Decentralized Hybrid Formation Control of Unmanned Aerial Vehicles

This paper presents a decentralized hybrid supervisory control approach for a team of unmanned helicopters that are involved in a leader-follower formation mission. Using a polar partitioning technique, the motion dynamics of the follower helicopters are abstracted to finite state machines. Then, a discrete supervisor is designed in a modular way for different components of the formation mission including reaching the formation, keeping the formation, and collision avoidance. Furthermore, a formal technique is developed to design the local supervisors decentralizedly, so that the team of helicopters as whole, can cooperatively accomplish a collision-free formation task

Broadcast Guidance of Multi-Agent Systems

We consider the emergent behavior of a group of mobile agents guided by an exogenous broadcast signal. The agents’ dynamics is modelled by single integrators and they are assumed oblivious to their own position, however they share a common orientation (i.e. they have compasses). The broadcast control, a desired velocity vector, is detected by arbitrary subgroups of agents,that upon receipt of the guidance signal become "ad-hoc" leaders. The control signal and the set of leaders are assumed to be constant over some considerable intervals in time. A system without "ad-hoc" leaders is referred to as autonomous. The autonomous rule of motion is identical for all agents and is a gathering process ensuring a cohesive group. The agents that become leaders upon receipt of the exogenous control add the detected broadcast velocity to the velocity vector dictated by the autonomous rule of motion. This paradigm was considered in conjunction with several models of cohesive dynamics, linear and non-linear, with fixed inter-agent interaction topology, as well as systems with neighborhood based topology determined by the inter-agent distances. The autonomous dynamics of the models considered provides cohesion to the swarm, while, upon detection of a broadcast velocity vector, the leaders guide the group of agents in the direction of the control. For each local cohesion interaction model we analyse the effect of the broadcast velocity and of the set of leaders on the emergent behavior of the system. We show that in all cases considered the swarm moves in the direction of the broadcast velocity signal with speed set by the number of agents receiving the control and in a constellation determined by the model and the subset of "ad-hoc" leaders. All results are illustrated by simulations

Robust Distributed Control Protocols for Large Vehicular Platoons with Prescribed Transient and Steady State Performance

In this paper, we study the longitudinal control problem for a platoon of vehicles with unknown nonlinear dynamics under both the predecessor-following and the bidirectional control architectures. The proposed control protocols are fully distributed in the sense that each vehicle utilizes feedback from its relative position with respect to its preceding and following vehicles as well as its own velocity, which can all be easily obtained by onboard sensors. Moreover, no previous knowledge of model nonlinearities/disturbances is incorporated in the control design, enhancing in that way the robustness of the overall closed loop system against model imperfections. Additionally, certain designer-specified performance functions determine the transient and steady-state response, thus preventing connectivity breaks due to sensor limitations as well as inter-vehicular collisions. Finally, extensive simulation studies and a real-time experiment conducted with mobile robots clarify the proposed control protocols and verify their effectiveness.Comment: IEEE Transactions on Control Systems Technology, accepte

The Capabilities of Chaos and Complexity

To what degree could chaos and complexity have organized a Peptide or RNA World of crude yet necessarily integrated protometabolism? How far could such protolife evolve in the absence of a heritable linear digital symbol system that could mutate, instruct, regulate, optimize and maintain metabolic homeostasis? To address these questions, chaos, complexity, self-ordered states, and organization must all be carefully defined and distinguished. In addition their cause-and-effect relationships and mechanisms of action must be delineated. Are there any formal (non physical, abstract, conceptual, algorithmic) components to chaos, complexity, self-ordering and organization, or are they entirely physicodynamic (physical, mass/energy interaction alone)? Chaos and complexity can produce some fascinating self-ordered phenomena. But can spontaneous chaos and complexity steer events and processes toward pragmatic benefit, select function over non function, optimize algorithms, integrate circuits, produce computational halting, organize processes into formal systems, control and regulate existing systems toward greater efficiency? The question is pursued of whether there might be some yet-to-be discovered new law of biology that will elucidate the derivation of prescriptive information and control. “System” will be rigorously defined. Can a low-informational rapid succession of Prigogine’s dissipative structures self-order into bona fide organization