ONLINE PILOT MODEL PARAMETER ESTIMATION FOR LOSS-OF-CONTROL PREVENTION IN AIRCRAFT SYSTEMS

A pilot is a highly nonlinear and incredibly complex controller whose responses are difficult to predict. Many accidents have occurred from pilot error before or after failures and almost always after entering areas of the flight envelope considered as Loss-of-Control regimes. If a pilot\u27s inputs to the flight control system can be predicted, then the introduction of dangerous flight conditions can be readily avoided. Avoidance could take the form of a warning indicator or augmentation of the pilot\u27s inputs. The primary difficulty lies in how to actually predict how the pilot will perform in the future. Methods to solve this problem are focused around the McRuer pilot model which simplifies the pilot response to a four-parameter equation that has been the focus of most recent solutions. Many recent attempts at solving this problem have found promising results in Wavelets, Most Likelihood Estimation, Extended Kalman Filters, and Unscented Kalman Filters. This thesis applies two new methods to the estimation problem and suggests a modification to one. The three methods investigated in this thesis are a modified form of the Unscented Kalman Filter, Fourier Transform Regression with Time Domain derivatives, and Adaptive Neural Networks. The Unscented Kalman Filter holds merit in many estimation problems for its ability to handle model nonlinearities and noise in the systems and sensors. In this respect, it held the best solution for this work given that it could correctly estimate the parameters. However, the filter had to be finely tuned to reach a solution. The Fourier Transform Regression method could only handle time-invariant pilot model parameters due to its usage of batches of data. Once the parameters began varying with time, the solutions began having singularities. The adaptive neural networks showed promise being that they are stochastic estimators, but the solutions held within show they need more development to become a viable solution to this problem. It is recommended that deep reinforcement learning or combinations of these algorithms be applied to this estimation problem in the future to determine a more robust solution that can estimate the pilot\u27s intent online

Estimation, planning, and mapping for autonomous flight using an RGB-D camera in GPS-denied environments

RGB-D cameras provide both color images and per-pixel depth estimates. The richness of this data and the recent development of low-cost sensors have combined to present an attractive opportunity for mobile robotics research. In this paper, we describe a system for visual odometry and mapping using an RGB-D camera, and its application to autonomous flight. By leveraging results from recent state-of-the-art algorithms and hardware, our system enables 3D flight in cluttered environments using only onboard sensor data. All computation and sensing required for local position control are performed onboard the vehicle, reducing the dependence on an unreliable wireless link to a ground station. However, even with accurate 3D sensing and position estimation, some parts of the environment have more perceptual structure than others, leading to state estimates that vary in accuracy across the environment. If the vehicle plans a path without regard to how well it can localize itself along that path, it runs the risk of becoming lost or worse. We show how the belief roadmap algorithm prentice2009belief, a belief space extension of the probabilistic roadmap algorithm, can be used to plan vehicle trajectories that incorporate the sensing model of the RGB-D camera. We evaluate the effectiveness of our system for controlling a quadrotor micro air vehicle, demonstrate its use for constructing detailed 3D maps of an indoor environment, and discuss its limitations.United States. Office of Naval Research (Grant MURI N00014-07-1-0749)United States. Office of Naval Research (Science of Autonomy Program N00014-09-1-0641)United States. Army Research Office (MAST CTA)United States. Office of Naval Research. Multidisciplinary University Research Initiative (Grant N00014-09-1-1052)National Science Foundation (U.S.) (Contract IIS-0812671)United States. Army Research Office (Robotics Consortium Agreement W911NF-10-2-0016)National Science Foundation (U.S.). Division of Information, Robotics, and Intelligent Systems (Grant 0546467

Decoding of walking kinematics from non-invasively acquired electroencephalographic signals in stroke patients

Our group has recently shown the feasibility of decoding kinematics of controlled walking from the lower frequency range of electroencephalographic (EEG) signals during a precision walking task. Here, we turn our attention to stroke survivors who have had lesions resulting in hemiparetic gait. We recorded the EEG of stroke recovery patients during a precision treadmill walking task while tracking bilaterally the kinematics of the hips, knees, and ankles. In offline analyses, we applied a Wiener Filter and two unscented Kalman filters of 1st and 10th orders to predict estimates of the kinematic parameters from scalp EEG. Decoding accuracies from four patients who have had cortical and subcortical strokes were comparable with previous studies in healthy subjects. With improved decoding of EEG signals from damaged brains, we hope we can soon correlate activity to more intentional and normal-form walking that can guide users of a powered lower-body prosthetic or exoskeleton

Distributed implementations of the particle filter with performance bounds

The focus of the thesis is on developing distributed estimation algorithms for systems with nonlinear dynamics. Of particular interest are the agent or sensor networks (AN/SN) consisting of a large number of local processing and observation agents/nodes, which can communicate and cooperate with each other to perform a predefined task. Examples of such AN/SNs are distributed camera networks, acoustic sensor networks, networks of unmanned aerial vehicles, social networks, and robotic networks. Signal processing in the AN/SNs is traditionally centralized and developed for systems with linear dynamics. In the centralized architecture, the participating nodes communicate their observations (either directly or indirectly via a multi-hop relay) to a central processing unit, referred to as the fusion centre, which is responsible for performing the predefined task. For centralized systems with linear dynamics, the Kalman filter provides the optimal approach but suffers from several drawbacks, e.g., it is generally unscalable and also susceptible to failure in case the fusion centre breaks down. In general, no analytic solution can be determined for systems with nonlinear dynamics. Consequently, the conventional Kalman filter cannot be used and one has to rely on numerical approaches. In such cases, the sequential Monte Carlo approaches, also known as the particle filters, are widely used as approximates to the Bayesian estimators but mostly in the centralized configuration. Recently there has been a growing interest in distributed signal processing algorithms where: (i) There is no fusion centre; (ii) The local nodes do not have (require) global knowledge of the network topology, and; (iii) Each node exchanges data only within its local neighborhood. Distributed estimation have been widely explored for estimation/tracking problems in linear systems. Distributed particle filter implementations for nonlinear systems are still in their infancy and are the focus of this thesis. In the first part of this thesis, four different consensus-based distributed particle filter implementations are proposed. First, a constrained sufficient statistic based distributed implementation of the particle filter (CSS/DPF) is proposed for bearing-only tracking (BOT) and joint bearing/range tracking problems encountered in a number of applications including radar target tracking and robot localization. Although the number of parallel consensus runs in the CSS/DPF is lower compared to the existing distributed implementations of the particle filter, the CSS/DPF still requires a large number of iterations for the consensus runs to converge. To further reduce the consensus overhead, the CSS/DPF is extended to distributed implementation of the unscented particle filter, referred to as the CSS/DUPF, which require a limited number of consensus iterations. Both CSS/DPF and CSS/DUPF are specific to BOT and joint bearing/range tracking problems. Next, the unscented, consensus-based, distributed implementation of the particle filter (UCD /DPF) is proposed which is generalizable to systems with any dynamics. In terms of contributions, the UCD /DPF makes two important improvements to the existing distributed particle filter framework: (i) Unlike existing distributed implementations of the particle filter, the UCD /DPF uses all available global observations including the most recent ones in deriving the proposal distribution based on the distributed UKF, and; (ii) Computation of the global estimates from local estimates during the consensus step is based on an optimal fusion rule. Finally, a multi-rate consensus/fusion based framework for distributed implementation of the particle filter, referred to as the CF /DPF, is proposed. Separate fusion filters are designed to consistently assimilate the local filtering distributions into the global posterior by compensating for the common past information between neighbouring nodes. The CF /DPF offers two distinct advantages over its counterparts. First, the CF /DPF framework is suitable for scenarios where network connectivity is intermittent and consensus can not be reached between two consecutive observations. Second, the CF /DPF is not limited to the Gaussian approximation for the global posterior density. Numerical simulations verify the near-optimal performance of the proposed distributed particle filter implementations. The second half of the thesis focuses on the distributed computation of the posterior Cramer-Rao lower bounds (PCRLB). The current PCRLB approaches assume a centralized or hierarchical architecture. The exact expression for distributed computation of the PCRLB is not yet available and only an approximate expression has recently been derived. Motivated by the distributed adaptive resource management problems with the objective of dynamically activating a time-variant subset of observation nodes to optimize the network's performance, the thesis derives the exact expression, referred to as the dPCRLB, for computing the PCRLB for any AN/SN configured in a distributed fashion. The dPCRLB computational algorithms are derived for both the off-line conventional (non-conditional) PCRLB determined primarily from the state model, observation model, and prior knowledge of the initial state of the system, and the online conditional PCRLB expressed as a function of past history of the observations. Compared to the non-conditional dPCRLB, its conditional counterpart provides a more accurate representation of the estimator's performance and, consequently, a better criteria for sensor selection. The thesis then extends the dPCRLB algorithms to quantized observations. Particle filter realizations are used to compute these bounds numerically and quantify their performance for data fusion problems through Monte-Carlo simulations