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001442316 001__ 1442316
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001442316 019__ $$a1272995369$$a1287777431
001442316 020__ $$a9783030838157$$q(electronic bk.)
001442316 020__ $$a3030838153$$q(electronic bk.)
001442316 020__ $$z9783030838140
001442316 020__ $$z3030838145
001442316 0247_ $$a10.1007/978-3-030-83815-7$$2doi
001442316 035__ $$aSP(OCoLC)1269055196
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001442316 049__ $$aISEA
001442316 050_4 $$aTJ217.6$$b.L38 2022
001442316 08204 $$a629.8$$223
001442316 1001_ $$aŁawryńczuk, Maciej,$$eauthor.
001442316 24510 $$aNonlinear predictive control using Wiener models :$$bcomputationally efficient approaches for polynomial and neural structures /$$cMaciej Ławryńczuk.
001442316 264_1 $$aCham :$$bSpringer,$$c[2022]
001442316 264_4 $$c©2022
001442316 300__ $$a1 online resource :$$billustrations (chiefly color)
001442316 336__ $$atext$$btxt$$2rdacontent
001442316 337__ $$acomputer$$bc$$2rdamedia
001442316 338__ $$aonline resource$$bcr$$2rdacarrier
001442316 347__ $$atext file
001442316 347__ $$bPDF
001442316 4901_ $$aStudies in systems, decision and control,$$x2198-4190 ;$$vvolume 389
001442316 504__ $$aIncludes bibliographical references and index.
001442316 5050_ $$aIntroduction to Model Predictive Control -- MPC Algorithms Using Input-Output Wiener Models -- MPC Algorithms Using State-Space Wiener Models -- Conclusions -- Index.
001442316 506__ $$aAccess limited to authorized users.
001442316 520__ $$aThis book presents computationally efficient MPC solutions. The classical model predictive control (MPC) approach to control dynamical systems described by the Wiener model uses an inverse static block to cancel the influence of process nonlinearity. Unfortunately, the model's structure is limited, and it gives poor control quality in the case of an imperfect model and disturbances. An alternative is to use the computationally demanding MPC scheme with on-line nonlinear optimisation repeated at each sampling instant. A linear approximation of the Wiener model or the predicted trajectory is found on-line. As a result, quadratic optimisation tasks are obtained. Furthermore, parameterisation using Laguerre functions is possible to reduce the number of decision variables. Simulation results for ten benchmark processes show that the discussed MPC algorithms lead to excellent control quality. For a neutralisation reactor and a fuel cell, essential advantages of neural Wiener models are demonstrated.
001442316 650_0 $$aPredictive control.
001442316 650_6 $$aCommande prédictive.
001442316 655_0 $$aElectronic books.
001442316 77608 $$iPrint version:$$z3030838145$$z9783030838140$$w(OCoLC)1259585040
001442316 830_0 $$aStudies in systems, decision and control ;$$vv. 389.$$x2198-4190
001442316 852__ $$bebk
001442316 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-83815-7$$zOnline Access$$91397441.1
001442316 909CO $$ooai:library.usi.edu:1442316$$pGLOBAL_SET
001442316 980__ $$aBIB
001442316 980__ $$aEBOOK
001442316 982__ $$aEbook
001442316 983__ $$aOnline
001442316 994__ $$a92$$bISE