Skip to main content

What is Control Theory?

In the realm of robotics, control theory refers to a set of techniques and principles that enable us to direct and manage the movements and actions of robots. It involves using math-based strategies to develop instructions and commands that guide a robot's behavior. Essentially, control theory is the science that allows us to control robots' motions, positions, and responses, making them perform tasks accurately and efficiently.

How is it used in FTC Robotics?​

Control theory plays a pivotal role in guiding the actions and behaviors of robots in FTC (FIRST Tech Challenge) robotics. It involves utilizing mathematical concepts and algorithms to manage and regulate the motion, positioning, and performance of robots. This tutorial will provide an accessible overview of control theory in the context of FTC robotics.

Key Concepts of Control Theory​

  1. Control Loops: At the heart of control theory are control loops. These loops continuously assess the current state of the robot, compare it to the desired state, and make necessary adjustments to bridge the gap between the two. The cycle of sensing, processing, and actuating defines the actions of the robot.
  2. Feedback and Error: Feedback is the process of obtaining information about the robot's actual state, such as its position or speed. Error is the difference between the desired state and the actual state. Control theory aims to minimize this error by making precise adjustments to the robot's behavior.
  3. Proportional-Integral-Derivative (PID) Control: PID control is a common control strategy in FTC robotics. It uses three components – Proportional, Integral, and Derivative – to calculate control outputs. The Proportional term corrects the present error, the Integral term corrects accumulated past errors, and the Derivative term anticipates future errors.

Applications​

  1. Drive Train Control: In autonomous and teleoperated modes, control theory helps maintain consistent and accurate movement of the robot. It ensures the robot reaches its intended destination with minimal deviations.
  2. Positional Control: Control theory is crucial for tasks that demand precise positioning, such as grabbing game elements or aligning with specific locations on the field. It ensures the robot reaches the desired positions accurately. a. A very common example of this is controlling your motor's velocity or position through the use of encoder measurements.
  3. Stability and Responsiveness: Control theory contributes to the stability of the robot's motions. It prevents erratic behavior and promotes smoother actions, ensuring that the robot performs reliably and predictably.

Implementing Control Theory in FTC Robotics​

  1. Sensors: To gather information about the robot's state, various sensors are employed, such as encoders to measure wheel rotations, gyros for orientation, and cameras for visual feedback.
  2. Programming: Control theory is implemented through programming. Robot control algorithms use sensor data and mathematical formulas to calculate control outputs that determine motor speeds, directions, and other actions.
  3. Tuning: Proper tuning of control parameters is crucial for effective implementation. This involves adjusting values like PID constants to achieve the desired level of accuracy and responsiveness.

Benefits of Control Theory​

  1. Consistency: Control theory ensures that the robot consistently performs tasks with minimal deviation, leading to improved accuracy during both autonomous and teleoperated modes.
  2. Adaptability: By continuously adjusting to changing conditions, control theory enables robots to respond effectively to unexpected challenges on the field.
  3. Efficiency: Using control theory allows for efficient resource utilization, minimizing energy consumption, and maximizing the robot's battery life.

Control theory forms the backbone of precise and reliable robot behaviors in FTC robotics. By understanding the fundamental concepts and applying them to programming and tuning, teams can enhance their robot's capabilities and achieve higher levels of success in competitions. Remember that practice and experimentation are key to mastering control theory and optimizing robot performance.