Distributed adaptive consensus tracking control for uncertain non-linear multi-agent systems with input saturation

• Published in: IET control theory & applications Vol. 13; no. 14; pp. 2153 - 2162
• Main Authors: Shahriari-kahkeshi, Maryam, Taj, Maedeh
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 24.09.2019
• Subjects: adaptive consensus control scheme; adaptive control; approximate the uncertain dynamics; closed loop systems; closed‐loop system; consensus strategy; consensus tracking error; control system synthesis; dead‐zone operator‐based model; distributed adaptive consensus tracking control; dynamic surface control method; follower agents; input saturation; multi‐agent systems; multi‐robot systems; neighbour agents; neurocontrollers; nonlinear control systems; nonlinear multiagent systems; radial basis function networks; radial basis function‐neural networks; Research Article; saturation nonlinearity; stability; uncertain systems; unknown control gains; multi-agent systems; neighbour agents; unknown control gains; multi-robot systems; adaptive consensus control scheme; radial basis function-neural networks; neurocontrollers; radial basis function networks; follower agents; saturation nonlinearity; stability; dynamic surface control method; uncertain systems; dead-zone operator-based model; adaptive control; control system synthesis; nonlinear multiagent systems; closed loop systems; approximate the uncertain dynamics; distributed adaptive consensus tracking control; nonlinear control systems; input saturation; consensus strategy; closed-loop system; consensus tracking error
• ISSN: 1751-8644; 1751-8652
• DOI: 10.1049/iet-cta.2018.5391

This study proposes a novel distributed adaptive consensus control scheme for a class of uncertain non-linear multi-agent systems with unknown control gains and input saturation. The radial basis function-neural networks are used to approximate the uncertain dynamics of the follower agents, as well as the effect of the neighbour agents of each agent. The dead-zone operator-based model is proposed to provide a smooth model of the saturation non-linearity. Then, the consensus strategy is proposed based on the minimal learning parameter algorithm and the dynamic surface control method. The stability analysis shows that all the signals of the closed-loop system are semi-globally uniformly ultimately bounded and the consensus tracking error converges to a small vicinity of the origin. The proposed scheme solves the ‘singularity’ problem without using the projection operator. Furthermore, it avoids the ‘explosion of complexity’ and ‘explosion of learning parameters’ problems, simultaneously, and reduces the computational burden. Simulation results performed on a set of single-link robots composed of four followers and one leader verify the effectiveness of the proposed method.