Adaptive Iterative Learning Control With Termination Condition for MASs Performing Multiple Tasks

doi:10.1109/TASE.2024.3519636

Adaptive Iterative Learning Control With Termination Condition for MASs Performing Multiple Tasks

2025-01-01

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DOI：10.1109/TASE.2024.3519636

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This paper investigates an adaptive iterative learning control (AILC) method for multiagent systems (MASs) performing multiple tasks. Different from traditional results for the single task, a multiple tasks case is considered in this work, which can complete various cooperative control. It should be pointed out that only one of the multiple tasks is performed in each iteration. For multiple tasks, a neural network (NN) is employed to create a mapping relationship between the input and output of nonlinearity, which is integrated into AILC to improve the control input. Additionally, an auxiliary signal is developed to compensate for the residual error caused by NN approximation and differentiator estimation. Then, a termination condition including mean square error and desired performance is established for the AILC method. By utilizing Lyapunov stability theory, it is proven that the tracking error converges to zero without termination condition and satisfies the desired accuracy with termination condition. In the simulation, multiple single-link manipulators are used to perform three different cooperative control tasks to validate the effectiveness of the proposed approach.

Keyword:

Accuracy

Artificial neural networks

Iterative learning control

Vectors

Topology

Electronic mail

Uncertainty

Trajectory

Process control

Multi-agent systems

Adaptive iterative learning control (AILC)

multiagent systems (MASs)

multiple tasks

neural network (NN)

termination condition

Adaptive Iterative Learning Control With Termination Condition for MASs Performing Multiple Tasks

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