Control Theory for Wet-Lab Robotics: From Feedback to Autonomous Experiments

Explain how control theory connects software decisions to physical robot and wet-lab behavior; model simple systems, interpret stability and tracking performance, choose and tune controllers in simulation, distinguish measured quantities from inferred outcomes, and design a reliable automated laboratory workflow. Begin with basic algebra and graphs, introduce calculus and linear algebra intuitively, and offer optional Python exercises. Teach each lesson through a wet-lab example, a worked example, a misconception check, and a short application exercise. Progress from foundations to intermediate applications, with clearly marked advanced extensions and a simulated capstone.

8 sections ยท 32 lessons

Course outline

1. See the Lab as a Feedback System

  1. Why a Robot Command Is Not a Guaranteed Outcome
  2. Open Loop, Feedback, and Feedforward
  3. The Math You Need, Introduced Visually
  4. What Good Control Looks Like

2. Model Motion, Heat, and Liquids

  1. States, Inputs, Outputs, and Hidden Variables
  2. First-Order Dynamics and Transport Delay
  3. Second-Order Dynamics and Robot Oscillation
  4. Identify a Model and Check Its Limits

3. Design and Tune Feedback Controllers

  1. Proportional Control and Stability
  2. PID: Correct the Present, Past, and Trend
  3. Tune Against Real Constraints
  4. Sampling, Delay, Noise, and Robustness

4. Apply Control Theory to Robot Motion and Contact

  1. From a Well Coordinate to Joint Motion
  2. Track a Trajectory Without Spilling
  3. Handle Labware with Force and Compliance
  4. Use Vision and Sensors to Correct Robot Error

5. Control the Wet-Lab Process

  1. Pipetting: Fluid Behavior Meets Motion Control
  2. What Liquid-Handling Sensors Actually Tell You
  3. Verify Volume and Learn from Transfer Errors
  4. Temperature, Mixing, pH, and Dissolved Oxygen

6. Estimate Hidden States and Handle Constraints

  1. State Space, Controllability, and Observability
  2. State Estimation and Kalman Filtering
  3. Optimal Control and Model Predictive Control
  4. Adaptive Control and Learning: What Changes Online

7. Coordinate Reliable and Autonomous Experiments

  1. Three Loops: Servo, Protocol, and Experiment
  2. Protocols as State Machines with Recovery
  3. Detect Faults and Preserve Experimental Meaning
  4. How a Self-Driving Lab Chooses the Next Experiment

8. Capstone: Design a Simulated Automated Lab

  1. Specify a Liquid-Transfer and Heating Workcell
  2. Build the Model and Compare Controllers
  3. Inject Failures and Test Recovery
  4. Defend the Design and Evaluate Real Systems

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