Tracking error
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Tracking error is the difference between a robot's or control system's desired reference trajectory and its actual measured output at any given point in time. In robotics and AI, it serves as the fundamental performance metric for evaluating how precisely a system follows commanded motions, forces, or positions. Controllers — including adaptive, sliding mode, neural network-based, and iterative learning approaches — are specifically designed to minimize this error, often with guarantees on convergence speed, boundedness, or prescribed performance constraints. Tracking error signals drive feedback corrections in real time, allowing systems to compensate for model uncertainties, external disturbances, and nonlinear dynamics. It matters because even small persistent tracking errors can compromise task quality in applications such as surgical robotics, manufacturing, and autonomous navigation, where precision is critical. Reducing tracking error to near zero, while maintaining stability and robustness, is therefore a central objective across virtually all motion control research and practical robotic system design.
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