Vibhor L. Bageshwar

Model predictive control of transitional maneuvers for adaptive cruise control vehicles

In this paper, model predictive control (MPC) is used to compute the spacing-control laws for transitional maneuvers (TMs) of vehicles equipped with adaptive cruise control (ACC) systems. A TM is required, for example, to establish a steady-state following distance behind a newly encountered vehicle traveling with a slower velocity. These spacing-control laws are computed by formulating the objective of a TM as an optimal control problem (OCP). The steady-state following distance, collision avoidance, and acceleration limits of the ACC vehicle are incorporated into the OCP as constraints. The spacing-control laws are then obtained by solving this constrained OCP by using a receding-horizon approach, where the acceleration command computed at each sampling instant is a function of the current measurements of range and range rate. A baseline scenario requiring a TM is used to evaluate and compare the performance of the MPC algorithm and the standard constant time gap (CTG) algorithm. The simulation results show that the ACC vehicle is able to perform the TM of the baseline scenario using the MPC spacing-control laws, whereas the ACC vehicle is unable to perform this TM using the CTG spacing-control laws. The success of the MPC spacing-control laws is shown to depend on whether collision avoidance and the acceleration limits of the ACC vehicle are explicitly incorporated into the formulation of the control algorithm.

Stochastic Observability Test for Discrete-Time Kalman Filters

Stochastic observability refers to the existence of a filter for which the errors of the estimated state mean vector have bounded variance. In this paper, we derive a test to assess the stochastic observability of a Kalman filter implemented for discrete linear time-varying stochastic systems. This test is derived with the assumptions that the system matrices consist of known deterministic parameters and that there is complete uncertainty in the statistics of the initial state vector. This test can also be used to assess the stochastic observability of extended Kalman filters implemented for nonlinear stochastic systems linearized about the true state vector trajectory. We illustrate the utility of the stochastic observability test using an aided inertial navigation system. We also provide a counterexample to illustrate that observability is a necessary, but not sufficient, condition for the stochastic observability of a Kalman filter implemented for a system.

On a Property of a Class of Offset-Free Model Predictive Controllers

We consider a model predictive control framework that includes a discrete-time linear time-invariant nominal plant model augmented with an output integrator disturbance model and a Kalman filter to estimate the state and disturbance vectors. While the application of this framework can guarantee offset-free control, it has shown a consistent limitation in the achievable closed loop estimator performance. Using root locus techniques, we identify sufficient conditions for a class of nominal plant models with at least one real pole for which the closed loop estimator poles cannot be arbitrarily selected regardless of the augmented systems statistics. We present several examples illustrating the limitations of the closed loop estimator pole locations.

Sensor fusion for GNSS denied navigation

We present technologies that are being developed to address the need for a navigation solution in the absence of Global Navigation Satellite Systems (GNSS) measurements. The navigation system uses sensors such as vision systems, RADARS and LIDARS with feature extraction, matching and motion estimation algorithms. We present experimental results of using scale invariant feature transform, speeded up robust features, and modified Harris feature extraction algorithms and compare the performance. We also present methods to extract lines and planes that can aid in navigation. For motion estimation we present results for visual odometry as well as simultaneous localization and mapping navigation. We experimentally verify the algorithms in both a realtime Linux framework as well as offline. We also present ongoing work in vision integrated navigation in an attitude and heading reference system as well as an extended Kalman filter framework. All the methods we present in this paper are incremental navigation methods.

Observer pole placement limitations for a class of offset-free model predictive controllers

Model predictive control (MPC) algorithms achieve offset-free control by augmenting the nominal system model with a disturbance model. The disturbance vector is used to predict the mismatch between the measured and predicted output vectors. In this paper, we consider an offset-free MPC framework that includes an output disturbance model and a Kalman filter to estimate the state and disturbance vectors. Using root locus techniques, we identify sufficient conditions for a class of nominal systems with at least one real positive pole for which the closed loop estimator poles cannot be arbitrarily selected. We present several examples illustrating the limitations of the closed loop estimator pole locations.

An Alternative Time Metric to Modified Tau for Unmanned Aircraft System Detect And Avoid

This paper documents a study that drove the development of a mathematical expression in the detect-and-avoid (DAA) minimum operational performance standards (MOPS) for unmanned aircraft systems (UAS). This equation describes the conditions under which vertical maneuver guidance should be provided during recovery of DAA well clear separation with a non-cooperative VFR aircraft. Although the original hypothesis was that vertical maneuvers for DAA well clear recovery should only be offered when sensor vertical rate errors are small, this paper suggests that UAS climb and descent performance should be considered—in addition to sensor errors for vertical position and vertical rate—when determining whether to offer vertical guidance. A fast-time simulation study involving 108,000 encounters between a UAS and a non-cooperative visual-flight-rules aircraft was conducted. Results are presented showing that, when vertical maneuver guidance for DAA well clear recovery was suppressed, the minimum vertical separation increased by roughly 50 feet (or horizontal separation by 500 to 800 feet). However, the percentage of encounters that had a risk of collision when performing vertical well clear recovery maneuvers was reduced as UAS vertical rate performance increased and sensor vertical rate errors decreased. A class of encounter is identified for which vertical-rate error had a large effect on the efficacy of horizontal maneuvers due to the difficulty of making the correct left/right turn decision: crossing conflict with intruder changing altitude. Overall, these results support logic that would allow vertical maneuvers when UAS vertical performance is sufficient to avoid the intruder, based on the intruder’s estimated vertical position and vertical rate, as well as the vertical rate error of the UAS’ sensor. To read the full paper please visit https://www.nasa.gov/aeroresearch/programs/iasp/uas/abstracts

Multi-intruder aircraft, multi-sensor tracking system

The National Airspace is evolving with the introduction of new types of aircraft and increased surveillance and safety requirements. Detect and Avoid (DAA) systems are designed to meet these requirements for both cooperative and non-cooperative air traffic. In this paper, we describe the Honeywell Tracking System (HTS) which provides DAA systems with accurate and smooth tracks for cooperative and non-cooperative intruder aircraft relative to an ownship aircraft. The HTS uses a combination of cooperative and non-cooperative sensors to track thirty intruder aircraft within one framework in real-time. We demonstrate the performance of the HTS using the results of two flight tests with cooperative and non-cooperative intruder aircraft.

Multi-intruder, multi-sensor tracking system

This article consists of a collection of slides from the authors conference presentation.