Feed forward
Related papers: 20
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Feedforward control is a proactive control strategy that uses a model of the system to compute and apply control actions in advance, rather than waiting for errors to occur before responding. Unlike feedback control, which reacts to measured deviations, feedforward anticipates disturbances or desired trajectories and generates compensating commands preemptively based on known system dynamics or learned models. In robotics and AI, feedforward control is widely applied in manipulator trajectory tracking, exoskeleton assistance, force control, vibration suppression, and neural network-based learning controllers. A robot arm, for example, might use a dynamic model to precompute the torques needed for a desired motion, with feedback handling residual errors. Neural networks can serve as feedforward components that approximate complex nonlinear dynamics, enabling accurate real-time control without full analytical models. Feedforward control matters because it dramatically improves tracking performance and responsiveness, especially for fast or complex motions where feedback alone introduces unacceptable lag or instability. By reducing the burden on feedback loops, it enables smoother, more energy-efficient operation and is essential for high-performance tasks such as human-robot interaction, prosthetic limb control, and precision manufacturing.
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