{"product_id":"model-predictive-control-for-engineers-e-marcus-braxton-9798188288433","title":"Model Predictive Control for Engineers: A Practical Guide to Design, Tuning, and Real-Time Implementation","description":"\u003cp\u003eEvery engineer eventually meets a control problem that reactive feedback cannot solve alone-interacting loops fighting each other, a delayed response arriving after the correction meant for it, or a hard operating limit that must be respected exactly rather than approached with a cautious margin.\u003c\/p\u003e\u003cp\u003eClassical feedback control reacts only to the error it currently observes; it has no internal model of the process and no way to anticipate what a control move will do several steps ahead. Predictive control answers this with one structural change-placing a model of the process inside the controller-but the gap between understanding that idea and actually designing, tuning, and deploying a working controller is where most engineering references fall short, staying too abstract to build from or asking the reader to trust results that were never actually verified.\u003c\/p\u003e\u003cp\u003eThis comprehensive, sixteen-chapter guide closes that gap, developing predictive control as one continuous, verified discipline-from the discrete-time models a controller predicts from, through the optimization machinery that makes constrained control tractable, to a controller running safely on real industrial equipment-with every equation and worked example checked directly against independent computation.\u003c\/p\u003e\u003cb\u003eWhat You'll Gain\u003c\/b\u003e\u003cul\u003e\n\u003cli\u003eUnderstand precisely why model-based, anticipatory control succeeds where reactive feedback struggles with delay, multivariable interaction, and hard operating constraints.\u003c\/li\u003e\n\u003cli\u003eBuild the discrete-time process models a predictive controller depends on, and learn to judge whether a given model is trustworthy enough to design from.\u003c\/li\u003e\n\u003cli\u003eAssemble a complete optimization-based control problem and apply a systematic, evidence-based tuning procedure in place of trial-and-error adjustment.\u003c\/li\u003e\n\u003cli\u003eExtend a working linear design to nonlinear dynamics, uncertain models, economically optimized objectives, and coordinated multi-controller architectures.\u003c\/li\u003e\n\u003cli\u003eRecognize when a precomputed control law can replace an online optimization for resource-constrained hardware, and verify it reproduces the same result.\u003c\/li\u003e\n\u003cli\u003eFollow a controller through commissioning, validation, independent safety integration, and long-term maintenance, illustrated with four worked deployment case studies spanning chemical processing, robotics, transportation, and building systems.\u003c\/li\u003e\n\u003cli\u003eUse five in-depth appendices covering the mathematical prerequisites as a self-contained reference whenever a gap in background appears.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cb\u003eKey Topics Covered\u003c\/b\u003e\u003cp\u003eDiscrete-time system modeling; convex optimization foundations; the full linear predictive-control formulation, unconstrained and constrained; provable closed-loop stability; systematic tuning; state estimation and offset-free tracking; nonlinear predictive control and its real-time solution methods; robust and stochastic treatments of model uncertainty; economically optimized objectives; precomputed control laws; distributed and hierarchical multi-controller coordination; and industrial deployment, safety integration, and lifecycle maintenance.\u003c\/p\u003e\u003cb\u003eWho This Book Is For\u003c\/b\u003e\u003cp\u003ePracticing engineers across process, automation, robotics, and automotive control roles who need to move beyond reactive feedback control, and graduate-level engineering students building a rigorous, verified foundation in predictive control. Readers should be comfortable with linear algebra, ordinary differential equations, and classical feedback control; no prior background in optimization theory or predictive control specifically is assumed.\u003c\/p\u003e\u003cp\u003eBegin building a clearer, verified understanding of predictive control with a guide designed to be consulted throughout an entire engineering career-get your copy and start from first principles through real-world deployment.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e E. Marcus Braxton,Joshua K. Waldo\u003cbr\u003e\u003cb\u003eISBN-13:\u003c\/b\u003e 9798188288433\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Independently Published\u003cbr\u003e\u003cb\u003eLanguage:\u003c\/b\u003e English\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 07\/21\/2026\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 500\u003cbr\u003e\u003cb\u003eFormat:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 2.53lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 11.00h x 8.50w x 1.01d","brand":"E. Marcus Braxton","offers":[{"title":"Paperback","offer_id":50665942024447,"sku":"9798188288433","price":120.0,"currency_code":"USD","in_stock":true}],"url":"https:\/\/www.whiterainbookhouse.com\/products\/model-predictive-control-for-engineers-e-marcus-braxton-9798188288433","provider":"WR Book House","version":"1.0","type":"link"}