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Adaptive Iterative Learning Control With Termination Condition for MASs Performing Multiple Tasks
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AI
DOI：10.1109/TASE.2024.3519636.png)
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Abstract
En 中文
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.
Keywords:
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
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