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1. Finite-time Attitude Consensus Control of a Multi-Agent Rigid Body System

   https://doi.org/10.23919/ACC45564.2020.9147639  

   Maadani, Mohammad; Butcher, Eric A.; Sanyal, Amit K. (July 2020, 2020 American Control Conference (ACC))

   In this paper, finite-time attitude consensus control laws for multi-agent rigid body systems are presented using rotation matrices. The control objective is to stabilize the relative configurations in a finite convergence time. First, the control design is done on the kinematic level where the angular velocities are the control signals. Next, the design is conducted on the dynamic level in the framework of the tangent bundle TSO(3) associated with SO(3), where the torques implement the feedback control of relative attitudes and angular velocities. The Lyapunov-based almost global finite-time stability of the consensus subspace is demonstrated for both cases. Finally, numerical simulations are provided to verify the effectiveness of the proposed consensus control algorithms. 

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2. Trajectory generation on SE(3) with applications to a class of underactuated vehicles

   https://doi.org/10.1109/CDC.2017.8264029  

   Dhullipalla, Mani H.; Hamrah, Reza; Sanyal, Amit K. (December 2017, 2017 IEEE 56th Annual Conference on Decision and Control (CDC))

   This paper addresses the problem of generating a continuous and differentiable trajectory on the Lie group of rigid body motions, SE(3), for a class of underactuated vehicles modeled as rigid bodies. The three rotational degrees of freedom (DOF) are independently actuated, while only one translational DOF is actuated by a body-fixed thrust vector. This model is applicable to a large set of unmanned vehicles, including fixed-wing and rotorcraft unmanned aerial vehicles (UAVs). The formulation utilizes exponential coordinates to express the underactuation constraint as an intrinsic part of the problem. It provides steps to generate a rest-to-rest trajectory after obtaining conditions that guarantee controllability. An attitude trajectory is selected to satisfy the given initial and final attitude state. The position trajectory generation is subsequently posed as an optimal control problem expressed as a linear quadratic regulator (LQR) in the exponential coordinates corresponding to position. As a result, an optimal position trajectory is obtained which ensures that the trajectory generated is feasible with realistic velocities and with given initial pose and final pose, while satisfying the underactuation constraint. Numerical simulation results are obtained that validate this trajectory generation scheme. 

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3. A Finite-Time Stable Observer for Relative Attitude Estimation

   https://doi.org/10.1109/CDC40024.2019.9029806  

   Wang, Ningshan; Hamrah, Reza; Sanyal, Amit K. (December 2019, 2019 IEEE 58th Conference on Decision and Control (CDC))

   Relative motion estimation of one rigid body with respect to another is a problem that has immediate applications to formations and maneuvers involving multiple unmanned vehicles or collision avoidance between vehicles. A finite-time stable observer for relative attitude estimation of a rigid object using onboard sensors on an unmanned vehicle, is developed and presented here. This observer assumes sensor inputs from onboard vision and inertial sensors, with the vision sensors measuring at least three points on the object whose relative locations with respect to a body-fixed frame on the object are also assumed to be known. In the absence of any measurement noise, the estimated relative attitude is shown to converge to the actual relative pose in a finite-time stable manner. Numerical simulations indicate that this relative attitude observer is robust to persistent measurement errors and converges to a bounded neighborhood of the true attitude. 

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4. Anisotropic flocking control of distributed multi-agent systems using fluid abstraction

   Silic, M.; Song, Z.; Mohseni, K. (January 2018, Proceedings of the AIAA Information Systems-AIAA Infotech @ Aerospace)

   This paper presents a multi-agent flocking scheme for real-time control of homogeneous unmanned aerial vehicles (UAVs) based on smoothed particle hydrodynamics. Swarm cohe- sion, collision avoidance, and velocity consensus are concurrently satisfied by characterizing the emerging macroscopic flock as a continuous fluid. Two vital implementation issues are addressed in particular including latency in information fusion and directionality of com- munication due to antenna patterns. Symmetric control forces are achieved by meticulous scheduling of inter-vehicle communication to sustain the motion stability of the flock. A gener- alized, anisotropic smoothing kernel that takes into account the relative position and attitude between agents is adopted to address potential flocking instability introduced by communi- cation anisotropy due to the antenna radiation pattern. The feasibility of the technique is demonstrated experimentally using a single UAV avoiding a virtual obstacle. 

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5. Dynamical Modeling and Distributed Control of Connected and Automated Vehicles: Challenges and Opportunities

   https://doi.org/10.1109/MITS.2017.2709781  

   Shengbo Eben Li, Yang Zheng (July 2017, IEEE intelligent transportation systems magazine)

   The platooning of connected and automated vehicles (CAVs) is expected to have a transformative impact on road transportation, e.g., enhancing highway safety, improving traffic utility, and reducing fuel consumption. Requiring only local information, distributed control schemes are scalable approaches to the coordination of multiple CAVs without using centralized communication and computation. From the perspective of multi-agent consensus control, this paper introduces a decomposition framework to model, analyze, and design the platoon system. In this framework, a platoon is naturally decomposed into four interrelated components, i.e., 1) node dynamics, 2) information flow network, 3) distributed controller, and 4) geometry formation. The classic model of each component is summarized according to the results of the literature survey; four main performance metrics, i.e., internal stability, stability margin, string stability, and coherence behavior, are discussed in the same fashion. Also, the basis of typical distributed control techniques is presented, including linear consensus control, distributed robust control, distributed sliding mode control, and distributed model predictive control. 

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