Observer-Based Adaptive Backstepping Consensus Tracking Control for High-Order Nonlinear Semi-Strict-Feedback Multiagent Systems

Combined with backstepping techniques, an observer-based adaptive consensus tracking control strategy is developed for a class of high-order nonlinear multiagent systems, of which each follower agent is modeled in a semi-strict-feedback form. By constructing the neural network-based state observer for each follower, the proposed consensus control method solves the unmeasurable state problem of high-order nonlinear multiagent systems. The control algorithm can guarantee that all signals of the multiagent system are semi-globally uniformly ultimately bounded and all outputs can synchronously track a reference signal to a desired accuracy. A simulation example is carried out to further demonstrate the effectiveness of the proposed consensus control method.

I. Introduction

Consensus control of multiagent systems as a burgeoning research topic has received a great amount of attentions due to its applications in various areas, such as satellite clusters [1], unmanned air vehicles [2], formation control of mobile robots [3], and distributed sensor networks [4]. The basic idea of consensus control is that all agents are driven to an agreement by a consensus protocol. In consensus control, two control strategies, leaderless consensus and leader-following consensus, have been widely developed. Leader-following consensus control means that a leader is a specified objective for the whole group. However, no matter leaderless consensus control or leader-following consensus control, most of the research results were limited to first- or second-order multiagent systems [5]–[10]. In fact, many practical engineering systems need to be modeled by high-order differential equations, for examples, some single-link flexible joint manipulators are modeled by a fourth-order nonlinear dynamic [11], or jerk systems [12] (i.e., derivative of acceleration) are described by a third-order differential equation. Therefore, compared with first- and second-order multiagent systems, high-order consensus controls are more valuable for practical application, and it is also more challenging due to the complexity of its system dynamic.