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

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. Note to Practitioners—Most of the existing AILC methods are investigated to perform a single task for MASs. However, practical applications of MASs, such as manipulators, unmanned vehicles, and aerospace, often require the ability to perform multiple tasks. One motivation is to effectively learn control experience from different tasks. To achieve this, an NN is designed to learn experiences, which are further incorporated into AILC method to perform multiple tasks. In addition, due to limitations in computing resources, controllers cannot be learned indefinitely in practical applications. To address this challenge, a termination condition is established to stop learning when the desired performance is met. Based on the above considerations, the proposed AILC method with termination condition can reduce computational resources and flexibly perform multiple tasks in practical applications.