Learning library
Machine learning
Machine learning here is built from its foundations rather than from libraries. Begin with vectors, gradients, and probability, then use them to fit models, judge their errors, and understand what a learned system can and cannot tell you.
99 lessons, in library order. Search within machine learning.
Foundations / 9 min read
Vector spaces: build direction from addition and scaling
Learn vector spaces through robot displacement. Explore span, linear independence, basis, and dimension with an interactive diagram, Python, and exercises.
- linear algebra
- robotics
- machine learning
Foundations / 9 min read
The dot product: angles, projections, and robot motion
Learn the dot product with an interactive vector diagram, a worked projection example, and a robot heading calculation. Includes exercises and solutions.
- linear algebra
- robotics
- machine learning
Foundations / 8 min read
Conditional probability: reading a robot sensor
Learn conditional probability with an interactive robot sensor example. Work through the formula, Bayes' rule, base rates, and common mistakes using counts.
- probability
- robotics
- machine learning
Foundations / 8 min read
Gradient descent: learn the update, then test its limits
Work through gradient descent by hand, then explore learning rates, overshoot, and stationary points in an interactive experiment with Python and exercises.
- optimization
- machine learning
- robotics
Foundations / 9 min read
Linear regression: fit a line and inspect its errors
Fit linear regression to robot sensor readings. Work through least squares, residuals, and MSE, then test an outlier with Python and exercises.
- regression
- robotics
- machine learning
Foundations / 8 min read
Bayesian inference: learning a robot's grasp success rate
Learn Bayesian inference through a robot grasp example. Update a Beta prior with successes and failures, then distinguish uncertainty from prediction.
- probability
- robotics
- machine learning
Foundations / 9 min read
Eigenvalues and eigenvectors: find the lines a matrix preserves
Understand eigenvalues through 2D transformations. Test stretching, reversal, zero eigenvalues, rotation, and repeated values, then connect them to robot error dynamics.
- linear algebra
- robotics
- machine learning
Foundations / 10 min read
Tensors: read shapes, select values, and move axes
Learn tensors through a robot image batch. Explore shape, indexing, slicing, and axis order, then compare reshape with transpose using a runnable Python example.
- linear algebra
- robotics
- machine learning
Foundations / 10 min read
Logistic regression: turn a score into a probability
Explore logistic regression with synthetic robot observations. Calculate sigmoid probabilities and log loss, then change a threshold and inspect the confusion matrix.
- classification
- probability
- robotics
- machine learning
Foundations / 9 min read
Markov chains: transitions and stationary distributions
Learn Markov chains with a robot example. Explore transition matrices, stationary distributions, periodic chains, and absorbing states with an interactive model.
- probability
- robotics
- machine learning
Foundations / 9 min read
Laplace distribution: model sensor error and tolerance
Learn the Laplace distribution through sensor error. Explore location, scale, density, interval probabilities, and the link between absolute error and median fitting.
- probability
- robotics
- machine learning
Foundations / 11 min read
Random forests: train different trees and combine their votes
Build a small random forest from synthetic robot observations. Inspect learned splits, bootstrap samples, random feature choices, and individual tree votes.
- classification
- robotics
- machine learning
Foundations / 8 min read
A map of machine learning: tasks, signals, and models
Understand how supervised learning, self-supervision, reinforcement learning, NLP, and deep learning fit together through practical robot and language examples.
- machine learning
- robotics
Foundations / 9 min read
NLP: turn robot commands into token probabilities
Learn natural language processing through a small robot-command corpus. Compare tokenization, unigram and bigram counts, unseen contexts, embeddings, and evaluation.
- natural language processing
- machine learning
- robotics
Foundations / 10 min read
pandas: select, align, and check a table of robot readings
Learn pandas Series and DataFrames through robot readings. Compare loc and iloc, inspect label alignment, handle missing values, and check grouped summaries and joins.
- python
- data preparation
- robotics
- machine learning
Foundations / 9 min read
Vector norms and normalization: L1, L2, and L∞
Measure vectors with L1, L2, and infinity norms. Explore unit boundaries, normalize a robot displacement, and distinguish zero vectors from tiny nonzero inputs.
- linear algebra
- robotics
- machine learning
Foundations / 10 min read
Matrix multiplication: calculate entries and compose transformations
Learn matrix multiplication through row-column dot products, compatible shapes, and a rotation-and-stretch experiment that shows why transformation order matters.
- linear algebra
- robotics
- machine learning
Foundations / 11 min read
Cross product: find a normal direction and calculate torque
Calculate a three-dimensional cross product, follow the right-hand rule, and connect its magnitude to area. Explore signed torque with a movable lever arm and force.
- linear algebra
- robotics
- machine learning
Foundations / 9 min read
Matrix transpose and inverse: when do they agree?
Transpose rectangular matrices, calculate a 2×2 inverse, and test when a transpose reverses a transformation. Explore rotations, reflections, and singular maps.
- linear algebra
- robotics
- machine learning
Foundations / 11 min read
Determinants: signed area, volume, and collapsed directions
Calculate a determinant, see how its sign records orientation, and connect zero area to singular matrices. Learn why a small determinant alone does not imply poor conditioning.
- linear algebra
- robotics
- machine learning
Foundations / 12 min read
Rank and null space: reachable outputs and hidden input changes
Use rank, column space, and null space to understand a linear map. Explore rank-nullity, unreachable targets, and families of solutions with a small matrix experiment.
- linear algebra
- robotics
- machine learning
Foundations / 10 min read
Projections and least squares: find the closest fit
Project a vector onto a direction, measure its orthogonal residual, and connect that geometry to least squares, regression, and nonunique coefficients.
- linear algebra
- robotics
- machine learning
Foundations / 12 min read
Singular value decomposition: directions, gains, and low-rank approximation
Build an SVD from orthogonal directions and nonnegative gains. See a circle become an ellipse, identify lost directions, and measure the error from keeping one singular component.
- linear algebra
- robotics
- machine learning
Foundations / 11 min read
Pseudoinverse: choose the smallest least-squares solution
Understand the Moore–Penrose pseudoinverse through exact and inconsistent systems. Separate residual error from solution norm, inspect projectors, and see how an SVD cutoff changes the problem.
- linear algebra
- robotics
- machine learning
Foundations / 9 min read
Coordinate frames: read the same point from a robot and the world
Convert a fixed landmark between robot and world coordinates. Learn frame conventions, translation and rotation, inverse transforms, and point versus displacement.
- linear algebra
- robotics
- machine learning
Foundations / 12 min read
Rotation matrices: turn vectors, track frames, and check the order
Build rotation matrices that preserve length and handedness. Compare fixed-axis rotations in 3D, distinguish rotating a vector from changing its coordinates, and undo a rotation with its transpose.
- linear algebra
- robotics
- machine learning
Foundations / 12 min read
Homogeneous transformations: map sensor coordinates into the world
Combine rotation and translation in one matrix. Follow a sensor-to-robot-to-world frame chain, distinguish points from displacements, and calculate the inverse.
- linear algebra
- robotics
- machine learning
Foundations / 10 min read
Euler angles and gimbal lock: when different angles mean the same orientation
Explore roll, pitch, and yaw with full rotation matrices. Compare equivalent orientations at ±90° pitch and separate Euler angle rates from angular velocity.
- linear algebra
- robotics
- machine learning
Foundations / 12 min read
Axis-angle rotation: build Rodrigues’ formula from three vector terms
Rotate a vector around any nonzero axis. Normalize the direction, follow Rodrigues’ parallel and perpendicular terms, and understand the equivalent descriptions at zero and 180 degrees.
- linear algebra
- robotics
- machine learning
Foundations / 12 min read
Unit quaternions: compose rotations and understand the sign
Rotate vectors with Hamilton quaternions, check composition order, and see why q and minus q describe the same orientation. Includes an interactive experiment and Python.
- linear algebra
- robotics
- machine learning
Foundations / 10 min read
Derivatives and the chain rule: predict a small change
Understand derivatives as local rates, compare secant and tangent slopes, and multiply the correct factors through nested functions. Test a smooth curve and a corner with an interactive experiment.
Foundations / 13 min read
Partial derivatives and gradients: predict a multivariable change
Hold one input fixed to find a partial derivative, combine the partials into a gradient, and compare a directional derivative with the actual change from a finite step.
- calculus
- optimization
- robotics
- machine learning
Foundations / 13 min read
Jacobian matrices: from joint rates to robot tip velocity
Read a Jacobian by its rows and columns, calculate a two-link arm's tip velocity, and compare a local prediction with a finite move. Includes singularities and the multivariate chain rule.
- calculus
- linear algebra
- robotics
- machine learning
Foundations / 11 min read
Hessians: measure curvature in every direction
Differentiate a gradient to build the Hessian, calculate directional curvature, and classify stationary points. Explore coupled quadratics, saddles, and the limits of zero eigenvalues.
- calculus
- linear algebra
- optimization
- robotics
- machine learning
Foundations / 13 min read
Taylor expansion and linearization: predict locally and check the error
Build constant, linear, and quadratic approximations around a chosen center. Compare their errors, calculate a Taylor remainder bound, and connect the same idea to gradients, Hessians, and Jacobians.
Foundations / 12 min read
Ordinary differential equations: turn a rate law into a time course
Solve a cooling initial-value problem, compare its exact solution with Euler steps, and separate model behavior from numerical accuracy and stability.
Foundations / 12 min read
Numerical integration: compare drift, phase, and step cost
Advance an oscillator with forward Euler, velocity-first symplectic Euler, and classical RK4. Compare each method with the analytic solution and separate energy drift, phase error, step cost, and stability.
Foundations / 12 min read
ODE stability: equilibria, attraction, and basins
Classify equilibria of a nonlinear rate law, use a phase line to find basins of attraction, and compare exact trajectories without confusing model stability with numerical stability.
Foundations / 12 min read
Manifolds and tangent spaces: move along a constraint
Use the unit circle to understand local coordinates and tangent vectors. Compare straight steps, exact rotation, and normalization, then examine why averaging headings and rotations needs care.
- calculus
- linear algebra
- robotics
- machine learning
Foundations / 13 min read
Geodesics: shortest arcs and longer routes on a circle
Compare a shortest circle arc, a longer constant-speed geodesic, and a straight chord. Work through angle wrapping, antipodal ties, coincident endpoints, and the metric that defines distance.
- calculus
- linear algebra
- robotics
- machine learning
Foundations / 12 min read
Lie groups and Lie algebras: connect robot poses to local motions
Use planar robot poses to understand SE(2), its tangent space se(2), and the Lie bracket. Compare motion order, shrink a commutator loop, and reproduce the calculations in Python.
- calculus
- linear algebra
- robotics
- machine learning
Foundations / 13 min read
Exponential and logarithm maps: turn a body twist into a pose
Exponentiate a constant planar body twist, calculate its coupled translation, and recover a chosen logarithm. Explore straight-motion limits, half-turn branch choices, and information lost in a full turn.
- calculus
- linear algebra
- robotics
- machine learning
Foundations / 13 min read
Twists and screw axes: connect point velocities to rigid motion
Build a six-component twist from a screw axis, calculate point velocities, and compare exact helical motion with a tangent prediction. Separate pitch, accumulated displacement, current rate, and pure translation.
- linear algebra
- robotics
- machine learning
Foundations / 12 min read
Adjoint transformations: express a twist in another frame
Transform angular-first twists between body and space frames. Derive the origin-shift term, distinguish linear twist coordinates from point velocity, and check a planar example with an interactive adjoint matrix.
- linear algebra
- robotics
- machine learning
Foundations / 14 min read
Product of exponentials: build a robot arm’s forward kinematics
Build a two-joint arm's tool pose from fixed home screw axes and matrix exponentials. Check the result against geometry, inspect multiplication order, and connect space and body formulas.
- linear algebra
- robotics
- machine learning
Foundations / 14 min read
Wrenches: combine force, moment, and power across frames
Calculate a force's moment about a chosen origin, include a free couple, and transform a moment-first wrench between frames. Use a worked planar load to check the inverse-transpose rule and power invariance.
- linear algebra
- robotics
- machine learning
Foundations / 14 min read
Space and body Jacobians: map joint rates to rigid motion
Build space and body Jacobians from joint screw axes, recover the physical tool velocity, and compare their ranks with a position-only task. Explore a planar two-link arm and verify its derivatives in Python.
- linear algebra
- robotics
- machine learning
Foundations / 14 min read
Robot statics: turn tool loads into holding torques
Use virtual work and a Jacobian transpose to calculate a robot arm's joint loads. Distinguish external and holding torque, check space and body frames, and interpret zero-torque loads.
- linear algebra
- robotics
- machine learning
Foundations / 13 min read
Kinematic singularities: find the tip velocities an arm can produce
Use a two-link robot arm to distinguish exact rank loss from near-singular conditioning. Calculate the minimum-norm joint rates for a requested tip velocity and identify the component the arm cannot produce.
- linear algebra
- robotics
- machine learning
Foundations / 11 min read
Manipulability: read a robot’s velocity ellipse
Map a joint-rate budget into a robot’s possible tool velocities. Read the ellipse’s singular values, compare area with conditioning, and understand singular poses, units, and the limits of force duality.
- linear algebra
- robotics
- machine learning
Foundations / 14 min read
Numerical inverse kinematics: solve a tool position with local steps
Use a position Jacobian and damped least squares to refine a two-joint arm toward a target. Inspect accepted steps, compare starting guesses, and distinguish convergence, a stalled solve, and unreachable geometry.
- linear algebra
- robotics
- machine learning
Foundations / 14 min read
Differential inverse kinematics: turn a tip-velocity command into a joint step
Calculate damped joint rates for a robot tip-velocity command, measure the resulting speed and direction error, and compare an instantaneous prediction with one finite joint step.
- linear algebra
- robotics
- machine learning
Foundations / 12 min read
Kinematic redundancy: use the motion a task leaves free
Split a three-link arm’s joint rates into a primary solution and null-space motion. Check the exact projector, compare damping leakage, and measure why a finite step can move a tool with zero initial velocity.
- linear algebra
- robotics
- machine learning
Foundations / 12 min read
Joint limits in inverse kinematics: solve a bounded velocity step
Turn physical joint ranges and speed limits into bounds on a local inverse-kinematics command. Compare a constrained least-squares solution with clipping, and check the resulting finite arm position.
- linear algebra
- robotics
- machine learning
Foundations / 13 min read
Analytical inverse kinematics: find both arm configurations for a target
Derive both joint-angle solutions for a two-link robot arm, check them with forward kinematics, and identify unreachable targets and merged boundary branches.
- linear algebra
- robotics
- machine learning
Foundations / 11 min read
Robot workspaces: derive the reachable position set
Derive the exact position workspace of a two-link robot with elbow limits. Test targets against its annulus, recover a valid arm configuration, and separate position reach from orientation and path feasibility.
- linear algebra
- robotics
- machine learning
Foundations / 13 min read
Trajectory time scaling: choose when a robot follows its path
Separate a robot’s geometric path from its timing. Compare cubic and quintic profiles, derive joint speed and acceleration through the chain rule, and choose a duration that meets explicit limits.
Foundations / 13 min read
Configuration space: follow joint paths across angle boundaries
Represent a robot arm as a point in joint space, follow paths across periodic angle boundaries, and distinguish angular distance from workspace motion and collision clearance.
- linear algebra
- robotics
- machine learning
Foundations / 11 min read
Collision checking: test the motion between endpoints
Check a translating disk against a circular obstacle, including every point between its endpoints. Derive the closest-point test, expose missed samples, and distinguish broad-phase box overlap from a collision.
- linear algebra
- robotics
- machine learning
Foundations / 12 min read
Trapezoidal velocity profiles: accelerate, cruise, and stop
Build a rest-to-rest motion for one linear joint. Derive triangular and trapezoidal velocity profiles, calculate braking distance, and inspect exact position, velocity and acceleration within explicit limits.
Foundations / 11 min read
Dijkstra’s algorithm: find the lowest-cost route
Trace Dijkstra’s algorithm through a weighted grid, update route estimates, and see why the goal must leave the priority queue before its cost is final. Reproduce a complete search in Python.
- optimization
- robotics
- machine learning
Foundations / 12 min read
A* search: guide the search with a lower bound
Use A* to find a low-cost route across a weighted grid. Calculate g, h, and f, compare Manhattan distance with Dijkstra, and see how an overestimate can return a worse path.
- optimization
- robotics
- machine learning
Foundations / 12 min read
Rapidly exploring random trees: grow a collision-free path
Build an RRT for a disk robot, check every new branch for collision, and connect the tree to a goal. Explore seeded sampling, step length, narrow passages, and the limits of a finite search budget.
- robotics
- machine learning
Foundations / 13 min read
Probabilistic roadmaps: reuse a graph for new routes
Build a probabilistic roadmap from collision-free samples, attach new start and goal queries, and search the same graph for routes. Explore neighbor counts, missed connections, and what a finite roadmap can prove.
- robotics
- machine learning
Foundations / 12 min read
RRT*: improve a path by rewiring the tree
Follow RRT* as it chooses cheaper parents, rewires nearby nodes, and updates every descendant's cost. Compare the first path with later improvements and understand what asymptotic optimality does and does not promise.
- robotics
- machine learning
- optimization
Foundations / 12 min read
Path smoothing: shorten a route with checked shortcuts
Shorten a robot path by removing unnecessary waypoints while checking every replacement segment for collision. Compare length and clearance, trace accepted and rejected shortcuts, and separate a simpler path from smooth robot motion.
- robotics
- machine learning
Foundations / 14 min read
Differential-drive kinematics: from wheel rates to pose
Convert left and right wheel rates into robot speed, turning rate, and an exact constant-rate pose update. Explore straight travel, arcs, spins, and reverse motion, then check the limits of wheel odometry.
- robotics
- machine learning
Foundations / 12 min read
Nonholonomic constraints: move sideways without sliding
Derive a wheeled robot’s no-sideways-slip constraint and trace a feasible maneuver that changes its lateral position. Separate instantaneous velocity limits from reachable poses, and see why the motion rules depend on the robot.
- robotics
- machine learning
Foundations / 13 min read
Dubins paths: shortest routes with a turning limit
Connect two robot poses with forward motion and a minimum turning radius. Compare all six Dubins path families, calculate arc lengths, and see why matching position alone misses the heading constraint.
- robotics
- machine learning
- optimization
Foundations / 14 min read
Ackermann steering: calculate wheel angles and turning radius
Derive the inner and outer front-wheel angles for ideal Ackermann steering. Connect bicycle steering, wheelbase, and track width to turning radius, reverse motion, and the rear axle's path.
- robotics
- machine learning
Foundations / 12 min read
Wheel odometry: turn encoder counts into a moving pose
Convert wheel encoder increments into a differential-drive robot's position and heading. Replay measured counts, calculate exact arc updates, and see how calibration errors and wheel slip change the estimate.
- robotics
- machine learning
Foundations / 12 min read
Reeds–Shepp paths: shortest car routes with reverse gear
Add reverse travel to a car with a minimum turning radius. Read signed motion primitives, calculate a three-arc turnaround, and compare complete Reeds–Shepp solutions with forward-only Dubins paths.
- robotics
- machine learning
- optimization
Foundations / 12 min read
Omnidirectional drives: move sideways with three omniwheels
Derive the wheel speeds for a three-wheel Kiwi drive. Command forward, sideways, and turning motion, preserve direction when motors saturate, and integrate the resulting world path.
- robotics
- machine learning
Foundations / 14 min read
Instantaneous center of rotation: find the center from a planar velocity
Calculate a planar rigid body's instantaneous center of rotation from its linear and angular velocity. Check point velocities, move the reporting reference, and distinguish turning, translation, and rest.
- robotics
- machine learning
Foundations / 12 min read
Skid steering: why turning requires wheel slip
Derive why four fixed wheels must scrub sideways during a turn. Compare a chosen effective-track model with differential-drive odometry, calculate contact slip speeds, and distinguish equivalent side rotation centers from the body's turning center.
- robotics
- machine learning
Foundations / 13 min read
Robot dynamics: separate the torques that move an arm
Explore robot dynamics through a two-link arm's torque budget. Separate inertia, velocity coupling, gravity, and friction, then check holding torque, link mass, and mechanical power.
- robotics
- machine learning
Foundations / 16 min read
Inverse dynamics: calculate the torque a robot’s motion needs
Calculate joint torque from a robot arm’s pose, velocity, and requested acceleration. Account for gravity, coupling, friction, and a known tip force, then see how actuator limits change the resulting acceleration.
- robotics
- machine learning
Foundations / 14 min read
Forward dynamics: predict an arm’s motion from joint torques
Solve a robot arm’s joint accelerations from torque, configuration, and velocity. Replay gravity release and compensation with RK4, then check energy balance and step-size error.
- robotics
- machine learning
Foundations / 14 min read
Dynamic parameter identification: learn a joint model from motion
Fit dynamic parameters from a rotating joint's motion and torque data. Recover inertia, gravity mass moment, and damping, then test excitation, noise, and held-out predictions.
- robotics
- machine learning
Foundations / 12 min read
Friction models: torque during motion and at rest
Compare Coulomb, viscous, and Stribeck friction in a robot joint. Calculate resisting torque and power loss during motion, check static holding at zero speed, and see where friction compensation needs a better model.
- robotics
- machine learning
Foundations / 15 min read
Actuator dynamics: from motor voltage to joint torque
Explore actuator dynamics with a DC motor’s current rise, back EMF, and geared load. Compare finite inductance with a reduced model, calculate reflected rotor inertia, and check torque, steady speed, and energy balance.
- robotics
- machine learning
Foundations / 14 min read
Joint flexibility: model the twist between motor and load
Explore joint flexibility with two rotary inertias joined by a spring and damper. Calculate transmitted torque, loaded deflection, elastic oscillation, and energy loss, then compare motor and load motion.
- robotics
- machine learning
Foundations / 13 min read
Model uncertainty: bound a robot joint’s acceleration
Turn uncertain inertia, damping, and disturbance torque into an acceleration range. Compare a nominal command with an actual model, handle negative acceleration correctly, and understand the assumptions behind a worst-case bound.
- robotics
- machine learning
Foundations / 14 min read
Feedback control: measure speed and correct the error
Explore feedback control with a robot joint speed model. Compare proportional correction with feedforward alone, calculate steady error, and test disturbances, sensor bias, and torque limits.
- robotics
- machine learning
Foundations / 14 min read
Open-loop and closed-loop control: compare plans with position feedback
Explore open-loop and closed-loop control by moving an axis along a planned path. Calculate how gain errors, drift, initial position, and sensor bias change tracking, then connect or disconnect the feedback path.
- robotics
- machine learning
Foundations / 14 min read
Transfer functions: predict joint speed from torque
Derive a robot joint transfer function with the Laplace transform. Separate zero-state and natural responses, calculate a pole and time constant, and compare torque steps with pulses.
- robotics
- machine learning
Foundations / 15 min read
Block diagrams: trace signals and derive the feedback loop
Read control block diagrams by naming signals and checking each junction. Combine series and parallel paths, derive feedback transfer functions, and compare reference, disturbance, and sensor-error responses.
- robotics
- machine learning
Foundations / 13 min read
Poles and zeros: connect root locations to a step response
Explore poles and zeros by changing one numerator zero in a stable second-order system. Calculate inverse response, compare real and complex poles, and distinguish canceled factors from hidden internal modes.
- robotics
- machine learning
Foundations / 12 min read
Stability criteria: find the feedback gain limit
Connect the Routh-Hurwitz criterion, Nyquist stability test, and gain and phase margins. Find when a three-lag feedback system settles, sustains oscillation, or becomes unstable.
- robotics
- machine learning
Foundations / 12 min read
Step response characteristics: measure rise, overshoot, and settling
Read step response characteristics from an exact second-order model. Compare rise time, overshoot, settling time, and steady error without mistaking the end of a plot for the final value.
- robotics
- machine learning
Foundations / 13 min read
PID control: build a command from error and measured motion
Build a PID controller from proportional, integral, and derivative terms. Compare tracking and load rejection, calculate each command contribution, and check the stability limit of integral gain.
- robotics
- machine learning
Foundations / 13 min read
PID tuning: calculate gains, then test the response
Tune a PID controller against explicit response and effort targets. Convert Ziegler–Nichols settings into parallel gains, understand relay auto-tuning, and compare manual adjustments.
- robotics
- machine learning
Foundations / 11 min read
Integral windup: what happens when the actuator runs out of effort
Explore integral windup in a sampled PI controller. Compare requested effort, actuator limits, and integral memory as an impossible reference returns to a feasible value.
- robotics
- machine learning
Foundations / 11 min read
Derivative kick and filtering: choose what D responds to
Explain derivative kick and filtering with a target step and measured noise. Compare derivative on error with derivative on measurement, calculate filtered command peaks, and weigh noise gain against lag.
- robotics
- machine learning
Foundations / 12 min read
Feedforward control: predict torque, then correct error
Calculate model-based torque from a smooth speed reference, then add feedback correction. Compare feedforward, proportional feedback, and their combination under inertia error, drag error, and unknown external torque.
- robotics
- machine learning
Foundations / 12 min read
Cascade control: let position request velocity and velocity request torque
Build cascade control from nested position and velocity loops. Follow command units, compare finite inner dynamics with ideal velocity tracking, and calculate the effect of an opposing load.
- robotics
- machine learning
Foundations / 12 min read
Bode plots and loop shaping: place gain and phase together
Read Bode plots and shape a position-control loop with a lead controller. Calculate crossover and phase margin, compare closed-loop tracking, and check what the model leaves out.
- robotics
- machine learning
Foundations / 12 min read
Control bandwidth: tracking speed and physical limits
Calculate closed-loop bandwidth relative to DC gain, then compare sinusoidal tracking with the torque it requires. Separate bandwidth from loop crossover and account for resonance, delay, sampling, and noise.
- robotics
- machine learning
Foundations / 11 min read
Controller discretization: sample, compute, and hold
Explore controller discretization with exact held-input dynamics. Compare immediate and delayed commands, calculate discrete poles, and connect sample timing to control stability.
- robotics
- machine learning