Recursive Learning of Feedforward and Compliance Compensation Parameters for Precision Motion Systems
For precision motion systems with time-varying or position-dependent behavior, this work provides a method to improve control performance by leveraging online data.
The paper presents a recursive algorithm for simultaneously learning feedforward and compliance compensation parameters in precision motion systems, achieving an order-of-magnitude improvement in servo performance on a semiconductor metrology and inspection system.
To meet the stringent requirements of future motion systems exhibiting time-varying and/or position-dependent behavior, online data must be leveraged to improve control performance. This paper presents a recursive algorithm for simultaneous learning of feedforward and compliance compensation parameters. A multivariate regression formulation is proposed that jointly estimates friction, mass, jerk, and compliance compensation parameters while mitigating parameter coupling. Experimental results on a high-tech semiconductor metrology and inspection system demonstrate an order-of-magnitude improvement in servo performance.