Section outline

  • PID Control Basics

    Proportional-Integral-Derivative (PID) control is one of the most widely used feedback control techniques in robotics and automation. It is used to ensure smooth, stable, and accurate motion in autonomous systems like mobile robots, robotic arms, and drones.

    • Understanding the PID Components

      • Proportional (P): The proportional term applies a correction based on the current error (difference between desired and actual position or speed). A higher proportional gain makes the system respond faster but can also introduce instability if too large.
      • Integral (I): This term considers the accumulation of past errors. It helps the system eliminate small steady-state errors but can lead to overshooting if not tuned properly.
      • Derivative (D): The derivative term looks at the rate of change of error. It dampens the system response, preventing overshoot and improving stability during quick changes.

    • Example Application – Line Follower Robot

      In a line follower robot, a PID controller can be used to correct the steering angle based on how far off the robot is from the center of the line. This ensures the robot follows curves smoothly without oscillating or going off-track.

      // Pseudocode for PID in line following
error = desired_position - current_position
integral += error
derivative = error - previous_error
output = Kp * error + Ki * integral + Kd * derivative
adjustMotors(output)
previous_error = error

      Tuning a PID Controller

      Tuning involves setting the values of Kp, Ki, and Kd for optimal performance. Techniques include:

      • Manual Tuning: Start with Kp, increase until it oscillates, then adjust Ki and Kd to stabilize.
      • Ziegler-Nichols Method: A systematic way of determining values by inducing oscillation and calculating from critical gain and period.

      Common Issues in PID Control

      • Too much Kp: Causes oscillation and overshooting.
      • Too much Ki: Can cause slow response or overshoot due to integral windup.
      • Too much Kd: May cause noise sensitivity or jitter in motor response.

      PID in Real-World Robotics

      PID is used in balancing robots, self-driving cars, quadcopters, and anywhere precise control is required. When combined with sensor feedback (like IMUs and encoders), it enables intelligent decision-making and real-time adjustments.

      Mastering PID logic is essential for developing stable and responsive autonomous robots. It forms the foundation for more complex control algorithms and adaptive tuning techniques in advanced robotics.