Predictor-Based Adaptive Iterative Learning Control of MASs With Distributed Error Compensation

This paper proposes an adaptive iterative learning control (AILC) scheme for multiagent systems (MASs) to improve the containment control performance. To deal with the uncertain nonlinearity, a neural network (NN)-based iterative predictor with the same structure of MASs is constructed, where an auxiliary approximation term is further incorporated to handle the NN reconstruction error. By utilizing the output of iterative predictor, an AILC-based containment control scheme is designed under the backstepping framework. To overcome the initial error problem, distributed compensation signals are developed to compensate for containment errors and dynamic surface errors. Instead of using dynamic information of neighbors, only the output of neighbors is used in each follower such that the communication load is reduced. It is established that as iteration index approaches infinity, the iteratively convergence of containment errors is achieved, and the stability of MASs is ensured. Simulation results on manipulators verify the effectiveness of both the iterative predictor and the adaptive iterative learning containment controller. Note to Practitioners—This paper is motivated by practical applications of AILC-based containment control for MASs, such as robot collaboration, intelligent transportation, smart grids, etc. Distributed containment control allows each agent to communicate only with its neighbors, thereby reducing unnecessary communication overhead. However, the overall communication network is unknown for each agent, which leads to the inevitable initial containment error. To address this issue, a distributed compensation signal is introduced to adjust the containment error. Additionally, an NN-based iterative predictor is designed to indirectly modify the control input to improve control performance. Since the transmission and reception of dynamic information from neighbors occupy communication resources, the proposed method can only utilize the output of neighbors to design the distributed containment controller. This approach further reduces communication load, which makes the AILC-based containment control method more flexible and suitable in practical applications.