Setpoint variation in iterative learning schemes
نویسندگان
چکیده
Iterative Learning Control (ILC) in motion control systems is often hampered by the fact that variations in the desired setpoint trajectory are not accounted for. When the setpoint changes, different dynamics are excited causing the learned signal to be less effective in reducing the tracking error. In this paper, two methods to deal with setpoint variation are discussed. In the first method, the learned feedforward signal is decomposed in different force tables corresponding to particular parts of the setpoint. The second method utilizes the learned data from different setpoints to create a finite impulse response (FIR) mapping between setpoint and feedforward input. The potential of both methods is demonstrated through experiment on a lithographic motion system.
منابع مشابه
Cogging Compensating Piecewise Iterative Learning Control for variable setpoints with application to a wafer stage
Iterative Learning Control (ILC) is an effective control technique for motion systems that perform repetitively the same trajectory (setpoint), e.g. a wafer stage. The result of the learning procedure is a feedforward signal that perfectly compensates all deterministic dynamics in the system for the learned setpoint performed at a specific start position. For other setpoints and start positions...
متن کاملSecond Order Iterative Learning Control for Scale Varying Setpoints
Iterative learning control (ILC) [1] can achieve a high performance for repetitive setpoints. However, for different setpoints ILC should start over. In this work point-to-point movements rk(t) with different magnitudes are considered which are constructed by scaling a nominal setpoint r(t), i.e., rk(t) = Tkr(t). A second order ILC (SOILC) [2] method is developed to accurately track these setpo...
متن کاملOn new faster fixed point iterative schemes for contraction operators and comparison of their rate of convergence in convex metric spaces
In this paper we present new iterative algorithms in convex metric spaces. We show that these iterative schemes are convergent to the fixed point of a single-valued contraction operator. Then we make the comparison of their rate of convergence. Additionally, numerical examples for these iteration processes are given.
متن کاملPerformance Assessment Measures of Batch Processes for Iterative Learning Control
A new method is proposed for the assessment of the batch control system when the iterative learning control is applied. Unlike the continuous process, the performance assessment of the batch process requires particular attention to both disturbance changes and setpoint changes. Because of the intrinsically dynamic operations and the nonlinear behavior of batch processes, the conventional approa...
متن کاملRational basis functions in iterative learning control - With experimental verification on a motion system
Iterative learning control (ILC) approaches often exhibit poor extrapolation properties with respect to exogenous signals, such as setpoint variations. This brief introduces rational basis functions in ILC. Such rational basis functions have the potential to both increase performance and enhance the extrapolation properties. The key difficulty that is associated with these rational basis functi...
متن کامل