Distributed Adaptive Control for Multi-Agent Systems with Pseudo Higher Order Neural Net

The idea of using multi-agent systems is becoming more popular every day. It not only saves time and resources but also eliminates much of the human workload. These ideas are especially effective in the combat zone, where multiple unmanned aerial vehicles can achieve simultaneous objectives or targets. The evolution of distributed control started with a simple integrator systems, and then different control methodologies have been adopted for more and more complex nonlinear systems. In addition, from a practical standpoint, the dynamics of the agents involved in networked control architecture might not be identical. Therefore, an ideal distributed control should accommodate multiple agents that are nonlinear systems associated with unknown dynamics. In this chapter, a distributed control methodology is presented where nonidentical nonlinear agents communicate among themselves following directed graph topology. In addition, the nonlinear dynamics are considered unknown. While the pinning control strategy has been adopted to distribute the input command among the agents, a Pseudo Higher Order Neural Net (PHONN)-based identification strategy is introduced for identifying the unknown dynamics. These two strategies are combined beautifully so that the stability of the system is assured even with minimum interaction among the agents. A detailed stability analysis is presented based on the Lyapunov theory, and a simulation study is performed to verify the theoretical claims. DOI: 10.4018/978-1-4666-2175-6.ch009