期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 53, 期 2, 页码 1184-1194出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2021.3110645
关键词
Vehicle dynamics; Observers; Convergence; Task analysis; Nonlinear dynamical systems; Artificial neural networks; Process control; Adaptive neural network (NN) control; discrete-time nonlinear systems; dynamic learning; formation control; multiagent systems (MASs)
This article investigates the leader-follower formation learning control problem for discrete-time strict-feedback multiagent systems. A two-layer control scheme is proposed to solve the problem and improve control performance by reusing experiential knowledge. The proposed method not only solves the leader-follower formation problem but also improves transient control performance.
This article investigates the leader-follower formation learning control (FLC) problem for discrete-time strict-feedback multiagent systems (MASs). The objective is to acquire the experience knowledge from the stable leader-follower adaptive formation control process and improve the control performance by reusing the experiential knowledge. First, a two-layer control scheme is proposed to solve the leader-follower formation control problem. In the first layer, by combining adaptive distributed observers and constructed $i_{n}$ -step predictors, the leader's future state is predicted by the followers in a distributed manner. In the second layer, the adaptive neural network (NN) controllers are constructed for the followers to ensure that all the followers track the predicted output of the leader. In the stable formation control process, the NN weights are verified to exponentially converge to their optimal values by developing an extended stability corollary of linear time-varying (LTV) system. Second, by constructing some specific ``learning rules,'' the NN weights with convergent sequences are synthetically acquired and stored in the followers as experience knowledge. Then, the stored knowledge is reused to construct the FLC. The proposed FLC method not only solves the leader-follower formation problem but also improves the transient control performance. Finally, the validity of the presented FLC scheme is illustrated by simulations.
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