Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Convex Optimization
Multi-input square iterative learning control with input ratelimits and bounds
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Brief On the design of ILC algorithms using optimization
Automatica (Journal of IFAC)
Iterative learning control design based on composite energy function with input saturation
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Iterative learning control in optimal tracking problems with specified data points
Automatica (Journal of IFAC)
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We consider the problem of synthesis of iterative learning control schemes for linear systems with saturation constraints. The problem of minimizing the tracking error is formulated as a constrained convex optimization problem, namely a linearly constrained quadratic program. Due the lack of information regarding the disturbances in the process, descent directions cannot be determined without running experiments. This in turn leads to strict limitations on the number of iterations employed in any iterative optimization scheme. Motivated by this fact, we implement an interior point algorithm, specifically the barrier method. The method is demonstrated on a prototype wafer stage testbed and its performance is compared to other existing methods.