PID, state-space, optimal control, path planning, and trajectory generation connected to robot and autonomous-vehicle implementation.
This is an editorially selected reading order. Begin at step 1 or go directly to the topic you need.
PID is not merely three gains. This primer connects its intuition and equations to sampling, saturation, anti-windup, tuning, safety, ROS 2, and real implementation checks.
Reproduce how measurement delay, actuator limits and integral windup change PID tracking on the same second-order model.
Sensor readings are not the state itself. This primer explains observability, Kalman predict/update equations, covariance, numerical examples, control integration, robot implementation, and fault-safe behavior.
LQR derives state-feedback gains from a state-space model and a quadratic cost. This primer covers Riccati equations, observability, PID/MPC tradeoffs, robot implementation, numerical intuition, and safety.
Model Predictive Control predicts future behavior with a model, optimizes a constrained input sequence, applies only its first move, and repeats. This primer covers equations, tuning, safety, implementation, and current examples.
Path planning is not drawing a shortest line. This primer compares grid and continuous-space algorithms while accounting for robot footprint, map uncertainty, computation, SLAM/Nav2 integration, and safety.
A path says where to travel; a trajectory says when and with what velocity, acceleration, and jerk. This primer covers minimum-jerk, quintic, trapezoidal profiles, constraints, safety, and ROS 2 integration.