Dynamics, Control & Trajectories

Making robots move precisely. Actuator sizing, PID tuning, manipulator dynamics and joint control, servo motion control and trajectory generation.

• Actuator Selection & Motor/Gearbox Sizing — Choosing the right motor for the job. Learn actuator types (brushed DC, BLDC/PMSM servo, stepper, AC induction, linear motors, piezo, solenoids) and when to use each, move profiles and torque requirements (acceleration, friction, gravity), reflected inertia and gear ratios, inertia matching, RMS torque and thermal limits, speed–torque curve checks, gearbox backlash and efficiency, and a sizing workflow — with Python comparing gear ratios.
• PID Tuning & Digital Implementation — Making PID work on real plants. Learn PID forms (ideal, parallel, series), tuning methods (Ziegler–Nichols open and closed loop, Cohen–Coon, IMC/lambda, SIMC), derivative filtering and derivative-on-measurement, setpoint weighting, integral windup and anti-windup, bumpless transfer, discretisation and sample time — with a full discrete PID simulator in Python comparing Z-N and SIMC tuning.
• Robot Dynamics & Joint Control — Forces, motion and making robots follow commands. Learn rigid-body dynamics of manipulators (inertia, Coriolis/centrifugal and gravity terms), the Euler–Lagrange and Newton–Euler methods, the manipulator equation M(q)q̈ + C(q,q̇)q̇ + g(q) = τ, actuator and gearbox effects (reflected inertia, friction), independent-joint PID control, gravity compensation, computed-torque (inverse-dynamics) control, impedance/admittance and force control, and collision detection — with a Python simulation of a single-link arm under PD + gravity compensation.
• Servo Motion Control: Trajectories & Cascaded Loops — Making axes move precisely and smoothly. Learn motion profiles (trapezoidal, S-curve, jerk limits), cascaded current–velocity–position loops and why each is faster than the next, tuning procedures, feedforward (velocity, acceleration, friction), following error, multi-axis coordination (interpolation, electronic gearing and camming), resonance and notch filters, and motion controllers and networks — with Python for profiles and a cascaded-loop simulation.
• Trajectory Generation: Polynomials, Profiles & Cartesian Paths — Turning goals into smooth, feasible motion. Learn path vs trajectory, point-to-point joint moves, cubic and quintic polynomials with boundary conditions, trapezoidal (LSPB) and S-curve (jerk-limited) velocity profiles, synchronising multiple joints, multi-point paths with via points and blending (splines), Cartesian straight-line and circular moves (with orientation interpolation via slerp), time-optimal scaling under velocity/acceleration/torque limits, and robot move instructions (MoveJ, MoveL, MoveC) — with Python for a quintic trajectory and a trapezoidal move time.