02505nam 2200589 a 450 991078088600332120230415195143.01-282-76353-99786612763533981-4295-62-0(CKB)2490000000001924(EBL)731291(OCoLC)681579667(SSID)ssj0000412879(PQKBManifestationID)12144910(PQKBTitleCode)TC0000412879(PQKBWorkID)10368749(PQKB)10637825(MiAaPQ)EBC731291(WSP)00007643(Au-PeEL)EBL731291(CaPaEBR)ebr10422414(CaONFJC)MIL276353(EXLCZ)99249000000000192420100819d2010 uy 0engur|n|---|||||txtccrBiomedical engineering entrepreneurship[electronic resource] /Jen-Shih LeeSingapore ;Hackensack, N.J. World Scientificc20101 online resource (500 p.)Description based upon print version of record.981-4295-60-4 Includes bibliographical references and index.Preface; Acknowledgements; Contents; SECTION I: INTRODUCTORY CHAPTER; SECTION II: ASSESSING THE VENTURE; SECTION III: LAUNCHING THE VENTURE; SECTION IV: BUILDING UP THE ENTERPRISE; SECTION V: CONCLUDING CHAPTERS; IndexThis book is written for undergraduate and graduate students in biomedical engineering wanting to learn how to pursue a career in building up their entrepreneur ventures. Practicing engineers wanting to apply their innovations for healthcare will also find this book useful. The 21st century is the Biotech Century where many nations are investing heavily in biotechnology. As a result, tremendous business opportunities exist for biomedical engineering graduates who are interested in becoming successful entrepreneurs. However, many challenges await these entrepreneurs intending to invent safe andBiomedical engineeringEntrepreneurshipBiomedical engineering.Entrepreneurship.610.2885.00bclLee Jen-shih1573770MiAaPQMiAaPQMiAaPQBOOK9910780886003321Biomedical engineering entrepreneurship3849658UNINA05366nam 22006855 450 991048384120332120200701033024.01-4471-7457-710.1007/978-1-4471-7457-8(CKB)4100000011254646(MiAaPQ)EBC6202747(DE-He213)978-1-4471-7457-8(PPN)248392786(EXLCZ)99410000001125464620200519d2020 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierNonlinear Industrial Control Systems Optimal Polynomial Systems and State-Space Approach /by Michael J. Grimble, Paweł Majecki1st ed. 2020.London :Springer London :Imprint: Springer,2020.1 online resource (778 pages)1-4471-7455-0 Includes bibliographical references and index.Part I: Background -- Introduction to Nonlinear Systems -- Nonlinear Systems Modelling and Identification -- Part II: Polynomial Systems -- Introduction to Nonlinear Generalized Minimum Variance Control -- Nonlinear Generalized Minimum Variance Control Design Issues -- Introduction to Factorised NGMV Nonlinear Controls -- H-infinity Robust Control for Nonlinear Systems -- Design Procedures in the Presence of Saturation and Other Nonlinearities -- Part III: State-space Systems -- Space Approach to NGMV Control -- Design Issues and NGMV Predictive Control -- Basic and Factorised NGMV Control of Continuous-time Systems -- Part IV: Nonlinear System Benchmarking Nonlinear Controls -- Dual Nonlinear Estimation Problems -- Neural Networks, Fuzzy Control and Learning -- Part V: Industrial Applications -- Nonlinear Industrial Process Control Applications -- Nonlinear Automotive, Aerospace and Marine Applications.Nonlinear Industrial Control Systems presents a range of mostly optimisation-based methods for severely nonlinear systems; it discusses feedforward and feedback control and tracking control systems design. The plant models and design algorithms are provided in a MATLAB® toolbox (downloadable from www.springer.com/978-1-4471-7455-4) that enable both academic examples and industrial application studies to be repeated and evaluated, taking into account practical application and implementation problems. The text makes nonlinear control theory accessible to readers having only a background in linear systems, and concentrates on real applications of nonlinear control. It covers: different ways of modelling nonlinear systems including state space, polynomial-based, linear parameter varying, state-dependent and hybrid; design techniques for nonlinear optimal control including generalised-minimum-variance, model predictive control, quadratic-Gaussian, factorised and H∞ design methods; design philosophies that are suitable for aerospace, automotive, marine, process-control, energy systems, robotics, servo systems and manufacturing; steps in design procedures that are illustrated in design studies to define cost-functions and cope with problems such as disturbance rejection, uncertainties and integral wind-up; and baseline non-optimal control techniques such as nonlinear Smith predictors, feedback linearization, sliding mode control and nonlinear PID. Nonlinear Industrial Control Systems is valuable to engineers in industry dealing with actual nonlinear systems. It provides students with a comprehensive range of techniques and examples for solving real nonlinear control design problems.Automatic controlIndustrial engineeringProduction engineeringAutomotive engineeringChemical engineeringCalculus of variationsControl and Systems Theoryhttps://scigraph.springernature.com/ontologies/product-market-codes/T19010Industrial and Production Engineeringhttps://scigraph.springernature.com/ontologies/product-market-codes/T22008Automotive Engineeringhttps://scigraph.springernature.com/ontologies/product-market-codes/T17047Industrial Chemistry/Chemical Engineeringhttps://scigraph.springernature.com/ontologies/product-market-codes/C27000Calculus of Variations and Optimal Control; Optimizationhttps://scigraph.springernature.com/ontologies/product-market-codes/M26016Automatic control.Industrial engineering.Production engineering.Automotive engineering.Chemical engineering.Calculus of variations.Control and Systems Theory.Industrial and Production Engineering.Automotive Engineering.Industrial Chemistry/Chemical Engineering.Calculus of Variations and Optimal Control; Optimization.629.8312Grimble Michael Jauthttp://id.loc.gov/vocabulary/relators/aut491140Majecki Pawełauthttp://id.loc.gov/vocabulary/relators/autMiAaPQMiAaPQMiAaPQBOOK9910483841203321Nonlinear Industrial Control Systems2186273UNINA