Sampling interval

About

The sampling interval is the time period between consecutive discrete measurements or control updates in a digital system. In robotics and AI, it defines how frequently a controller reads sensor data, executes computations, and issues commands to actuators — essentially setting the rhythm of a digital control loop. A shorter sampling interval captures faster system dynamics more accurately but demands greater computational resources, while a longer interval reduces processing load at the risk of missing critical changes in system state. The choice of sampling interval critically affects stability, tracking performance, and robustness in applications such as robot manipulator control, iterative learning control, and multi-agent coordination. In sampled-data systems, the sampling interval also interacts with communication delays and uncertainties, making its careful selection essential for guaranteeing safe and reliable operation. For engineers designing real-time robotic systems, properly tuning the sampling interval is a foundational step that directly influences controller bandwidth, discretization error, and overall closed-loop behavior.