The essential idea discussed here is to enhance the iterative learning scheme with neural networks applied for controller synthesis as well as for system output prediction. Consequently, an iterative control update rule is developed through efficient data-driven scheme of neural network training.
The neural networks are used to estimate the learning gain of an iterative learning law and to store the learned control input profiles for different reference ...
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Sep 3, 2020 · Experimental results on a robot arm show that the proposed NN-ILC method can easily realize the ILC of multiple tasks.
Dec 1, 2023 · This paper proposes a data-driven NN-based ILC algorithm to address the trajectory tracking control problem of nonlinear repetitive discrete-time SISO systems ...
This paper proposes an offline learning method using a neural network which exploits this dataset to approximate the converged ILC for a nonlinear system. The ...
Subsequently, a neural network is utilized to learn the dynamics of the unknown open-loop system, even in cases where it could be unstable. The iterative ...
May 29, 2022 · Here we present a solution called learning of iterative learning control (ILC) based on neural networks. It is able to recommend control parameters for ILC ...
An iterative training radial basis function (RBF) neural network is developed to estimate the effect of initial condition on terminal output and to learn the ...
Dec 19, 2023 · In this paper, the problem of adaptive iterative learning based consensus control for periodically time-varying multi-agent systems is studied
The neural network is used to identify the positive model of the nonlinear system on iterative axis, which can give feed forward actions of iterative learning ...