References
Books, papers, courses, and software for control systems theory.
A curated index of 44 resources across 4 categories, including the textbook references used to expand the map coverage.
25 entries
Textbooks & Learning Resources
Canonical books and video resources spanning classical control, digital and sampled-data control, state space, nonlinear systems, MPC, optimal control, stochastic estimation, adaptive control, and robust MIMO design.
Feedback Systems: An Introduction for Scientists and Engineers
Karl J. Astrom and Richard M. Murray. Open introduction to feedback, modeling, linear systems, state/output feedback, PID, robustness, and architecture.
Feedback Control of Dynamic Systems
Gene F. Franklin, J. David Powell, and Abbas Emami-Naeini. Classical and state-space design, root locus, frequency response, digital control, nonlinear systems, and case studies.
Digital Control of Dynamic Systems, 3rd ed.
Gene F. Franklin, J. David Powell, and Michael L. Workman. Sampled-data modeling, z-transform analysis, discrete equivalents, transform and state-space design, quantization, sample-rate selection, identification, nonlinear effects, and a disk-drive servo case study.
Applied Digital Control: Theory, Design and Implementation
J. R. Leigh. Sampling, z-transform methods, root locus and frequency-response design, digital algorithms, sensors and converters, implementation case histories, large-scale systems, distributed computer control, adaptive control, and robust control.
Digital Control System Analysis & Design, Global Edition
Charles L. Phillips, H. Troy Nagle, and Aranya Chakrabortty. Discrete-time systems, sampling and reconstruction, open- and closed-loop sampled systems, stability analysis, digital controller design, pole assignment, observers, identification, LQ control, Kalman filtering, and case studies.
Control Systems Engineering
Norman S. Nise. Modeling, time response, subsystem reduction, stability, steady-state error, root locus, frequency response, state-space design, and digital control.
Modern Control Engineering
Katsuhiko Ogata. Modeling of mechanical, electrical, fluid, and thermal systems; transient response; root locus; frequency response; PID; and state-space design.
Modern Control Systems
Richard C. Dorf and Robert H. Bishop. Mathematical models, state variables, feedback performance, stability, root locus, frequency design, robust control, and digital control.
Control System Design
Graham C. Goodwin, Stefan F. Graebe, and Mario E. Salgado. SISO/MIMO design, PID, sampled-data and hybrid control, optimization, state space, nonlinear control, MPC, and decoupling.
Multivariable Feedback Control: Analysis and Design
Sigurd Skogestad and Ian Postlethwaite. MIMO limitations, uncertainty, robust stability and performance, controller design, control-structure design, model reduction, and LMIs.
Nonlinear Systems
Hassan K. Khalil. Phase-plane behavior, Lyapunov stability, input-output stability, passivity, perturbation methods, singular perturbations, and feedback linearization.
Model Predictive Control
Eduardo F. Camacho and Carlos Bordons. Generalized, commercial, multivariable, constrained, robust, nonlinear, hybrid, and fast MPC.
Dynamic Programming and Optimal Control
Dimitri P. Bertsekas. Dynamic programming, deterministic and stochastic decision problems, shortest paths, imperfect information, infinite-horizon problems, and approximate DP.
Optimal Control Theory: An Introduction
Donald E. Kirk. Performance measures, dynamic programming, calculus of variations, Pontryagin's minimum principle, and numerical trajectory optimization.
Control System Design: An Introduction to State-Space Methods
Bernard Friedland. State-space representation, frequency analysis, controllability, observability, pole placement, observers, separation principle, LQR, random processes, and Kalman filters.
Optimal Control and Estimation
Robert F. Stengel. Optimal trajectories, LQ control, optimal estimation, Kalman filtering, stochastic optimal control, dual control, and multivariable design.
Optimal State Estimation: Kalman, H-infinity, and Nonlinear Approaches
Dan Simon. Least squares, Kalman filters, information and square-root forms, smoothing, H-infinity filtering, EKF, UKF, and particle filters.
Adaptive Control
Karl J. Astrom and Bjorn Wittenmark. Real-time parameter estimation, self-tuning regulators, MRAS, stochastic adaptive control, auto-tuning, gain scheduling, and implementation.
Introduction to Stochastic Control Theory
Karl J. Astrom. Stochastic processes, stochastic state models, spectral descriptions, stochastic differential equations, parametric optimization, and optimal stochastic control.
Schaum's Outline of Feedback and Control Systems
Joseph J. DiStefano III, Allen R. Stubberud, and Ivan J. Williams. Problem-oriented review of Laplace and Z-transforms, stability, transfer functions, block diagrams, signal-flow graphs, Nyquist, root locus, Bode, and Nichols methods.
Steve Brunton's Control Bootcamp
YouTube playlist introducing control-system modeling, analysis, and design with practical examples.
Brian Douglas Control Systems Lectures
YouTube channel with approachable lectures on classical control, frequency-domain methods, state-space control, and control intuition.
MathWorks/MATLAB YouTube Channel
Videos on MATLAB, Simulink, Control System Toolbox workflows, modeling, simulation, and control design examples.
Prof Giordano Scarciotti YouTube Channel
Lecture videos on control theory, dynamical systems, and related engineering mathematics.
Robotic Systems Control YouTube Channel
Videos on robotics-oriented control, system modeling, estimation, and implementation topics.
8 entries
Open Texts & Course Notes
Freely accessible notes and textbooks for structured study and implementation practice.
MIT OCW 6.241J Dynamic Systems and Control
Graduate notes on dynamic systems, feedback interconnections, stability, performance, and robust control.
Caltech CDS 101/110
Course material aligned with Astrom and Murray's Feedback Systems text.
Stanford EE363 Linear Dynamical Systems
State-space models, controllability, observability, least squares, and estimation.
Stanford EE364B Convex Optimization II
Robust optimization, stochastic MPC, model predictive control, and numerical methods.
MIT Underactuated Robotics
Nonlinear dynamics, LQR, trajectory optimization, planning, Lyapunov analysis, learning, and robotics applications.
Model Predictive Control: Theory, Computation, and Design
Open MPC textbook by Rawlings, Mayne, and Diehl with theory, algorithms, computation, and examples.
Planning Algorithms
Steven M. LaValle. Open book on combinatorial planning, sampling-based motion planning, collision checking, feedback motion planning, and planning under uncertainty.
Linear Matrix Inequalities in System and Control Theory
Boyd, El Ghaoui, Feron, and Balakrishnan. Foundational reference on LMI formulations for systems, robust control, stability, and convex optimization.
6 entries
Classic Papers & Surveys
Primary papers and surveys behind filtering, MPC, path planning, and safety-critical control.
A New Approach to Linear Filtering and Prediction Problems
R. E. Kalman's original Kalman filtering paper.
Constrained Model Predictive Control: Stability and Optimality
Mayne, Rawlings, Rao, and Scokaert. Core reference on MPC stability and optimality.
Rapidly-Exploring Random Trees: A New Tool for Path Planning
Steven M. LaValle's original RRT technical report.
A Formal Basis for the Heuristic Determination of Minimum Cost Paths
Hart, Nilsson, and Raphael's original A* paper.
Control Barrier Functions: Theory and Applications
Survey-style reference on safety-critical control with CBFs.
Robustness of Control Barrier Functions for Safety Critical Control
Robustness and CLF-CBF quadratic-program formulations.
5 entries
Open Software & Benchmarks
Tools for modeling, analysis, simulation, optimization, control design, and estimation.
python-control
Python library for classical and state-space control analysis and design.
Drake
Model-based robotics toolbox with simulation, optimization, planning, and control tools.
CasADi
Symbolic framework for nonlinear optimization and optimal control.
do-mpc
Python toolbox for nonlinear MPC and moving horizon estimation.
JuliaControl
Julia ecosystem for control systems modeling, analysis, and synthesis.