Adaptive Neural Cooperative Control of Multirobot Systems With Input Quantization

This article develops the adaptive neural cooperative control scheme for a group of mobile robots with a limited sensing range in presence of input quantization by a dynamic surface control technique. First, to make the controller design feasible, the original robotic system is transformed into a new fully actuated system using a transverse function. Then, taking into consideration the effects of a hysteresis quantizer, an adaptive neural cooperative controller is developed based on the universal approximation property of the radial basis function neural networks and the connectivity preservation strategy. Furthermore, the proposed control scheme can guarantee that all closed-loop signals are semi-globally uniformly ultimately bounded. Meanwhile, desired constraints are not breached and tracking errors are within the predefined domains. Finally, several simulation results are carried out to testify the feasibility and efficiency of the theoretical findings revealed in this article.

I. Introduction

The past couple of decades witnessed considerable attention to the multirobot system (MRS) due to its wide application in space exploration, environmental monitoring, and other fields [1], [2], [3], which is the special application case of the multiagent system (MAS) [4], [5]. The MRS has the characteristics of good spatial distribution and information complementarity, low cost, and high performance. It can replace a single robot to complete complex tasks and be more flexible, robust, and social. Cooperative control of multiple robots is widely used in military, space exploration, environmental monitoring, and other fields and has important practical application value. In particular, taking the inherent characteristics of robots such as nonholonomic constraints and nonlinearity as constraints and combining with distributed cooperative motion control, the cooperative target tracking control of multi robots has more important theoretical research significance [6], [7], [8].