LEADER 04310nam 22006255 450 001 9911002545103321 005 20250511130329.0 010 $a3-031-78003-5 024 7 $a10.1007/978-3-031-78003-5 035 $a(CKB)38776012800041 035 $a(DE-He213)978-3-031-78003-5 035 $a(MiAaPQ)EBC32107891 035 $a(Au-PeEL)EBL32107891 035 $a(OCoLC)1524425561 035 $a(EXLCZ)9938776012800041 100 $a20250511d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aData-Driven, Nonparametric, Adaptive Control Theory /$fby Andrew J. Kurdila, Andrea L'Afflitto, John A. Burns 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (XV, 331 p. 85 illus., 64 illus. in color.) 225 1 $aLecture Notes in Control and Information Sciences,$x1610-7411 ;$v495 311 08$a3-031-78002-7 327 $aChapter 1. Introduction -- Chapter 2. Elements of Real and Functional Analysis -- Chapter 3. Elements of Native Space Theory -- Chapter 4. Elements of Dynamical Systems Theory -- Chapter 5. Native Space Embedding Control Methods -- Chapter 6. Data-Driven Methods and Adaptive Control: Deterministic Analysis -- Chapter 7. Data-Driven Methods and Adaptive Control: Stochastic Analysis -- Chapter 8. Conclusion -- Appendix. 330 $aData-Driven, Nonparametric, Adaptive Control Theory introduces a novel approach to the control of deterministic, nonlinear ordinary differential equations affected by uncertainties. The methods proposed enforce satisfactory trajectory tracking despite functional uncertainties in the plant model. The book employs the properties of reproducing kernel Hilbert (native) spaces to characterize both the functional space of uncertainties and the controller's performance. Classical control systems are extended to broader classes of problems and more informative characterizations of the controllers? performances are attained. Following an examination of how backstepping control and robust control Lyapunov functions can be ported to the native setting, numerous extensions of the model reference adaptive control framework are considered. The authors? approach breaks away from classical paradigms in which uncertain nonlinearities are parameterized using a regressor vector provided a priori or reconstructed online. The problem of distributing the kernel functions that characterize the native space is addressed at length by employing data-driven methods in deterministic and stochastic settings. The first part of this book is a self-contained resource, systematically presenting elements of real analysis, functional analysis, and native space theory. The second part is an exposition of the theory of nonparametric control systems design. The text may be used as a self-study book for researchers and practitioners and as a reference for graduate courses in advanced control systems design. MATLABŪ codes, available on the authors? website, and suggestions for homework assignments help readers appreciate the implementation of the theoretical results. 410 0$aLecture Notes in Control and Information Sciences,$x1610-7411 ;$v495 606 $aSystem theory 606 $aControl theory 606 $aAutomatic control 606 $aFunctional analysis 606 $aSystems Theory, Control 606 $aControl and Systems Theory 606 $aFunctional Analysis 615 0$aSystem theory. 615 0$aControl theory. 615 0$aAutomatic control. 615 0$aFunctional analysis. 615 14$aSystems Theory, Control. 615 24$aControl and Systems Theory. 615 24$aFunctional Analysis. 676 $a003 700 $aKurdila$b Andrew$4aut$4http://id.loc.gov/vocabulary/relators/aut$01713820 702 $aL'Afflitto$b Andrea$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aBurns$b John A$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911002545103321 996 $aData-Driven, Nonparametric, Adaptive Control Theory$94385215 997 $aUNINA