Made Easy Series

Kalman Filters Made Easy is the engineer’s companion for understanding uncertainty — not as an abstract mathematical idea, but as a concrete, unavoidable part of every real‑world system. Drawing from practical examples across robotics, drones, autonomous vehicles, and sensor‑driven devices, this book explains why measurements drift, why models fail, and why even the best sensors can’t be trusted on their own. You’ll explore the real sources of uncertainty — sensor noise, environmental interference, timing jitter, model imperfections, and even human engineering choices — all illustrated with relatable scenarios such as GPS drift, accelerometer bias, and unpredictable sensor timing. As the book states, “The world is messy. Sensors are imperfect. Nothing we measure is ever exactly right.” Instead of overwhelming you with formulas, the book builds intuition step by step. You’ll learn how engineers think about uncertainty as a “cloud” around the truth, how predictions make that cloud grow, and how measurements shrink it again. You’ll see why relying only on sensors leads to jitter, why relying only on models leads to drift, and how Kalman filters intelligently combine both to produce the best possible estimate of a hidden state. As the text explains, “Two imperfect pieces of evidence, combined intelligently, are better than either one on its own.” By the end, you’ll understand not just how Kalman filters work, but why they are structured the way they are — and how to apply them to real engineering problems. Whether you’re building drones, robots, autonomous systems, or any device that must make sense of noisy data, this book gives you the mental models, intuition, and practical insight to design systems that work reliably even when the world doesn’t cooperate. Perfect for: - Robotics and drone engineers - Embedded developers and makers - Students learning estimation and control - Anyone working with sensors, noise, or real‑time systems

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 analogies
• How to build prediction models using state‑space systems
• How horizons, costs, and constraints shape controller behavior
• How to tune Q, R, and terminal costs without guesswork
• How to handle actuator limits, rate limits, and safety constraints
• How to design offset‑free MPC using disturbance models
• How robust MPC and constraint tightening keep real systems safe
• Why MPC reduces to LQR when constraints disappear—and why that matters

Every 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.