In this article, the objective for a group of nonholonomic agents is to achieve multicircular circumnavigation with any desired angular spacing around a nonstationary target. A cooperative protocol is proposed to achieve this objective and, additionally, generalizes the circumnavigation problem of unicycle vehicles around a target (moving and stationary). Due to the nonholonomic constraints, existing protocols cannot be extended directly to achieve this objective. Thus, the proposed algorithm is worked out from the desired geometry to achieve the objective. A fixed-time consensus estimator is designed under a cyclic digraph to arrive at the desired interagent angular separation in the target-centric frame. The target information and desired formation parameters are assumed to be known to only one agent partially. Due to uncertainty in the target's motion, it is assumed that the target's acceleration and angular velocity are unknown. Fixed-time estimators under a digraph address the lack of information. The tracking controller drives the agents to the desired position around the target, thus making the errors go to zero instead of any finite bounds. The controller guarantees the bounded control effort irrespective of the error magnitude. The numerical examples are presented to show the effectiveness of the algorithm.

This paper provides a real time on-board algorithm for a biplane-quadrotor to iteratively learn a forward transition maneuver via repeated flight trials. The maneuver is controlled by regulating the pitch angle and propeller thrust according to feedforward control laws that are parameterized by polynomials. Based on a nominal model with simplified aerodynamics, the optimal coefficients of the polynomials are chosen through simulation such that the maneuver is completed with specified terminal conditions on altitude and air speed. In order to compensate for modeling errors, repeated flight trials are performed by updating the feedforward control parameters according to an iterative learning algorithm until the maneuver is perfected. A geometric attitude controller, valid for all flight modes is employed in order to track the pitch angle according to the feedforward law. Further, a high-fidelity thrust model of the propeller for varying advance-ratio and orientation angle is obtained from wind tunnel data which is captured using a neural network model. This facilitates accurate application of feedforward thrust for varying flow conditions during transition. Experimental flight trials are performed to demonstrate the robustness and rapid convergence of the proposed learning algorithm.

In this paper, we present a cooperative target-centric formation control strategy that maintains the dynamic graph connectivity for a system of unmanned aerial vehicles. The connectivity of a graph plays a critical role in dynamic networks of multiple agents since it represents a level of information sharing capability of a system. The connectivity of a network of unmanned systems changes as the state dependent graph evolves over time, revealing the risk of the system being uncontrollable during null connectivity. We show that the existing formation controller fails to make the formation when the network connectivity is lost. We propose a novel formation control strategy which keeps the multi-agent graph connected throughout the system dynamic process. The convergence of the formation controller is shown with the help of the Lyapunov theory. Simulation results validate the effectiveness of the proposed control law.