John J. Deyst

A New Nonlinear Guidance Logic for Trajectory Tracking

A new nonlinear guidance logic, that has demonstrated superior performance in guiding unmanned air vehicles (UAVs) on curved trajectories, is presented. The logic approximates a proportional-derivative controller when following a straight line path, but the logic also contains an element of anticipatory control enabling tight tracking when following curved paths. The method uses inertial speed in the computation of commanded lateral acceleration and adds adaptive capability to the change of vehicle speed due to external disturbances, such as wind. Flight tests using two small UAVs showed that each aircraft was controlled to within 1.6 meters RMS when following circular paths. The logic was ultimately used for air rendezvous of the two aircraft, bringing them in close proximity to within 12 meters of separation, with 1.4 meters RMS relative position errors.

Performance and lyapunov stability of a nonlinear path-following guidance method

Performance and stability are demonstrated for a nonlinear path-following guidance method for unmanned air vehicles. The method was adapted from a pure pursuit-based path following, which has been widely used in ground based robot applications. The method is known to approximate a proportional-derivative controller when following a straight line path, but it is shown that there is also an element of anticipatory control that enables tight tracking when following curved paths. Ground speed is incorporated into the computation of commanded lateral acceleration, which adds an adaptive capability to accommodate vehicle speed changes due to external disturbances such as wind. Asymptotic Lyapunov stability of the nonlinear guidance method is demonstrated when the unmanned air vehicle is following circular paths. The adaptive nature of the guidance method makes its stability independent of vehicle velocity. The stability analysis is also extended to show robust stability of the guidance law in the presence of saturated lateral acceleration, which is an inherent limitation of flight vehicles. Flight tests of the algorithm, using two small unmanned air vehicles, showed that each aircraft was controlled to within 1.6 m root mean square when following circular paths. The method was used to perform a rendezvous of the two aircraft, bringing them into very close proximity, within 12 m of along track separation and 1.4 m root mean square relative position errors.

Architectural innovation in product development through early supplier integration

The paper explains how an important opportunity exists to pro-actively integrate suppliers at an early stage in the concept exploration and definition stages of product development. Research suggests that the concept of architectural innovation can be extended so that product features are matched with the associated specialized technical skills of partners in the development team. In addition to the establishment of integrated product teams, key enablers include: long-term commitment to suppliers; co-location; joint responsibility for design and configuration control; seamless information flow; and retaining flexibility in the definition of system configuration. Important contributing factors include: supplier-capability-enhancing investments; target costing; and incentive mechanisms. To promote innovative outcomes in military and government programmes, attention is drawn to the importance of governments championing closely-knit customer-supplier relationships. Firms can build enduring competitive strength by leveraging the specialized knowledge bases of their supplier networks. Two case-studies provide lessons to improve current approaches to the creation of long-term partnerships, or strategic alliances, with suppliers.

Conditions for asymptotic stability of the discrete minimum-variance linear estimator

Stability of the discrete homogeneous linear minimum-variance estimation formulas is investigated. Sufficient conditions for uniform asymptotic stability in the large are derived. The conditions, if satisfied, also imply stochastic controllability and observability of the plant.

Monte Carlo localization in dense multipath environments using UWB ranging

For most outdoor applications, systems such as GPS provide users with accurate position estimates. However, reliable range-based localization using radio signals in indoor or urban environments can be a problem due to multipath fading and line-of-sight (LOS) blockage. The measurement bias introduced by these delays causes significant localization error, even when using additional sensors such as an inertial measurement unit (IMU) to perform outlier rejection. We describe an algorithm for accurate indoor localization of a sensor in a network of known beacons. The sensor measures the range to the beacons using an Ultra-Wideband (UWB) signal and uses statistical inference to infer and correct for the bias due to LOS blockage in the range measurements. We show that a particle filter can be used to estimate the joint distribution over both pose and beacon biases. We use the particle filter estimation technique specifically to capture the non-linearity of transitions in the beacon bias as the sensor moves. Results using real-world and simulated data are presented.

Correction to "Conditions for asymptotic stability of the discrete minimum-variance linear estimator"

This note presents bounds on the discrete Riccati equation, correcting an error in proving the stability of discrete-time minimum variance estimators.

An Information-Theoretic Metric of System Complexity With Application to Engineering System Design

System complexity is considered a key driver of the inability of current system design practices to at times not recognize performance, cost, and schedule risks as they emerge. We present here a denition of system complexity and a quantitative metric for measuring that complexity based on information theory. We also derive sensitivity indices that indicate the fraction of complexity that can be reduced if more about certain factors of a system can become known. This information can be used as part of a resource allocation procedure aimed at reducing system complexity. Our methods incorporate Gaussian process emulators of expensive computer simulation models and account for both model inadequacy and code uncertainty. We demonstrate our methodology on a candidate design of an infantry ghting vehicle.

A Bayesian Framework for Uncertainty Quantication in the Design of Complex Systems

One of the main challenges of current system design practices is the inability to recognize performance, cost, and schedule risks as they emerge. This paper presents a Bayesian framework for the design of complex systems, in which uncertainty in various parameters and quantities of interest is characterized probabilistically, and updated through successive design iterations as new estimates become available. Incorporated in the proposed model are methods to quantify system complexity and risk, and reduce them through the allocation of resources for redesign and re nement. This approach enables the rigorous quanti cation and management of uncertainty, thereby serving to help mitigate technical and programmatic risk. The Bayesian system design framework is demonstrated on the notional design of a hybrid infantry ghting vehicle for military applications.