Fault tolerant control of a quadrotor using L-1 adaptive control

Purpose – The growing use of small unmanned rotorcraft in civilian applications means that safe operation is increasingly important. The purpose of this paper is to investigate the fault tolerant properties to faults in the actuators of an L1 adaptive controller for a quadrotor vehicle. Design/methodology/approach – L1 adaptive control provides fast adaptation along with decoupling between adaptation and robustness. This makes the approach a suitable candidate for fault tolerant control of quadrotor and other multirotor vehicles. In the paper, the design of an L1 adaptive controller is presented. The controller is compared to a fixed-gain LQR controller. Findings – The L1 adaptive controller is shown to have improved performance when subject to actuator faults, and a higher range of actuator fault tolerance. Research limitations/implications – The control scheme is tested in simulation of a simple model that ignores aerodynamic and gyroscopic effects. Hence for further work, testing with a more complete model is recommended followed by implementation on an actual platform and flight test. The effect of sensor noise should also be considered along with investigation into the influence of wind disturbances and tolerance to sensor failures. Furthermore, quadrotors cannot tolerate total failure of a rotor without loss of control of one of the degrees of freedom, this aspect requires further investigation. Practical implications – Applying the L1 adaptive controller to a hexrotor or octorotor would increase the reliability of such vehicles without recourse to methods that require fault detection schemes and control reallocation as well as providing tolerance to a total loss of a rotor. Social implications – In order for quadrotors and other similar unmanned air vehicles to undertake many proposed roles, a high level of safety is required. Hence the controllers should be fault tolerant. Originality/value – Fault tolerance to partial actuator/effector faults is demonstrated using an L1 adaptive controller

Adaptive Learning: An Innovative Method for Online Teaching and Learning

The term adaptive learning refers to a nonlinear approach to online instruction that adjusts to a student\u27s needs as the student progresses through course content, resulting in a customized experience for the learner based on prior knowledge. This concept is emerging in the field of online learning. Through a project funded by the eXtension Foundation, we reviewed and conducted pilot testing on adaptive learning tools for Extension programming. We found that the adaptive learning format aided learners in mastering content. A significant contribution to the Extension community resulting from our project is improved understanding of an innovative way of teaching online

Wearable Haptic Devices for Long-Term Gait Re-education for Neurological Conditions

Many people with long-term neurological and neurodegenerative conditions such as stroke, brain injury, multiple sclerosis or Parkinson’s disease suffer from an impaired walking gait pattern. Gait improvement can lead to better fluidity in walking, improved health outcomes, greater independence, and enhanced quality of life. Existing lab-based studies with wearable haptic devices have shown that rhythmic haptic cueing can cause immediate improvements to gait features such as temporal symmetry, stride length and walking speed. However, such wearable haptic devices are unsuitable for self-managed use, and to move this approach from out of the lab into long-term sustained usage, numerous design challenges need to be addressed. We are designing, developing, and testing a closed-loop system to provide adaptive haptic rhythmic cues for sustainable self-managed long-term use outside the lab by survivors of stroke, and other neurological conditions, in their everyday lives

Wearables for Long Term Gait Rehabilitation of Neurological Conditions

Many people with long-term neurological and neurodegenerative conditions such as stroke, brain injury, multiple sclerosis or Parkinson’s disease suffer from an impaired walking gait pattern. Gait improvement can lead to better fluidity in walking, improved health outcomes, greater independence, and enhanced quality of life. Existing lab-based studies with wearable devices have shown that rhythmic haptic cueing can cause immediate improvements to gait features such as temporal symmetry, stride length and walking speed. However, current wearable systems are unsuitable for self-managed use, and to move this approach from out of the lab into long-term sustained usage, numerous design challenges need to be addressed. We are designing, developing, and testing a closed-loop system to provide adaptive haptic rhythmic cues for sustainable self-managed long-term use outside the lab by survivors of stroke, and other neurological conditions, in their everyday lives

On Degeneracy Issues in Multi-parametric Programming and Critical Region Exploration based Distributed Optimization in Smart Grid Operations

Improving renewable energy resource utilization efficiency is crucial to reducing carbon emissions, and multi-parametric programming has provided a systematic perspective in conducting analysis and optimization toward this goal in smart grid operations. This paper focuses on two aspects of interest related to multi-parametric linear/quadratic programming (mpLP/QP). First, we study degeneracy issues of mpLP/QP. A novel approach to deal with degeneracies is proposed to find all critical regions containing the given parameter. Our method leverages properties of the multi-parametric linear complementary problem, vertex searching technique, and complementary basis enumeration. Second, an improved critical region exploration (CRE) method to solve distributed LP/QP is proposed under a general mpLP/QP-based formulation. The improved CRE incorporates the proposed approach to handle degeneracies. A cutting plane update and an adaptive stepsize scheme are also integrated to accelerate convergence under different problem settings. The computational efficiency is verified on multi-area tie-line scheduling problems with various testing benchmarks and initial states

Motion Cueing Algorithm Development: Human-Centered Linear and Nonlinear Approaches

While the performance of flight simulator motion system hardware has advanced substantially, the development of the motion cueing algorithm, the software that transforms simulated aircraft dynamics into realizable motion commands, has not kept pace. Prior research identified viable features from two algorithms: the nonlinear "adaptive algorithm", and the "optimal algorithm" that incorporates human vestibular models. A novel approach to motion cueing, the "nonlinear algorithm" is introduced that combines features from both approaches. This algorithm is formulated by optimal control, and incorporates a new integrated perception model that includes both visual and vestibular sensation and the interaction between the stimuli. Using a time-varying control law, the matrix Riccati equation is updated in real time by a neurocomputing approach. Preliminary pilot testing resulted in the optimal algorithm incorporating a new otolith model, producing improved motion cues. The nonlinear algorithm vertical mode produced a motion cue with a time-varying washout, sustaining small cues for longer durations and washing out large cues more quickly compared to the optimal algorithm. The inclusion of the integrated perception model improved the responses to longitudinal and lateral cues. False cues observed with the NASA adaptive algorithm were absent. The neurocomputing approach was crucial in that the number of presentations of an input vector could be reduced to meet the real time requirement without degrading the quality of the motion cues

Path Planning and Performance Evaluation Strategies for Marine Robotic Systems

The field of marine robotics offers many new capabilities for completing dangerous missions such as deep-sea exploration and underwater demining. The harshness of marine environments, however, means that without effective onboard decision-making, vehicle loss or mission failure are likely. Thus, to enable more autonomous operation while building trust that these systems will perform as expected, this thesis develops improved path planning and testing strategies for two different types of marine robotic platforms. The first portion of the research focuses on improved environmental data collection with an autonomous underwater vehicle (AUV). Gaussian process-based modeling is combined with informative path planning to explore an environment, while preferentially collecting data in regions of interest that exhibit extreme sensory measurements. The performance of this adaptive data sampling framework with a torpedo-style AUV is studied in both simulation and field experiments. Results show that the proposed methodology is able to be fielded on an operational platform and collect measurements in regions of interest without sacrificing overall model fidelity of the full sampling area. The second portion of the research then focuses on autonomous surface vessel (ASV) navigation that must comply with international collision avoidance standards and basic ship handling principles. The approach introduces a novel quantification of good seamanship that is used within an ASV path planner to minimize the collision risk with other vessels. This approach generalizes well to both single-vessel and multi-vessel encounters by avoiding rule-based conditions. The performance of this ASV planning strategy is evaluated in simulation against other baseline planners, and the results of on-water testing with a 29-ft ASV demonstrate that the approach is scalable to real systems. Beyond developing improved path planning frameworks, this research also explores methods for improved testing and evaluation of black-box autonomous systems. Statistical learning techniques such as adaptive scenario generation and unsupervised clustering are used to extract the failure modes of the autonomy from large-scale simulation datasets. Subsequently, changes in these failure modes are tracked in a novel form of performance-based regression testing. The effectiveness of this testing framework is demonstrated on the aforementioned ASV planner by discovering several types of unexpected failures

Large Binocular Telescope Interferometer Adaptive Optics: On-sky performance and lessons learned

The Large Binocular Telescope Interferometer is a high contrast imager and interferometer that sits at the combined bent Gregorian focus of the LBT's dual 8.4~m apertures. The interferometric science drivers dictate 0.1'' resolution with $10^3-10^4$ contrast at $10~\mu m$, while the $4~\mu m$ imaging science drivers require even greater contrasts, but at scales $>$0.2''. In imaging mode, LBTI's Adaptive Optics system is already delivering $4~\mu m$ contrast of $10^4-10^5$ at $0.3''-0.75''$ in good conditions. Even in poor seeing, it can deliver up to 90\% Strehl Ratio at this wavelength. However, the performance could be further improved by mitigating Non-Common Path Aberrations. Any NCPA remedy must be feasible using only the current hardware: the science camera, the wavefront sensor, and the adaptive secondary mirror. In preliminary testing, we have implemented an ``eye doctor'' grid search approach for astigmatism and trefoil, achieving 5\% improvement in Strehl Ratio at $4~\mu m$, with future plans to test at shorter wavelengths and with more modes. We find evidence of NCPA variability on short timescales and discuss possible upgrades to ameliorate time-variable effectsComment: Published in Proceedings of SPIE, vol 9148: Adaptive Optics Systems I

A framework for generalized group testing with inhibitors and its potential application in neuroscience

The main goal of group testing with inhibitors (GTI) is to efficiently identify a small number of defective items and inhibitor items in a large set of items. A test on a subset of items is positive if the subset satisfies some specific properties. Inhibitor items cancel the effects of defective items, which often make the outcome of a test containing defective items negative. Different GTI models can be formulated by considering how specific properties have different cancellation effects. This work introduces generalized GTI (GGTI) in which a new type of items is added, i.e., hybrid items. A hybrid item plays the roles of both defectives items and inhibitor items. Since the number of instances of GGTI is large (more than 7 million), we introduce a framework for classifying all types of items non-adaptively, i.e., all tests are designed in advance. We then explain how GGTI can be used to classify neurons in neuroscience. Finally, we show how to realize our proposed scheme in practice

A Family of Maximum Margin Criterion for Adaptive Learning

In recent years, pattern analysis plays an important role in data mining and recognition, and many variants have been proposed to handle complicated scenarios. In the literature, it has been quite familiar with high dimensionality of data samples, but either such characteristics or large data have become usual sense in real-world applications. In this work, an improved maximum margin criterion (MMC) method is introduced firstly. With the new definition of MMC, several variants of MMC, including random MMC, layered MMC, 2D^2 MMC, are designed to make adaptive learning applicable. Particularly, the MMC network is developed to learn deep features of images in light of simple deep networks. Experimental results on a diversity of data sets demonstrate the discriminant ability of proposed MMC methods are compenent to be adopted in complicated application scenarios.Comment: 14 page