Feedback Systems¶

Course Overview¶

• Institution: MIT
• Course code: 6.302
• Track: Control Systems
• Tier: A
• Role: Mainline
• Level: Not standardized by provider (use prerequisites)
• Last reviewed: 2026-07-28

MIT's Feedback Systems builds a feedback-control spine through problems, labs, and exams, while missing primary text notes and hardware-specific labs require substitute readings and a replacement bill of materials.

Why choose this course

Mainline course. A reliable option that can serve as a main course or strong alternative.

Before you start

• Recommended foundation: Signals and Systems
• Recommended foundation: Engineering Mathematics

Verifiable learning outcomes

• Explain the core models in Control Systems, including their assumptions and limits
• Solve representative derivations and problems, checking units, limiting cases, or numerical results
• Complete a reproducible experiment or implementation with raw data, parameters, versions, and verification

Workload and pacing

13 weeks at 11 hours/week. This maintainer planning estimate is derived from course role and the density of public practice and labs; it is not a provider workload promise. Pilot two weeks while logging instruction, practice, lab, and review time, then adjust the remaining plan when actual effort differs by more than 25%.

Safety level

Low energy. Keep work isolated, current-limited, and low energy; verify ratings, grounding, short-circuit risk, and emergency shutdown before power-up.

Course Resources¶

Software, hardware, and cost

Software

• Maintainer-suggested open-source/free verification path: Python 3, Jupyter, python-control, SciPy, and GNU Octave
• The resource inventory lists public code coverage; pin interpreter, dependencies, toolchain, datasets, and PDK versions where applicable

Hardware

• The resource inventory lists lab coverage; prefer borrowing or sharing the following equipment: a course-supported low-voltage plant, sensors, actuators, real-time controller, and emergency shutdown. Verify ratings, authorization, and safety conditions only after the provider lab manual explicitly calls for them

Cost note

The suggested software stack is available open source or free; this is not a provider requirement or bill of materials. The actual boards, components, fabrication, and instruments—and their costs—depend on the provider lab manual, region, and local availability; prefer simulation, borrowing, or sharing before purchase.

Public resource coverage

Resources and access

“Listed by official page” means the link was discovered on a successfully fetched official source on the verification date; it does not guarantee that every region or account can open the target directly. Access does not grant redistribution rights. Re-check the provider page, target link, and third-party notices before downloading, adapting, or publishing material.

Practice and Verification¶

Practice loop

Feedback Systems · MIT 6.302: Robust Closed-Loop and Model-Mismatch Audit

This is a maintainer-suggested self-study project for Feedback Systems · MIT 6.302, not an official course assignment. Design baseline and improved controllers for a simulated plant in Control Systems, quantifying stability, tracking, disturbance rejection, saturation, and parameter mismatch.

Origin: Maintainer-suggested project

Deliverables

• Plant equations, parameter ranges, actuator and sensor constraints, and control metrics
• Open-loop model, baseline controller, improved controller, and simulation tests
• Raw state, control, and metric data for step, disturbance, noise, and parameter sweeps
• A report comparing stability margins, overshoot, settling time, energy, and failure regions

Verification

• Keep the nominal closed loop stable and meet predeclared overshoot and settling-time thresholds
• Cover zero reference, maximum reference, actuator saturation, sample delay, and parameter extremes
• Cross-check time-domain results with pole or frequency margins or a Lyapunov argument
• Increase parameter mismatch until the first instability and report the stability boundary and safe degraded behavior

Reproducibility

• Commit model, controller, scenario, test, analysis, and plotting sources
• Pin solver, step size, parameters, random seeds, and controller version
• Preserve raw trajectories, controller configurations, and the generated report

Safety boundary: Simulation only — Control simulated plants only; do not deploy the exercise controller to real motors, vehicles, drones, medical, chemical, or power equipment.

Risks, gaps, and boundaries

Primary textbook notes are not public, and the motor, thermal, and op-amp laboratories need a substitute BOM.

Completion evidence

• Weekly learning log with time, questions, corrected errors, decisions, next steps, and links to that week's reproducible artifacts
• Theory dossier with explicit assumptions, notation, derivation, units, and boundary conditions, checked by at least one independent method
• Simulation package with model or netlist, inputs, solver and version, parameter-sweep script, benchmark comparison, expected results, and one rerun command
• Code repository with pinned dependencies and toolchain, a minimal run command, tests or waveform/benchmark checks, expected output, and license notes
• Experiment package with schematic/setup, calibration record, raw data, uncertainty, safety checks, failed runs, and steps to rebuild plots from raw data