Most engineering models come from measurement, not derivation — even when the underlying physics is well understood. System Identification for Engineers teaches the small set of methods that cover the large majority of real identification jobs: designing an experiment that genuinely excites the dynamics you care about, cleaning and preprocessing real sensor data, fitting first- and second-order models by hand and by code, least squares and its recursive (online) form, ARX/ARMAX/Output-Error model structures, subspace state-space identification, closed-loop and nonlinear identification, and validating a model honestly before trusting it in deployment.

Every method is built plain-language-first, worked through with runnable Python (NumPy, SciPy, python-control) on clearly-labeled synthetic datasets, and tied together in three full case studies that run the entire pipeline start to finish: a DC motor identified from encoder data, a quadrotor attitude axis identified under closed-loop and mild nonlinear conditions, and a battery equivalent-circuit model tracked across simulated aging. Written for working controls, robotics, and mechatronics engineers who need a model that holds up in deployment — not a research paper.

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.

Battery Management Systems Made Easy
A Practical Engineer’s Guide to Designing, Building, and Validating Lithium‑Ion Battery Management Systems

Modern lithium‑ion packs are unforgiving. Push voltage too high and you plate lithium. Charge below freezing and you destroy the cell. Trust a noisy current sensor and your SoC drifts into fantasy. A BMS exists to prevent all of that — but building one that is accurate, safe, and robust is far from trivial.

This book closes the gap between academic electrochemistry and real‑world engineering. It is written for the engineer who must take a stack of cells, a microcontroller, and a deadline — and deliver a system that does not catch fire and does not lie about how much energy is left.

• How lithium‑ion cells really behave: OCV curves, internal resistance, temperature effects, aging
• How to design the core BMS algorithms: SoC, SoH, power estimation, balancing
• How to architect a complete system: AFEs, current sensing, thermal design, safety paths
• How to implement firmware that is robust, testable, and fault‑tolerant
• How to validate a pack: test plans, standards, certification, field diagnostics
• How to avoid real failures seen in e‑bikes, telecom systems, ESS, and EV prototypes

• Written by an engineer, not a theorist
• Focus on practical trade‑offs, not idealized models
• Real field failures, real numbers, real constraints
• Clear math without unnecessary derivations
• Includes reference equations, BOM templates, code snippets, and troubleshooting playbooks

If you design, review, or debug lithium‑ion battery systems — this book will make your work faster, safer, and more confident.

Master control systems without drowning in theory.

Control Systems Made Easy is a practical, engineer-focused guide to understanding and applying modern control engineering principles—from feedback loops and transfer functions to PID tuning and state space methods.

Designed for students, robotics developers, embedded engineers, and working professionals, this book bridges the gap between textbook mathematics and real-world engineering implementation.

Rather than presenting control theory as abstract equations, this book explains how engineers actually design, tune, and troubleshoot dynamic systems.

Inside you'll learn how to:

• Understand feedback and closed-loop behavior

• Model dynamic systems using differential equations and transfer functions

• Analyze stability and transient response

• Design and tune PID controllers with confidence

• Interpret root locus and frequency-domain methods

• Work with state-space models and observers

• Build intuition for robustness and practical constraints

• Apply control concepts to robotics, automation, drones, and embedded systems

Every chapter includes:

• Clear explanations without unnecessary academic complexity

• Practical engineering examples

• Common pitfalls and troubleshooting advice

• Python simulations and code examples

• End-of-chapter exercises and review checklists

Whether you're preparing for exams, building robotic systems, or refreshing your engineering fundamentals, this book will help you develop genuine control intuition—not just formula memorization.

Control engineering doesn’t have to be intimidating.

Learn the principles. Build intuition. Design better systems.

Sensor Fusion Made Easy

Sensor Fusion Made Easy is a practical, engineer-focused guide to building reliable state estimation systems using real-world sensors.

Modern robotics, drones, and autonomous systems depend on combining imperfect data from IMUs, GPS, cameras, LiDAR, and other sensors. Yet most resources on sensor fusion are either too theoretical or too fragmented to apply in real projects.

This book bridges that gap.

Designed for working engineers and advanced developers, it takes a hands-on approach to sensor fusion — starting with intuitive concepts and progressing toward production-ready systems.

Inside This Book You Will Learn How To

• Understand and model real sensor noise and failure modes
• Build Kalman Filters from scratch for linear systems
• Apply Extended and Unscented Kalman Filters to nonlinear problems
• Implement complementary filters for embedded systems
• Work with real sensors including IMU, GNSS, cameras, and LiDAR
• Design multi-sensor fusion architectures with fault detection
• Transition from theory to deployable pipelines using ROS

Each concept is presented with:

• clear explanations
• practical engineering insights
• fully runnable Python and C++ examples

You will not only understand how sensor fusion works — you will build systems that actually run, debug them when they fail, and improve them for real-world performance.

By the end of this book, you will have a solid foundation in estimation techniques and the confidence to design robust sensor fusion pipelines for:

• robotics
• autonomous vehicles
• drones
• embedded systems
• intelligent machines

This is not a purely academic textbook.

It is a field guide for engineers who need results.