Week 8 — Control Systems Implementation
Day 1 of 7 | Difficulty: ⭐⭐⭐
What Is a Robot Control System?
A robot control system is the decision-making layer that converts what the robot should do (goals, trajectories) into what the motors should do (torques, velocities, positions). It closes the loop between sensing, planning, and actuation.
Without control, even a perfectly designed robot is just an expensive statue. Control is what makes robots adapt, correct, and execute.
The Control Hierarchy
Robots operate at multiple timescales simultaneously. Think of it as a corporate org chart:
Level 1: Task Planning (100ms–1s)
What: Decide what to do.
Examples: “Navigate to the kitchen.” “Pick up the red box.”
Algorithms: Path planning (A*, RRT), task scheduling, behavior trees.
Level 2: Trajectory Generation (10ms–100ms)
What: Compute how to move.
Examples: Joint angles as a function of time, Cartesian waypoints.
Algorithms: Interpolation, splines, inverse kinematics.
Level 3: Motion Control (1ms–10ms)
What: Track the trajectory.
Examples: PID on joint position, torque control.
Algorithms: PID, LQR, MPC, impedance control.
Level 4: Motor Control (0.1ms–1ms)
What: Drive the motor.
Examples: PWM generation, current limiting, commutation.
Hardware: Motor drivers, ESCs, servo controllers.
Key insight: Each level feeds the one below. A task planner sets a goal, the trajectory generator creates a path, the motion controller tracks it, and the motor driver executes.
The Feedback Loop
At the heart of every control system is the feedback loop:
Reference (desired) → [Controller] → Actuator → Plant (robot)
↑ |
└──── [Sensor] ←──────────┘
- Reference: Where we want to be (target position, trajectory).
- Controller: Computes the correction (PID, LQR, etc.).
- Actuator: Executes the command (motor, hydraulic piston).
- Plant: The physical system being controlled (robot arm, mobile base).
- Sensor: Measures the actual state (encoder, IMU, camera).
Error = Reference − Measured State. The controller’s job is to drive this error to zero.
Control Strategies: A Taxonomy
| Strategy | When to Use | Pros | Cons |
|---|---|---|---|
| Open-loop | Cheap systems, known dynamics | Simple, no sensors | No correction for disturbances |
| PID | Most common; position/velocity control | Simple, robust, tunable | Struggles with complex dynamics |
| LQR | Linear systems, optimal trade-offs | Optimal gains, handles MIMO | Requires linearization, full state |
| MPC | Constrained systems, preview | Handles constraints, optimal | Computationally heavy |
| Impedance/Force | Contact tasks (assembly, surgery) | Safe interaction, compliant | Needs force sensing |
Why ROS2 Matters for Control
ROS2 (Robot Operating System 2) is the industry-standard middleware for robot software. It provides:
- Pub/sub communication: Sensors publish data, controllers subscribe, actuators receive commands.
- Real-time capabilities: ROS2 supports hard real-time control loops (with appropriate DDS and kernel tuning).
- Modularity: Swap controllers, sensors, or actuators without rewriting the whole stack.
- Tooling: RViz for visualization, rosbag for logging, Gazebo for simulation.
This week: We’ll build control nodes in ROS2, integrate real (simulated) sensors, and close the loop.
Preview: This Week’s Journey
| Day | Topic | Output |
|---|---|---|
| 1 | Control system architecture | Mental model of the hierarchy |
| 2 | PID design and tuning | Working PID controller |
| 3 | State-space control | LQR design principles |
| 4 | ROS2 control architecture | Nodes, topics, launch files |
| 5 | Sensor fusion | EKF for state estimation |
| 6 | Python practice | Full control loop in ROS2 + Gazebo |
| 7 | Week summary | Key takeaways + Week 9 preview |
Key Takeaway
Control systems are the nervous system of a robot. They take high-level intentions, break them into real-time commands, and use sensor feedback to correct errors. ROS2 is the framework that binds these layers together.
Next: Day 2 — PID Controller Design and Tuning