Model Predictive Control Made Easy
A clear, intuitive, and practical introduction to one of the most powerful control techniques in modern engineering.
Model Predictive Control (MPC) is everywhere-running chemical plants, stabilizing drones, optimizing HVAC systems, steering autonomous cars, and shaping the future of robotics and energy systems. Yet for many engineers, MPC still feels intimidating: too much math, too many assumptions, too many black-box solvers.
This book fixes that.
Model Predictive Control Made Easy takes you from intuition to implementation with clarity rarely found in technical literature. Instead of drowning you in equations, it builds understanding step by step-starting with how you drive a car, and ending with how to design real-time controllers that respect constraints, anticipate the future, and remain robust in the face of uncertainty.
You'll learn:
Why MPC works through simple, memorable analogiesHow to build prediction models using state-space systemsHow horizons, costs, and constraints shape controller behaviorHow to tune Q, R, and terminal costs without guessworkHow to handle actuator limits, rate limits, and safety constraintsHow to design offset-free MPC using disturbance modelsHow robust MPC and constraint tightening keep real systems safeWhy MPC reduces to LQR when constraints disappear-and why that mattersEvery chapter is written to be read by real engineers, not mathematicians. The explanations are crisp. The examples are practical. The insights come from real-world experience, not abstract theory.
Whether you're working in robotics, automotive systems, process control, aerospace, or embedded systems, this book gives you the mental model and practical tools to design MPC controllers with confidence.
If you've ever wanted MPC explained simply, clearly, and correctly-this is the book.
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