Model Predictive Control for Engineers: A Practical Guide to Design, Tuning, and Real-Time Implementation (Paperback) (ISBN-13: 9798188288433)

Every 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.

Classical 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.

This 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.

• Understand precisely why model-based, anticipatory control succeeds where reactive feedback struggles with delay, multivariable interaction, and hard operating constraints.
• Build the discrete-time process models a predictive controller depends on, and learn to judge whether a given model is trustworthy enough to design from.
• Assemble a complete optimization-based control problem and apply a systematic, evidence-based tuning procedure in place of trial-and-error adjustment.
• Extend a working linear design to nonlinear dynamics, uncertain models, economically optimized objectives, and coordinated multi-controller architectures.
• Recognize when a precomputed control law can replace an online optimization for resource-constrained hardware, and verify it reproduces the same result.
• Follow 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.
• Use five in-depth appendices covering the mathematical prerequisites as a self-contained reference whenever a gap in background appears.

Discrete-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.

Practicing 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.

Begin 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.