Monish D. Tandale

Vision-Based Sensor and Navigation System for Autonomous Air Refueling

Autonomous in-flight aerial refueling is an important capability for the future deployment of unmanned aerial vehicles, because they will likely be ferried in flight to overseas theaters of operation instead of being shipped unassembled in containers. A reliable sensor, capable of providing accurate relative position measurements of sufficient bandwidth, is key to such a capability. A vision-based sensor and navigation system is introduced that enables precise and reliable probe-and-drogue autonomous aerial refueling for non-micro-sized unmanned aerial vehicles. A performance robust controller is developed and integrated with the sensor system, and feasibility of the total system is demonstrated by simulated docking maneuvers with both a stationary drogue and a drogue subjected to light turbulence. An unmanned air vehicle model is used for controller design and simulation. Results indicate that the integrated sensor and controller enables precise aerial refueling, including consideration of realistic measurement errors and disturbances.

Trajectory Tracking Controller for Vision-Based Probe and Drogue Autonomous Aerial Refueling

This paper addresses autonomous aerial refueling between an unmanned tanker aircraft and an unmanned receiver aircraft using the probe-and-drogue method. An important consideration is the ability to achieve successful docking in the presence of exogenous inputs such as atmospheric turbulence. Practical probe and drogue autonomous aerial refueling requires a reliable sensor capable of providing accurate relative position measurements of su‐cient bandwidth, integrated with a robust relative navigation and control algorithm. This paper develops a Reference Observer Based Tracking Controller that does not require a model of the drogue or presumed knowledge of its position, and integrates it with an existing vision based relative navigation sensor. A trajectory generation module is used to translate the relative drogue position measured by the sensor into a smooth reference trajectory, and an output injection observer is used to estimate the states to be tracked by the receiver aircraft. Accurate tracking is provided by a state feedback controller with good disturbance rejection properties. A frequency domain stability analysis for the combined reference observer and controller shows that the system is robust to sensor noise, atmospheric turbulence, and high frequency unmodeled dynamics. Feasibility and performance of the total system is demonstrated by simulated docking maneuvers of an unmanned receiver aircraft docking with the non-stationary drogue of an unmanned tanker, in the presence of atmospheric turbulence. Performance characteristics of the vision based relative navigation sensor are also investigated, and the total system is compared to an earlier version. Results presented in the paper indicate that the integrated sensor and controller enable precise aerial refueling, including consideration of realistic measurements errors, plant modeling errors, and disturbances.

Improved Adaptive–Reinforcement Learning Control for Morphing Unmanned Air Vehicles

This paper presents an improved adaptive-reinforcement learning control methodology for the problem of unmanned air vehicle morphing control. The reinforcement learning morphing control function that learns the optimal shape change policy is integrated with an adaptive dynamic inversion control trajectory tracking function. An episodic unsupervised learning simulation using the Q-learning method is developed to replace an earlier and less accurate actor-critic algorithm. Sequential function approximation, a Galerkin-based scattered data approximation scheme, replaces a K-nearest neighbors (KNN) method and is used to generalize the learning from previously experienced quantized states and actions to the continuous state-action space, all of which may not have been experienced before. The improved method showed smaller errors and improved learning of the optimal shape compared to the KNN.

Fault-Tolerant Structured Adaptive Model Inversion Control

An adaptive dynamic inversion control formulation is presented that takes advantage of the inherent dynamic structure of the state-space description of a large class of systems. The formulations impose the exact kinematic differential equations, thereby restricting the adaptation process that compensates for model errors to the acceleration level. The utility of this formulation is demonstrated for the problem of fault tolerance to actuator failures on redundantly actuated systems. The approach incorporates an actuator failure model in the controller formulation, so that actuator failure can be identified as a change in the parameters of the failure model. Tracking of reference trajectories is imposed, and initial error conditions and structured parametric uncertainties are incorporated explicitly in both the plant parameters and the control influence matrix. A numerical example consisting of a nonlinear model of an F-16 type aircraft with thrust vectoring is presented. Simulation results show that the fault-tolerant adaptive controller is capable of simultaneously handling parametric uncertainties, large initial condition errors, and actuator failures while maintaining adequate tracking performance. N recent years, there has been much interest in the development of reconfigurable control systems that can accommodate actuator failures without compromising mission integrity. There has been substantial progress in the development of real-time failure detection and isolation algorithms, system identification after failure, and control reconfiguration techniques in aerospace applications. In Ref. 1, a survey of various reconfigurable flight control methodologies is presented and it is shown that most traditional reconfiguration flight control approaches rely on failure detection and isolation. The complexity of such a system with this feature grows with the increase in the number of failures, and there tends to be a significant possibility of false alarms. 1,2 A different approach to reconfigurable flight control is based on adaptive control theory, in which the adaptive control structure implicitly reconfigures the control law using adaptive estimates of the altered dynamics after failure. 3 In Ref. 3, an adaptive control scheme is presented that uses a linear approximation of the plant model to compute the control, and a neural network based adaptive control law for flight reconfiguration has been developed and successfully flight tested. 4−6 A robust fault-tolerant controller has also been developed to reject state-dependent disturbances. 7 The approach presented in this paper uses a structured nonlinear adaptive dynamic inversion control methodology. Instead of using an explicit failure detection and isolation algorithm, this methodology is based on the adaptive control theory where the controller is constantly updating itself. This methodology is applicable to a general class of nonlinear systems that are affine in the control with uncertain parameters appearing linearly. Fault-tolerance capability is introduced by incorporating a failure model in the controller so that a failure can be identified and compensated for by a change in the parameters of the failure model. First, model reference adaptive control, structured model reference adaptive control, and structured adaptive model inversion

Particle Filter for Ballistic Target Tracking with Glint Noise

The performance of ballistic target interception is critically dependent on the performance of the target state estimation. The estimation performance then strongly depends on the accuracy of the measurement model. The Gaussian uncertainty distribution has commonly been used for representing the statistical properties of sensor noise, due to its mathematical simplicity and effectiveness. However, seeker sensor measurements are often corrupted by glint noise which is highly non-Gaussian, and conventional Gaussian filtering algorithms are known to show unsatisfactory performance in the presence of glint noise. This research proposes the use of a particle filter for ballistic target tracking in a glint noise environment. The target tracking performance of the particle filter is compared with that of the extended Kalman filter.

Queueing Network Models of the National Airspace System

Understanding the relationships between trajectory uncertainties due to aviation operations, precision of navigation and control, and the traffic flow efficiency are central to the design of next generation Air Transportation Systems. Monte-Carlo simulations using air traffic simulation software packages can be used to quantify these effects. However, they are generally time consuming, and do not provide explicit relationships for comparing various technology options. On the other hand, queuing models of the air traffic system can rapidly demonstrate the influence of trajectory uncertainties on traffic flow efficiency, facilitating tradeoff studies in an effective and time-efficient manner. A methodology for incorporating the trajectory uncertainty models into queuing network models of the air traffic at national, regional and local scales is discussed. Usefulness of these models in assessing the impact of uncertainties on traffic flow efficiency is illustrated.

Structured adaptive model inversion control with actuator saturation constraints applied to tracking spacecraft maneuvers

This work presents an adaptive control methodology that facilitates correct adaptation in the presence of actuator saturation constraints, model errors and initial condition errors. The central idea is to modify the reference trajectory on saturation, in such a way that the modified trajectory approximates the original reference closely, and can be tracked within saturation limits. Asymptotic stability of the tracking errors between the plant trajectories and the modified reference, and bounded learning of the adaptive parameters is guaranteed. Simulation results are presented for tracking of an attitude reorientation trajectory for a rigid spacecraft.

STRUCTURED ADAPTIVE MODEL INVERSION CONTROL TO SIMULTANEOUSLY HANDLE ACTUATOR FAILURE AND ACTUATOR SATURATION

Traditional adaptive control lacks rigorous theoretical treatment for control in the presence of actuator saturation. Generally, adaptation is stopped as soon as the control saturates to avoid incorrect adaptation. Adaptation in the presence of saturation may be critical, especially when the controller is recovering from a failure. This paper presents an Adaptive Control methodology that facilitates correct adaptation in the presence of actuation saturation limits. The central idea is to modify the reference trajectory on saturation, in such a way that the modified trajectory approximates the original reference as close as possible, and can be tracked within saturation limits. Nonlinear six degree of freedom simulations of an F-16 type aircraft are shown to demonstrate this control scheme.

4D Green Trajectory Design for Terminal Area Operations Using Nonlinear Optimization Techniques

The work under this research deals with the development of a computational framework suitable for the design and analysis of 4D green trajectories for terminal airspace operations. First, a 4D-trajectory-based operational concept for terminal area operation consisting of ground-side automation and flight-deck-side automation is presented. The focus of the current paper is the development of 4D-trajectory design tools as part of the ground-side automation. The paper first identifies aircraft aerodynamic, fuel consumption, emissions, and noise models necessary for trajectory optimization based on open-source data such as the Base of Aircraft DAta (BADA). A numerical trajectory optimization framework is then proposed for the design of 4D-trajectories. The framework is able to accommodate aircraft performance constraints, separation constraints, and airport capacity considerations, and it can model “green” considerations such as fuel & emissions minimization, and noise reduction. The trajectory optimization framework is demonstrated on single and multiple aircraft scenarios. Using parametric optimization approach the paper explores the relationship between the time-of-arrival at runway threshold and the fuel consumption for a B737 aircraft. In the multi-aircraft scenario the paper illustrates the implementation of 3 nmi separation criteria between a pair of aircraft. A companion paper deals with the flight-deck-side automation that tracks the 4D trajectory clearances created by the groundside automation.

A Queuing Framework for Terminal Area Operations

As a part of NASA’s NextGen research effort, the focus area of Airspace Super-Density Operations (ASDO) performs research pertaining to highly efficient operations at the busiest airports and terminal airspaces. It is expected that multiple ASDO concepts will be interacting with one another in a complex stochastic manner. This research effort developed a high-fidelity queuing model of the terminal area suitable for the design and analysis of NextGen ASDO concepts, as well as to perform time-varying stochastic analysis of terminal area operations with regards to schedule and wind uncertainties. A unique aspect of the current approach is the discretization of terminal airspace routes into 3-nmi servers for enforcing separation requirements. The current research effort developed high-fidelity queuing models of the San Francisco International Airport (SFO) terminal airspace, based on published airspace geometry. A discrete-event simulation framework was developed to simulate the temporal evolution of flights in the terminal area. The queuing simulation framework was used in different case studies involving various phenomena in the terminal area such as compression, conflict and delay analysis, runway reconfiguration and variable inter-aircraft separation. In addition to being a useful analysis tool, the proposed simulation framework shows potential as a real time stochastic decision support tool due to its low computational cost.