Many industrial motion systems require performing a variety of tasks with high precision and safety. Iterative learning control (ILC) is a method with convergent update laws, generally classified into: 1) parametrized learning approach for achieving task-flexibility against varying tasks; or 2) signal-based learning approach which can achieve perfect tracking-performance for repeating tasks. The aim of this study is to join the distinct ILC frameworks, achieving all desirable properties in a single framework. Specifications on convergence, tracking-performance and task-flexibility of the developed joint parametrized/signal-based ILC are theoretically derived, confirmed with experimental results on a two-mass system.

Next-generation high-precision mechatronic systems require safe and precise control of unmeasurable states. State-tracking iterative learning control (ILC) can achieve extremely high state-tracking performance up to the performance of state estimation, with convergence guaranteed apriori through the frequency-domain characteristics of the state estimator. The aim of this study is to develop a noncausal state estimation framework with verifiable frequency-domain characteristics. In batch-operated systems such as ILC, the use of noncausal design leads to substantial performance improvements that surpass the fundamental limits of causal approaches. Furthermore, by analytically verifying the frequency-domain characteristics of the noncausal state estimator, the developed framework retains the benefit of guaranteeing convergence in ILC. The developed framework is validated both by simulation and experiment, confirming improved state-tracking with monotonic convergence of ILC, achieved by exploiting noncausality in state estimation.

Iterative learning control yields accurate feedforward input by utilizing experimental data from past iterations. However, typically there exists a tradeoff between task flexibility and tracking performance. This study aims to develop a learning framework with both high task-flexibility and high tracking-performance by integrating rational basis functions with frequency-domain learning. Rational basis functions enable the learning of system zeros, enhancing system representation compared to polynomial basis functions. The developed framework is validated through a two-mass motion system, showing high tracking-performance with high task-flexibility, enhanced by the rational basis functions effectively learning the flexible dynamics.

Iterative learning control (ILC) techniques are capable of improving the tracking performance of control systems that repeatedly perform similar tasks by utilizing data from past iterations. The aim of this paper is to achieve both the task flexibility enabled by ILC with basis functions and the performance of frequency-domain ILC, with an intuitive design procedure. The cost function of norm-optimal ILC is determined that recovers frequency-domain ILC, and consequently, the feedforward signal is parameterized in terms of basis functions and frequency-domain ILC. The resulting method has the performance and design procedure of frequency-domain ILC and the task flexibility of basis functions ILC, and are complimentary to each other. Validation on a benchmark example confirms the capabilities of the framework.

Iterative learning control (ILC) yields substantial performance improvement for repetitive motion tasks. While task-flexibility for non-repetitive motion tasks can be achieved with the use of basis functions, this typically comes with a trade-off in performance or design parameters. This study aims to achieve both task-flexibility and high performance with a single time-domain optimization framework. By defining a criterion combining the cost for performance and task-flexibility, an optimal feedforward with task-flexibility of basis function ILC and high performance surpassing standard norm-optimal ILC is obtained. Numerical validation on a two-mass motion system confirm the capabilities of the developed framework.

State-tracking Iterative Learning Control (ILC) yields perfect state-tracking performance at each n sample instances for systems that perform repetitive tasks, where n stands for the order of the system. By achieving perfect state-tracking, oscillatory intersample behavior often encountered in output-tracking ILC has been mitigated. However, state-tracking ILC only assures the estimated state error to converge to a significantly small value, meaning the accuracy of the state estimation takes a critical role. State estimation using a causal state observer has had an inevitable trade-off between the estimation delay and the noise sensitivity. By utilizing the non-causal operation of ILC, a non-causal state estimation can be designed. This non-causal state estimation performs beyond the trade-off of causal estimation, improving the estimation delay without compromising the noise sensitivity. The aim of this paper is to implement the non-causal state observer to state-tracking ILC, and present the improved state tracking by applying it to a second order system.