Nandan Kumar Sinha

Unmanned Aerial Vehicle (UAV) swarms show great advantages in many applications such as surveillance, reconnaissance, and exploration because of the reduced computational expense, robustness, and less complexity. Artificial potential field (APF) is one such decentralized control strategy that steers UAV through steering and repulsive potential fields to achieve a formation. This paper extends to the previous research of the use of bifurcating APFs for swarm formation control by studying the unaccounted effects of repulsive potentials on the equilibrium states of the swarm system. Two prominent effects are observed, both of which are tied to the parameters of control law and the number of agents in the swarm. The radius of circular formation differs from estimates given by the nonlinear bifurcation analysis of steering potential and lower energy stable formation rings can be achieved with an increase in the number of agents. Finally, detailed simulation results validate the observed effects of the interaction between steering and repulsive potentials.

The prime focus of this work is to estimate stability and control derivatives of an airship in a completely nonlinear environment. A complete six degrees of freedom airship model has its aerodynamic model as nonlinear functions of angle of attack. Estimating the parameters of aerodynamic model in a nonlinear environment is challenging as it demands an exhaustive dataset that could cover the entire regime of operation of airship. In this work, data generation is achieved by simulating the mathematical model of airship for different trim conditions obtained from continuation analysis. The mathematical model is simulated using predicted parameter values obtained using DATCOM methodology. A modular neural network is then trained using back-propagation and Adam optimisation algorithm for each of the aerodynamic coefficients separately. The estimated nonlinear airship parameters are found to be consistent with the DATCOM parameter values which were used for open-loop simulation. This validates the proposed methodology and could be extended to estimate airship parameters from real flight data.

Though conceptualization of nonlinear sliding mode control has gained great emphasis in mechatronics and nonlinear systems in general, little attention is given to real time implementation owing to its inadequacy in handling mismatched uncertainties. This work contemplates on a robust nonlinear control scheme with sliding mode control and extended Kalman filter in closed-loop to estimate and handle bounded uncertainties. Stability of this closed-loop framework is established through Lyapunov analysis. The proposed formulation is first validated on a simulation platform and then implemented on a 2-DOF experimental gyroscope setup. Efficacy of this approach is evident from its rigorous tracking performance attained with a smooth and bounded control profile, despite induced uncertainties in various forms.

Aircraft dynamics are dominated by nonlinearities that may drive the aircraft into chaotic motions under certain conditions. Past studies in this area have explored several factors leading to the evolution of chaotic dynamics. However, a proper route or sequence for the evolution of chaotic dynamics has not been adequately substantiated. In this context, this paper systematically examines possible routes to chaos in the post-stall dynamics of an F-18 High-Alpha Research Vehicle model with external steady wind as the driving agent. Using tools from nonlinear dynamics based on bifurcation analysis, phase portrait, Poincaré map and amplitude spectrum analysis techniques, existence of quasi-periodic, period-doubling and intermittency routes to chaos are established. An eighth-order nonlinear aircraft model incorporating wind effects has been used for generating time responses from different post-stall flight conditions.

Recent interests in Loss-of-Control (LOC) related accidents of aircraft bring back focus on the need for construction of realistic simulations not only of impending accident scenarios but also of recovery of aircraft from fully developed accident scenarios. Developing control schedules for both the activities thereby becomes crucial. In this paper, a novel approach based on constrained numerical continuation procedure is presented to effectively compute open-loop control interconnect schedules for a six-degree-of-freedom aircraft model. For illustrative purposes, the approach based on a new formulation of constraint equations is used to design open-loop control interconnect schedule for recovery of an F-18 HARV model from auto-rotational spin condition.

Cooperative missions carried out by multiagent systems consisting of aerial agents such as UAV or MAV have immense application in surveillance, reconnaissance, exploration, search-and-rescue, etc. Some of these applications require these swarms to navigate through complicated environments such as urban areas consisting of narrow corridors or channels. The formation control of the multiagent system has to accommodate for tangibility in the formation structure in order to navigate through such tight spaces without much congestion amongst the individuals of the swarm. A congested swarm is prone to interagent collisions or collisions with the environment leading to complete failure of the mission. This paper proposes a congestion-relieving method for formation control of a swarm using priority tags on individuals based on Artificial Potential Fields (APF). Based on the formation equilibrium study for the swarm, a minimum distance is calculated as a threshold to classify the swarm as dense or sparse and detect congestion due to narrow corridor navigation. The key feature of the proposed formation control is its decentralized nature which allows scalability and flexibility on large multiagent systems as it requires only limited local information to generate the formation structure. Numerical simulations are carried out to evaluate the performance of priority-tagged APF while navigating through narrow corridors and the results are discussed by comparing performance without priority tags.

Recent space activity trends herald a huge increase in the frequency of space launches and plans for constellations of satellites in space in the very near future. This calls for active measures to remove space debris from the orbits to maintain sustainable space access. This is mainly due to the high risk of collision of active spacecrafts with space debris that can easily lead to the failure of missions. Methods like space tether and tethered net are active areas of research and stand as promising candidates for low-cost and effective solutions to mitigate space debris. However, the success rate of capturing debris in conventional methods is debatable. This paper discusses the application of a swarm of agents attached to a space debris using space teth-ers to perform cooperative orbital maneuver of the space debris. The application of a swarm instead of a monolithic space robot boosts the flexibility, reusability, and robustness of the missions and improves the success rate of capturing the space debris of different shapes and sizes. A swarm of CubeSats attached to a rigid body space debris via space tethers is modeled. The space tether is modeled as a series of lumped masses to account for its dynamics and flexibility. The dynamics of the coupled nonlinear system are studied and swarm control laws are formulated using Artificial Potential Field method. The proposal of decentralized control of the swarm opens the possibility of scaling the missions to cooperatively work in numbers which can be computationally too expensive otherwise. A deorbiting control for the swarm is proposed such that it maximizes the decay rate of the semimajor axis. Numerical simulation results show that the proposed swarm control is capable of detumbling the space debris and deorbit it into the atmosphere from Lower Earth Orbit (LEO).

Maneuver design is a vital prerequisite for autonomy in aerospace guidance and control. It ensures system safety by analyzing the feasibility of the proposed maneuvers. This paper demonstrates a holistic approach to maneuver design by emphasizing its feasibility with consideration on the complete dynamics of the system and its constraints. Computational bifurcation analysis is employed to generate a sequence of trim solutions based on specified state and control constraints that define a stipulated maneuver. Implementing this approach on a stratospheric airship model helps attempt a few of its challenging and interesting facets like autonomous hovering, ascending, and descending by gauging its performance and formulating realistic maneuvers pertaining to the imposed limitations on the lateral excursion, control, and state constraints. The effectiveness of the proposed maneuvers is then evaluated using numerical simulations with computed control schedules in an open-loop.

This paper describes design and development of an integrated guidance, navigation, and control (GNC) algorithm for smooth navigation of an airship following a series of planar waypoints for surveillance applications. The guidance algorithm employed in this study is a look ahead-based steering guidance law which provides the required guidance command to the controller for executing the target mission. The command signal generated by guidance law is based on the airship current location relative to the next target waypoint. Two different control architectures are used in the GNC algorithm in order to execute the guidance command and carry out the path following mission. A proportional–integral–derivative (PID) law-based outer loop kinematic controller is designed to control airship attitude/orientation, and a linear quadratic regulator (LQR)-based inner loop optimal controller is designed to control the airship’s speed and angular rates. Performance and robustness of developed GNC scheme are evaluated in MATLAB®-based simulation environment for two different flight scenarios. Results show that an optimal path following trajectory is obtained with minimum deviation from the given target waypoints.

This paper presents multi sensor fusion architecture to fuse multi target tracks from the Radar and an Infrared Search & Track (IRST) system. The proposed architecture uses global nearest neighborhood (GNN) algorithm for data association between target tracks of Radar and IRST. Further a GNN based central track manager is proposed to maintain the fused tracks from Radar and IRST. The Radar track outputs are modelled in Cartesian coordinates, providing the target position and velocity states with their corresponding covariance, while the radar detections are in polar coordinates. The IRST track output provides angle only information of the targets with their corresponding covariance. Simulations are carried out in single aircraft - multi target scenario and it is observed that the fused track output has improved angle accuracy with lesser variance.