Distributed adaptive control for consensus tracking with application to formation control of nonholonomic mobile robots

Chen

Intl J Robust & Nonlinear

et al. 2017

Self Cite

Summary In this paper, an output‐feedback adaptive consensus tracking control scheme is proposed for a class of high‐order nonlinear multi‐agent systems. The agents are allowed to have unknown parameters, unknown nonlinearities, and input quantization simultaneously. The desired trajectory to be tracked is available for only a subset of agents, and only the relative outputs and the quantized inputs need to be measured or transmitted as signal exchange among neighbors regardless of the system order. By introducing a kind of high‐gain K‐filters and a smooth function, the effect among agents caused by the unknown nonlinearities is successfully counteracted, and all closed‐loop signals are proved to be globally uniformly bounded. Moreover, it is shown that the tracking errors converge to a residual set that can be made arbitrarily small. Simulation results on robot manipulators are presented to illustrate the effectiveness of the proposed scheme. Copyright © 2017 John Wiley & Sons, Ltd.

Section: Introductionmentioning

confidence: 99%

“…With the aid of the popular backstepping design , the matching condition was removed in . Nevertheless, the same as , the schemes in are based on state‐feedback. By comparison, existing results on output‐feedback adaptive consensus control for high‐order nonlinear multi‐agent systems are very limited.…”

Section: Introductionmentioning

confidence: 99%

Output‐feedback adaptive consensus tracking control for a class of high‐order nonlinear multi‐agent systems

Chen

Intl J Robust & Nonlinear

et al. 2017

Self Cite

“…For a directed connection graph, the adaptive schemes in (Das and Lewis, 2010;Zhang and Lewis, 2012) achieved consensus control at a price of sacrificing the asymptotic convergence. Further results on adaptive control under directed communication graph include adaptive coordination of multiple Lagrangian systems (Mei, Ren, Chen and Ma, 2013) and consensus tracking for state-feedback nonlinear systems in the strict feedback form (Wang, Huang, Wen and Fan, 2014), which were obtained using direct local measurements or certain parameters such as P i in (Wang et al, 2014) that requires the knowledge of the Laplacian matrix. The asymmetric connection in a directed graph remained as an obstacle in the extension of adaptive schemes beyond undirected graphs to fully distributed adaptive consensus control, using the relative state or relative output measurements.…”

Section: Introductionmentioning

confidence: 99%

Distributed adaptive consensus control of nonlinear output-feedback systems on directed graphs

Ding

2016

Automatica

119

This paper deals with consensus control in leader-follower format of a class of network-connected uncertain nonlinear systems by output feedback. Each subsystem is in the nonlinear output feedback form with unknown parameters, and the connection graph among the subsystems is directed. Distributed adaptive control inputs are designed to achieve the consensus control in the sense that the subsystem states asymptotically follow the subsystem at node 0 with no input, which is also known as the leader. The proposed adaptive control only uses relative output measurements and the local information of the connection to each subsystem, and hence the proposed adaptive control is fully distributed. The proposed scheme is different from the consensus output regulation schemes literature, and the leader plays a similar role as a reference model in the classic model reference adaptive control.

IEEE Trans. Automat. Contr.

“…One way to alleviate the problem is to use robust adaptive laws at the cost of asymptotic convergence [11], [12]. Adaptive consensus tracking under directed connection graphs is addressed in [13] for formation control via state feedback. Adaptive laws are also proposed to estimate network connectivity, aiming at a fully distributed control, without using the knowledge of eigenvalues of Laplacian matrices [14].…”

Section: Introductionmentioning

confidence: 99%

Distributed Adaptive Consensus Output Regulation of Network-Connected Heterogeneous Unknown Linear Systems on Directed Graphs

Ding

2017

Abstract-This technical note deals with output regulation of network connected linear systems under directed connection graphs. The subsystem dynamics are unknown and heterogeneous. The desired output signal, generated by a linear exosystem, is only available to some subsystems, and the others will estimate the exosystem state through the network connections for obtaining regulation errors. The exosystem is parametrised in a specific form such that it has a skew-symmetric system matrix, whose property is further explored in the estimation of exosystem state, and in the estimation of the desired feed-forward control inputs for output regulation. The unknown subsystem parameters are dealt with by the adaptive laws, driven by the estimated regulation errors. The proposed adaptive control strategy is fully distributed, in the sense that the control design only uses the information in the connected neighbourhood, without reference to the eigenstructure of the associated Laplacian matrix, as long as the connection graph is strongly connected.