Distributed Control Of Multi-Agent Systems Using Biologically-Inspired Reinforcement Learning

In this dissertation, we investigate the real-time flocking control of Multi-Agent Systems (MAS) in presence of system uncertainties and dynamic environment. To handle the impacts from system uncertainties and dynamic environment, a novel reinforcement learning technique, which is appropriate for real-time implementation, has been integrated with multi-agent flocking control in this dissertation. The Brain Emotional Learning Based Intelligent Controller (BELBIC) is a biologically-inspired reinforcement learning based controller relying on a computational model of emotional learning in mammalian limbic system. The learning capabilities, multi-objective properties, and low computational complexity of BELBIC make it a very promising learning technique for implementation in real-time applications. Firstly, a novel brain emotional learning based flocking control structure is proposed. Then, the real-time update laws are developed to tune the emotional signals based on the real-time operation data. It is important to note that this data-driven reinforcement learning approach relaxes the requirement for system dynamics and effectively handle the uncertain impacts of environment. Using the tuned emotional signals, the optimal flocking control can be obtained. The Lyapunov analysis has been used to prove the convergence of the proposed design. The effectiveness of the proposed design is also demonstrated through numerical and experimental results based on the coordination of multiple Unmanned Aircraft Systems (UAS) platforms