Adaptive consensus based formation control of unmanned vehicles

Over the past decade, the control research community has given significant attention to formation control of multiple unmanned vehicles due to a variety of commercial and defense applications. Consensus-based formation control is considered to be more robust and reliable when compared to other formation control methods due to scalability and inherent properties that enable the formation to continue even if one of the vehicles experiences a failure. In contrast to existing methods on formation control where the dynamics of the vehicles are neglected, this dissertation in the form of four papers presents consensus-based formation control of unmanned vehicles-both ground and aerial, by incorporating the vehicle dynamics. First, neural networks (NN)-based optimal adaptive consensus-based formation control over finite horizon is presented for networked mobile robots or agents in the presence of uncertain robot/agent dynamics and communication. In the second paper, a hybrid automaton is proposed to control the nonholonomic mobile robots in two discrete modes: a regulation mode and a formation keeping mode in order to overcome well-known stabilization problem. The third paper presents the design of a distributed consensus-based event-triggered formation control of networked mobile robots using NN in the presence of uncertain robot dynamics to minimize communication. All these papers assume state availability. Finally, the fourth paper extends the consensus effort by introducing the development of a novel nonlinear output feedback NN-based controller for a group of quadrotor UAVs --Abstract, page iv

Hybrid Consensus-Based Control of Nonholonomic Mobile Robot Formation

This paper addresses the hybrid consensus-based formation keeping problem for nonholonomic mobile robots in the presence of a novel time-varying, composite, nonlinear velocity-tracking error system. First, continuous-time regulation and consensus-based formation controllers are developed for a group of wheeled mobile robots. These controllers are then used to create a hybrid automaton, which drives the robots to their goal positions while maintaining a specified formation.In order to avoid the hard switches between regulation and formation keeping controllers, a novel blended velocity tracking error approach is proposed in this work to create nonlinear, time-varying velocity error dynamics. Therefore, the hybrid controller consists of two discrete modes, each with continuous dynamics, and the novel blended velocity tracking error approach provides a smooth transition between each mode. The controller in the regulation mode drives the robot to a goal position while the formation keeping controller ensures that the robots achieve a specified geometric formation prior to reaching their goal-position. Time-varying Lyapunov functions are used to rigorously demonstrate 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 illustrating that the robots are converging to their goal positions while operating in both regulation and formation keeping mode. Simulation results verify the theoretical conjectures

Distributed Consensus-Based Event-Triggered Approximate Control of Nonholonomic Mobile Robot Formations

In this paper, a distributed consensus-based formation control of networked nonholonomic mobile robots using neural networks (NN) in the presence of uncertain robot dynamics with event-based communication is presented. The robots communicate their location and velocity information with their neighbors, at event-based sampling instants, to drive themselves to a pre-defined desired formation by using distributed controllers. For relaxing the perfect velocity tracking assumption, control torque is designed to reduce the velocity tracking error by explicitly taking into account each robot dynamics and the formation dynamics of the network of robots via NN approximation. The approximated dynamics are employed to generate the control torque with event-sampled measurement updates and communication. For the distributed formation control scheme, Lyapunov stability theory is utilized to develop decentralized event-sampling condition and to demonstrate that the robots reach a consensus in their regulation errors. Finally, simulation results are presented to verify theoretical claims and to demonstrate the reduction in computations and communication cost