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Found 6 papers, 1 papers with code
Feedforward Control in the Presence of Input Nonlinearities: A Learning-based Approach
Advanced feedforward control methods enable mechatronic systems to perform varying motion tasks with extreme accuracy and throughput.
Cross-Coupled Iterative Learning Control for Complex Systems: A Monotonically Convergent and Computationally Efficient Approach
Cross-coupled iterative learning control (ILC) can achieve high performance for manufacturing applications in which tracking a contour is essential for the quality of a product.
Is Vanilla Policy Gradient Overlooked? Analyzing Deep Reinforcement Learning for Hanabi
In pursuit of enhanced multi-agent collaboration, we analyze several on-policy deep reinforcement learning algorithms in the recently published Hanabi benchmark.
Position-Dependent Snap Feedforward: A Gaussian Process Framework
Position-dependent compliance is compensated for by using a Gaussian process to model the snap feedforward parameter as a continuous function of position.
Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder
Mechatronic systems have increasingly stringent performance requirements for motion control, leading to a situation where many factors, such as position-dependency, cannot be neglected in feedforward control.
Learning nonlinear feedforward: a Gaussian Process Approach Applied to a Printer with Friction
Feedforward control is essential to achieving good tracking performance in positioning systems.
