Direct Adaptive Control for a Trajectory Tracking UAV

This research focuses on the theoretical development and analysis of a direct adaptive control algorithm to enable a fixed-wing UAV to track reference trajectories while in the presence of persistent external disturbances. A typical application of this work is autonomous flight through urban environments, where reference trajectories would be provided by a path planning algorithm and the vehicle would be subjected to significant wind gust disturbances. Full 6-DOF nonlinear and linear UAV simulation models are developed and used to study the performance of the direct adaptive control system for various scenarios. A stability proof is developed to prove convergence of the direct adaptive control system under certain conditions. Specific adaptive controller implementation details are provided, including the use of a sensor blending algorithm to address the non-minimum phase properties of the UAV models. The robustness of the adaptive system pertaining to the amount of modeling error that can be accommodated by the controller is studied, and the disturbance rejection capabilities and limitations of the controllers are also analyzed. The overall results of this research demonstrate that the direct adaptive control algorithm can enable trajectory tracking in cases where there are both significant uncertainties in the external disturbances and considerable error in the UAV model

Monitoring and Control Framework for Advanced Power Plant Systems Using Artificial Intelligence Techniques

This dissertation presents the design, development, and simulation testing of a monitoring and control framework for dynamic systems using artificial intelligence techniques. A comprehensive monitoring and control system capable of detecting, identifying, evaluating, and accommodating various subsystem failures and upset conditions is presented. The system is developed by synergistically merging concepts inspired from the biological immune system with evolutionary optimization algorithms and adaptive control techniques.;The proposed methodology provides the tools for addressing the complexity and multi-dimensionality of the modern power plants in a comprehensive and integrated manner that classical approaches cannot achieve. Current approaches typically address abnormal condition (AC) detection of isolated subsystems of low complexity, affected by specific AC involving few features with limited identification capability. They do not attempt AC evaluation and mostly rely on control system robustness for accommodation. Addressing the problem of power plant monitoring and control under AC at this level of completeness has not yet been attempted.;Within the proposed framework, a novel algorithm, namely the partition of the universe, was developed for building the artificial immune system self. As compared to the clustering approach, the proposed approach is less computationally intensive and facilitates the use of full-dimensional self for system AC detection, identification, and evaluation. The approach is implemented in conjunction with a modified and improved dendritic cell algorithm. It allows for identifying the failed subsystems without previous training and is extended to address the AC evaluation using a novel approach.;The adaptive control laws are designed to augment the performance and robustness of baseline control laws under normal and abnormal operating conditions. Artificial neural network-based and artificial immune system-based approaches are developed and investigated for an advanced power plant through numerical simulation.;This dissertation also presents the development of an interactive computational environment for the optimization of power plant control system using evolutionary techniques with immunity-inspired enhancements. Several algorithms mimicking mechanisms of the immune system of superior organisms, such as cloning, affinity-based selection, seeding, and vaccination are used. These algorithms are expected to enhance the computational effectiveness, improve convergence, and be more efficient in handling multiple local extrema, through an adequate balance between exploration and exploitation.;The monitoring and control framework formulated in this dissertation applies to a wide range of technical problems. The proposed methodology is demonstrated with promising results using a high validity DynsimRTM model of the acid gas removal unit that is part of the integrated gasification combined cycle power plant available at West Virginia University AVESTAR Center. The obtained results show that the proposed system is an efficient and valuable technique to be applied to a real world application. The implementation of this methodology can potentially have significant impacts on the operational safety of many complex systems

Deep Learning-Based, Passive Fault Tolerant Control Facilitated by a Taxonomy of Cyber-Attack Effects

In the interest of improving the resilience of cyber-physical control systems to better operate in the presence of various cyber-attacks and/or faults, this dissertation presents a novel controller design based on deep-learning networks. This research lays out a controller design that does not rely on fault or cyber-attack detection. Being passive, the controller’s routine operating process is to take in data from the various components of the physical system, holistically assess the state of the physical system using deep-learning networks and decide the subsequent round of commands from the controller. This use of deep-learning methods in passive fault tolerant control (FTC) is unique in the research literature. The proposed controller is applied to both linear and nonlinear systems. Additionally, the application and testing are accomplished with both actuators and sensors being affected by attacks and /or faults

Automation and Control Architecture for Hybrid Pipeline Robots

The aim of this research project, towards the automation of the Hybrid Pipeline Robot (HPR), is the development of a control architecture and strategy, based on reconfiguration of the control strategy for speed-controlled pipeline operations and self-recovering action, while performing energy and time management. The HPR is a turbine powered pipeline device where the flow energy is converted to mechanical energy for traction of the crawler vehicle. Thus, the device is flow dependent, compromising the autonomy, and the range of tasks it can perform. The control strategy proposes pipeline operations supervised by a speed control, while optimizing the energy, solved as a multi-objective optimization problem. The states of robot cruising and self recovering, are controlled by solving a neuro-dynamic programming algorithm for energy and time optimization, The robust operation of the robot includes a self-recovering state either after completion of the mission, or as a result of failures leading to the loss of the robot inside the pipeline, and to guaranteeing the HPR autonomy and operations even under adverse pipeline conditions Two of the proposed models, system identification and tracking system, based on Artificial Neural Networks, have been simulated with trial data. Despite the satisfactory results, it is necessary to measure a full set of robot’s parameters for simulating the complete control strategy. To solve the problem, an instrumentation system, consisting on a set of probes and a signal conditioning board, was designed and developed, customized for the HPR’s mechanical and environmental constraints. As a result, the contribution of this research project to the Hybrid Pipeline Robot is to add the capabilities of energy management, for improving the vehicle autonomy, increasing the distances the device can travel inside the pipelines; the speed control for broadening the range of operations; and the self-recovery capability for improving the reliability of the device in pipeline operations, lowering the risk of potential loss of the robot inside the pipeline, causing the degradation of pipeline performance. All that means the pipeline robot can target new market sectors that before were prohibitive

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 368)

This bibliography lists 305 reports, articles, and other documents introduced into the NASA Scientific and Technical Information System during Sep. 1992. The subject coverage concentrates on the biological, physiological, psychological, and environmental effects to which humans are subjected during and following simulated or actual flight in the Earth's atmosphere or in interplanetary space. References describing similar effects on biological organisms of lower order are also included. Such related topics as sanitary problems, pharmacology, toxicology, safety and survival, life support systems, exobiology, and personnel factors receive appropriate attention. Applied research receives the most emphasis, but references to fundamental studies and theoretical principles related to experimental development also qualify for inclusion

Making intelligent systems team players: Case studies and design issues. Volume 1: Human-computer interaction design

Initial results are reported from a multi-year, interdisciplinary effort to provide guidance and assistance for designers of intelligent systems and their user interfaces. The objective is to achieve more effective human-computer interaction (HCI) for systems with real time fault management capabilities. Intelligent fault management systems within the NASA were evaluated for insight into the design of systems with complex HCI. Preliminary results include: (1) a description of real time fault management in aerospace domains; (2) recommendations and examples for improving intelligent systems design and user interface design; (3) identification of issues requiring further research; and (4) recommendations for a development methodology integrating HCI design into intelligent system design

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

Highly redundant and fault tolerant actuator system: control, condition monitoring and experimental validation

This thesis is concerned with developing a control and condition monitoring system for a class of fault tolerant actuators with high levels of redundancy. The High Redundancy Actuator (HRA) is a concept inspired by biomimetics that aims to provide fault tolerance using relatively large numbers of actuation elements which are assembled in parallel and series configurations to form a single actuator. Each actuation element provides a small contribution to the overall force and displacement of the system. Since the capability of each actuation element is small, the effect of faults within the individual element of the overall system is also small. Hence, the HRA will gracefully degrade instead of going from fully functional to total failure in the presence of faults. Previous research on HRA using electromechanical technology has focused on a relatively low number of actuation elements (i.e. 4 elements), which were controlled with multiple loop control methods. The objective of this thesis is to expand upon this, by considering an HRA with a larger number of actuation elements (i.e. 12 elements). First, a mathematical model of a general n-by-m HRA is derived from first principles. This method can be used to represent any size of electromechanical HRA with actuation elements arranged in a matrix form. Then, a mathematical model of a 4-by-3 HRA is obtained from the general n-by-m model and verified experimentally using the HRA test rig. This actuator model is then used as a foundation for the controller design and condition monitoring development. For control design, two classical and control method-based controllers are compared with an H_infinity approach. The objective for the control design is to make the HRA track a position demand signal in both health and faulty conditions. For the classical PI controller design, the first approach uses twelve local controllers (1 per actuator) and the second uses only a single global controller. For the H_infinity control design, a mixed sensitivity functions is used to obtain good tracking performance and robustness to modelling uncertainties. Both of these methods demonstrate good tracking performance, with a slower response in the presence of faults. As expected, the H_infinity control method's robustness to modelling uncertainties, results in a smaller performance degradation in the presence of faults, compared with the classical designs. Unlike previous work, the thesis also makes a novel contribution to the condition monitoring of HRA. The proposed algorithm does not require the use of multiple sensors. The condition monitoring scheme is based on least-squares parameter estimation and fuzzy logic inference. The least-squares parameter estimation estimates the physical parameters of the electromechanical actuator based on input-output data collected from real-time experiments, while the fuzzy logic inference determines the health condition of the actuator based on the estimated physical parameters. Hence, overall, a new approach to both control and monitoring of an HRA is proposed and demonstrated on a twelve elements HRA test rig

A Hybrid Nonlinear Model Predictive Control and Recurrent Neural Networks for Fault-Tolerant Control of an Autonomous Underwater Vehicle

The operation of Autonomous Unmanned Vehicles (AUVs) that is used for environment protection, risk evaluation and plan determination for emergency, are among the most important and challenging problems. An area that has received much attention for use of AUVs is in underwater applications where much work remains to be done to equip AUVs with systems to steer them accurately and reliably in harsh marine environments. Design of control strategies for AUVs is very challenging as compared to other systems due to their operational environment (ocean). Particularly when hydrodynamic parameters uncertainties are to be integrated into both the controller design as well as AUVs nonlinear dynamics. On the other hand, AUVs like all other mechanical systems are prone to faults. Dealing effectively with faulty situations for mechanical systems is an important consideration since faults can result in abnormal operation or even a failure. Hence, fault tolerant and fault-accommodating methods in the controller design are among active research topics for maintaining the reliability of complex AUV control systems. The objective of this thesis is to develop a nonlinear Model Predictive Control (MPC) that requires solving an online Quadratic Programming (QP) problem by using a Recurrent Neural Network (RNN). Also, an Extended Kalman Filter (EKF) is integrated with the developed scheme to provide the MPC algorithm with the system states estimates as well as a nonlinear prediction. This hybrid control approach utilizes both the mathematical model of the system as well as the adaptive nature of the intelligent technique through neural networks. The reason behind the selection of MPC is to benefit from its main capability in optimization within the current time slots while taking future time slots into consideration. The proposed control method is integrated with EKF which is an appropriate method for state estimation and data reconciliation of nonlinear systems. In order to address the high performance runtime cost of solving the MPC problem (formulated as a quadratic programming problem), an RNN is developed that has a low model complexity as well as good performance in real-time implementation. The proposed method is first developed to control an AUV following a desired trajectory. Since the problem of trajectory tracking and path following of AUVs exhibit nonlinear behavior, the effectiveness of the developed MPC-RNN algorithm is studied in comparison with two other control system methods, namely the linear MPC using Kalman Filter (KF) and the conventional nonlinear MPC using the EKF. In order to guarantee the fault-tolerant features of our proposed control method when faced with severe actuator faults, the developed MPC-RNN scheme is integrated with a dual Extended Kalman Filter that is used for a combined estimation of AUV states and parameters. The actuator faults are defined as the system parameters that are to be estimated online by the dual-EKF. Therefore, the developed Active Fault-Tolerant Control (AFTC) strategy is then applied to an AUV faced with loss of effectiveness (LOE) actuator fault scenarios while following a trajectory. Analysis and discussions regarding the comparison of the proposed AFTC method with Fault-Tolerant Nonlinear Model Predictive Control (FTNMPC) algorithm are presented in this work. The proposed approach to AFTC exploits the advantages of the MPC-RNN algorithm properties as well as accounting explicitly for severe control actuator faults in the nonlinear AUV model with uncertainties that are formulated by the MPC