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add links to ILC.jl
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docs/src/examples/ilc.md

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# Iterative-Learning Control
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In this example, we will design an [Iterative-Learning Control (ILC)](https://en.wikipedia.org/wiki/Iterative_learning_control) iteration scheme. ILC can be thought of as a simple reinforcement-learning strategy that is suitable in situations where a *repetitive task* is to be performed multiple times, and disturbances acting on the system are also repetitive and predictable but unknown. Multiple versions of ILC exists, in this tutorial we will consider a heuristic scheme as well as a model-based scheme.
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!!! note
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See the Julia package [IterativeLearningControl2.jl](https://baggepinnen.github.io/IterativeLearningControl2.jl/dev/) for implementations of several different ILC algorithms.
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## Algorithm
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The ILC iteration scheme typically looks something like this (many variants exists), at ILC iteration $k$:

docs/src/index.md

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- [ControlSystemIdentification.jl](https://github.com/baggepinnen/ControlSystemIdentification.jl) is a system-identification toolbox for identification of LTI systems using either time or frequency-domain data. This package can use data to estimate statespace models, transfer-function models and Kalman filters that can be used for control design.
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- [ControlSystemsMTK.jl](https://juliacontrol.github.io/ControlSystemsMTK.jl/dev/) is an interface between ControlSystems.jl and [ModelingToolkit.jl](https://mtk.sciml.ai/stable/).
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- [DiscretePIDs.jl](https://github.com/JuliaControl/DiscretePIDs.jl) contains a reference implementation in Julia of a discrete-time PID controller including set-point weighting, integrator anti-windup, derivative filtering and bumpless transfer.
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- [IterativeLearningControl2.jl]([IterativeLearningControl2.jl](https://baggepinnen.github.io/IterativeLearningControl2.jl/dev/)) Implementations of several different ILC algorithms with a unified interface.
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See also [the paper](https://portal.research.lu.se/en/publications/controlsystemsjl-a-control-toolbox-in-julia) describing the toolbox
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