Hybrid Consensus-Based Formation Control of UAVs H. M. Guzey Dept. of Electrical and Computer Engineering Missouri University of Science and Technology 301 W. 16-Th St. Rolla MO 65409-0040 ABSTRACT In this paper, hybrid consensus based formation control for a team of Unmanned Aerial Vehicles (UAV’s) is considered. A hybrid consensus based formation controller is applied for UAV’s moving at fixed altitudes to drive them to a goal point while maintaining a specified formation. The proposed hybrid automaton consists of two discrete states, each with continuous dynamics: a regulation state and a formation keeping state. The controller in the regulation state uses local state information to achieve its objective while the formation controller utilizes the state and controller information of neighboring UAV’s. Consequently, the UAV’s switch between the control objectives of formation keeping and goal seeking in route to their goal points. The switching behavior creates hybrid dynamics from the interactions between the continuous and discrete states making the stability analysis of the system more complex than considering purely discrete or purely continuous. Therefore, the stability of the hybrid approach is proven by using multiple Lyapunov functions and also considers the switching conditions between the regulation and the formation states. The Lyapunov based approach demonstrates that the formation errors converge to a small bounded region around the origin and the size of the bound can be adjusted by using the switching conditions. Convergence to goal position while in formation is also demonstrated in the same Lyapunov analysis, and simulation results verify the theoretical conjectures. Keywords: multiple UAVs; consensus; formation control; hybrid automata; feedback linearization; 1. INTRODUCTION In the last decade, formation control of multiple unmanned vehicles (MUAVs) have received attention [1]-[10] of the scientific community. Because of the potential applications on both civilian and military application of MUAVs, the researches in this field have been growing faster recently. MUAVs working together provide much more advantageous than a single UAV in larger environments. For instance, when the time is limited in search and rescue operation in a large field [1]; tracking a moving target [2]; load transformation task when the load can’t be carried and stabilized by a single UAV[3]; area coverage surveillance [4] or observation of larger fields in public safety applications [5] or so on. Several different approaches [6]-[10] are utilized to control formation of MUAV’s such as virtual structure approach[10], leader-follower approach [8][9], and consensus based approach[6][7]. The consensus based formation control approach is considered in this paper due to the robustness and scalability properties. In consensus-based formation control, the UAVs share information regarding their position errors from their respective goal positions. The shared information is then synthesized into a control law which seeks to achieve the same position error for all UAVs until each UAV has reached its goal position. The desired formation is achieved and maintained by reaching and maintaining consensus on the position errors. Therefore, the main tasks in consensus-based formation control are described as: i) given an initial state, achieve a desired formation, and ii) maintain this formation while the UAVs move through the environment to reach their desired goal position. Most consensus approaches in the literature [6][7] contribute to the first task of formation control. However, a combined regulation-formation controller is proposed in [11] where consensus is reached on the system state vector. Although the stability of the combined approach is examined, proving that the formation is achieved before the robots reach their goal positions is not addressed. Motivated by aforementioned limitations of existing consensus controllers[6][7][11], our previous work [12] proposed a novel hybrid regulation-formation controller that allows the formation errors to converge before the systems reach their goal positions and removes the conflict of driving to a goal point while maintaining the formation. Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications XII, D. Henry, G. Gosian, D. Lange, D. Linne von Berg, T. Walls, D. Young, Eds. Proc. of SPIE Vol. 9460, 946003 · © 2015 SPIE · CCC code: 0277-786X/15/$18 · doi: 10.1117/12.2177582 Proc. of SPIE Vol. 9460 946003-1 Downloaded From: http://proceedings.spiedigitallibrary.org/ on 05/16/2017 Terms of Use: http://spiedigitallibrary.org/ss/termsofuse.aspx