaptive Control: Applications of Fuzzy Modeling, Stochastic Optimization and Metaheuristics
• Éditeur: Springer Nature
• Date de parution: 2023
• Langue: anglais
• ISBN à 10 chiffres: 303097104X
• ISBN à 13 chiffres: 9783030971045
• Reliure: Paperback
• État de l'article: Brand New

A propos de ce titre

Synopsis

This book details the designing of hybrid control strategies for practical systems containing time varying uncertainties, disturbances, nonlinearities, unknown parameters, unmodelled dynamics, delays, etc., concurrently. In this book, the advantages of different controllers will be brought together to produce superior control performance for the practical systems. Being aware of the advantages of adaptive controller to tackle unknown constant, time varying uncertainties and time varying disturbances, a variant of adaptive controller, namely L₁ adaptive controller, is hybridized with other strategies.

In this book, to facilitate optimal parameter setting of the basic L₁ adaptive controller, stochastic optimization technique will be hybridized with it. The stability of the optimization technique along with the controller will be guaranteed analytically with the help of spectral radius convergence. The proposed method exhibits satisfactory exploration and exploitation capabilities.

Again, this book will throw light on tackling nonlinearities along with uncertainties and disturbances by hybridizing fuzzy logic with L₁ adaptive controller. The performances of the designed controllers will be compared with different control methodologies to validate their effectiveness. The overall stability of the nonlinear system with the designed controller will be guaranteed with the help of fuzzy Lyapunov function to retain the zonal behaviour of the system. This fuzzy PDC-L₁ adaptive controller is efficient to tackle nonlinearities and at the same time cancels unknown constant, time varying uncertainties and time varying disturbances adequately.

This book will also contain four simulation case studies to validate fruitfulness of the designed controllers. To demonstrate the superior control ability of these controllers in tackling practical system, three experimental case studies will also be provided.