Adaptive cooperative tracking control of higher-order nonlinear systems with unknown dynamics

• Published in: Automatica (Oxford) Vol. 48; no. 7; pp. 1432 - 1439
• Main Authors: Zhang, Hongwei, Lewis, Frank L.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.07.2012; Elsevier
• Subjects: Adaptative systems; Applied sciences; Computer science; control theory; systems; Consensus; Control system analysis; Control system synthesis; Control theory. Systems; Cooperative control; Exact sciences and technology; Flows in networks. Combinatorial problems; Multi-agent system; Neural adaptive control; Nonlinear system; Operational research and scientific management; Operational research. Management science; Synchronization; Multi-agent system; Synchronization; Neural adaptive control; Cooperative control; Nonlinear system; Consensus; Disturbance rejection; Non linear control; Control synthesis; Dynamical system; Adaptive control; Modeling; Cooperation; Non autonomous system; Integrator; Bounded error; Error bound; Tracking task; Neural network; Topology; Communication network; Non linear system; Weighted graph; Neurocontrollers; Multiagent system; Telecommunication network; Distributed control; Output feedback; Directed graph
• ISSN: 0005-1098; 1873-2836
• DOI: 10.1016/j.automatica.2012.05.008

A practical design method is developed for cooperative tracking control of higher-order nonlinear systems with a dynamic leader. The communication network is a weighted directed graph with a fixed topology. Each follower node is modeled by a higher-order integrator incorporating with unknown nonlinear dynamics and an unknown disturbance. The leader node is modeled as a higher-order nonautonomous nonlinear system. It acts as a command generator giving commands only to a small portion of the networked group. A robust adaptive neural network controller is designed for each follower node such that all follower nodes ultimately synchronize to the leader node with bounded residual errors. Moreover, these controllers are distributed in the sense that the controller design for each follower node only requires relative state information between itself and its neighbors. A simulation example demonstrates the effectiveness of the algorithm.