Safety Assurance of Non-Deterministic Flight Controllers in Aircraft Applications

Loss of control is a serious problem in aviation that primarily affects General Aviation. Technological advancements can help mitigate the problem, but the FAA certification process makes certain solutions economically unfeasible. This investigation presents the design of a generic adaptive autopilot that could potentially lead to a single certification for use in several makes and models of aircraft. The autopilot consists of a conventional controller connected in series with a robust direct adaptive model reference controller. In this architecture, the conventional controller is tuned once to provide outer-loop guidance and navigation to a reference model. The adaptive controller makes unknown aircraft behave like the reference model, allowing the conventional controller to successfully provide navigation without the need for retuning. A strong theoretical foundation is presented as an argument for the safety and stability of the controller. The stability proof of direct adaptive controllers require that the plant being controlled has no unstable transmission zeros and has a nonzero high frequency gain. Because most conventional aircraft do not readily meet these requirements, a process known as sensor blending was used. Sensor blending consists of using a linear combination of the plant’s outputs that has no unstable transmission zeros and has a nonzero high frequency gain to drive the adaptive controller. Although this method does not present a problem for regulators, it can lead to a steady state error in tracking applications. The sensor blending theory was expanded to take advantage of the system’s dynamics to allow for zero steady state error tracking. This method does not need knowledge of the specific system’s dynamics, but instead uses the structure of the A and B matrices to perform the blending for the general case. The generic adaptive autopilot was tested in two high-fidelity nonlinear simulators of two typical General Aviation aircraft. The results show that the autopilot was able to adapt appropriately to the different aircraft and was able to perform three-dimensional navigation and an ILS approach, without any modification to the controller. The autopilot was tested in moderate atmospheric turbulence, using consumer-grade sensors and actuators currently available in General Aviation aircraft. The generic adaptive autopilot was shown to be robust to atmospheric turbulence and sensor and actuator random noise. In both aircraft simulators, the autopilot adapted successfully to changes in airspeed, altitude, and configuration. This investigation proves the feasibility of a generic autopilot using direct adaptive controller. The autopilot does not need a priori information of the specific aircraft’s dynamics to maintain its safety and stability arguments. Real-time parameter estimation of the aircraft dynamics are not needed. Recommendations for future work are provided

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

Distributed Control for Collective Behaviour in Micro-unmanned Aerial Vehicles

Full version unavailable due to 3rd party copyright restrictions.The work presented herein focuses on the design of distributed autonomous controllers for collective behaviour of Micro-unmanned Aerial Vehicles (MAVs). Two alternative approaches to this topic are introduced: one based upon the Evolutionary Robotics (ER) paradigm, the other one upon flocking principles. Three computer simulators have been developed in order to carry out the required experiments, all of them having their focus on the modelling of fixed-wing aircraft flight dynamics. The employment of fixed-wing aircraft rather than the omni-directional robots typically employed in collective robotics significantly increases the complexity of the challenges that an autonomous controller has to face. This is mostly due to the strict motion constraints associated with fixed-wing platforms, that require a high degree of accuracy by the controller. Concerning the ER approach, the experimental setups elaborated have resulted in controllers that have been evolved in simulation with the following capabilities: (1) navigation across unknown environments, (2) obstacle avoidance, (3) tracking of a moving target, and (4) execution of cooperative and coordinated behaviours based on implicit communication strategies. The design methodology based upon flocking principles has involved tests on computer simulations and subsequent experimentation on real-world robotic platforms. A customised implementation of Reynolds’ flocking algorithm has been developed and successfully validated through flight tests performed with the swinglet MAV. It has been notably demonstrated how the Evolutionary Robotics approach could be successfully extended to the domain of fixed-wing aerial robotics, which has never received a great deal of attention in the past. The investigations performed have also shown that complex and real physics-based computer simulators are not a compulsory requirement when approaching the domain of aerial robotics, as long as proper autopilot systems (taking care of the ”reality gap” issue) are used on the real robots.EOARD (European Office of Aerospace Research & Development), euCognitio

Intelligent Control Strategies for an Autonomous Underwater Vehicle

The dynamic characteristics of autonomous underwater vehicles (AUVs) present a control problem that classical methods cannot often accommodate easily. Fundamentally, AUV dynamics are highly non-linear, and the relative similarity between the linear and angular velocities about each degree of freedom means that control schemes employed within other flight vehicles are not always applicable. In such instances, intelligent control strategies offer a more sophisticated approach to the design of the control algorithm. Neurofuzzy control is one such technique, which fuses the beneficial properties of neural networks and fuzzy logic in a hybrid control architecture. Such an approach is highly suited to development of an autopilot for an AUV. Specifically, the adaptive network-based fuzzy inference system (ANFIS) is discussed in Chapter 4 as an effective new approach for neurally tuning course-changing fuzzy autopilots. However, the limitation of this technique is that it cannot be used for developing multivariable fuzzy structures. Consequently, the co-active ANFIS (CANFIS) architecture is developed and employed as a novel multi variable AUV autopilot within Chapter 5, whereby simultaneous control of the AUV yaw and roll channels is achieved. Moreover, this structure is flexible in that it is extended in Chapter 6 to perform on-line control of the AUV leading to a novel autopilot design that can accommodate changing vehicle pay loads and environmental disturbances. Whilst the typical ANFIS and CANFIS structures prove effective for AUV control system design, the well known properties of radial basis function networks (RBFN) offer a more flexible controller architecture. Chapter 7 presents a new approach to fuzzy modelling and employs both ANFIS and CANFIS structures with non-linear consequent functions of composite Gaussian form. This merger of CANFIS and a RBFN lends itself naturally to tuning with an extended form of the hybrid learning rule, and provides a very effective approach to intelligent controller development.The Sea Systems and Platform Integration Sector, Defence Evaluation and Research Agency, Winfrit

Parameter Estimation Analysis for Hybrid Adaptive Fault Tolerant Control

Research efforts have increased in recent years toward the development of intelligent fault tolerant control laws, which are capable of helping the pilot to safely maintain aircraft control at post failure conditions. Researchers at West Virginia University (WVU) have been actively involved in the development of fault tolerant adaptive control laws in all three major categories: direct, indirect, and hybrid. The first implemented design to provide adaptation was a direct adaptive controller, which used artificial neural networks to generate augmentation commands in order to reduce the modeling error. Indirect adaptive laws were implemented in another controller, which utilized online PID to estimate and update the controller parameter. Finally, a new controller design was introduced, which integrated both direct and indirect control laws. This controller is known as hybrid adaptive controller.;This last control design outperformed the two earlier designs in terms of less NNs effort and better tracking quality. The performance of online PID has an important role in the quality of the hybrid controller; therefore, the quality of the estimation will be of a great importance. Unfortunately, PID is not perfect and the online estimation process has some inherited issues; the online PID estimates are primarily affected by delays and biases. In order to ensure updating reliable estimates to the controller, the estimator consumes some time to converge. Moreover, the estimator will often converge to a biased value. This thesis conducts a sensitivity analysis for the estimation issues, delay and bias, and their effect on the tracking quality. In addition, the performance of the hybrid controller as compared to direct adaptive controller is explored.;In order to serve this purpose, a simulation environment in MATLAB/SIMULINK has been created. The simulation environment is customized to provide the user with the flexibility to add different combinations of biases and delays to the explored derivatives. Biases were considered in the range -500% to 500% and delays in the range 0.5 to 40 seconds. The stability and control derivatives considered in this research effort are a combination of decoupled derivatives in the three channels, longitudinal, lateral, and directional. Numerous simulation scenarios and flight conditions are considered to provide more credibility to the obtained results. In addition, a statistical analysis has been conducted to assess the results. The performance of the control laws has been evaluated in terms of the integral of the error in tracking the three desired angular rates, pitch, roll, and yaw. In addition, the effort of the neural networks exerted to compensate for tracking errors is considered in the analysis as well.;The results show that in order to obtain reliable estimates for the investigated derivatives, the estimator needs to generate values with less than five seconds delay. In addition, derivatives estimates are within 50% or -15% off the exact values. Moreover, the importance of updating derivatives depends on the maneuver scenario and the flight condition. The estimation process at quasi-steady state conditions provides reliable estimates as opposed to estimation during fast dynamic changes; also, the estimation process has better performance at large rate of change of derivatives values

Underwater Vehicles

For the latest twenty to thirty years, a significant number of AUVs has been created for the solving of wide spectrum of scientific and applied tasks of ocean development and research. For the short time period the AUVs have shown the efficiency at performance of complex search and inspection works and opened a number of new important applications. Initially the information about AUVs had mainly review-advertising character but now more attention is paid to practical achievements, problems and systems technologies. AUVs are losing their prototype status and have become a fully operational, reliable and effective tool and modern multi-purpose AUVs represent the new class of underwater robotic objects with inherent tasks and practical applications, particular features of technology, systems structure and functional properties

Retrofit systems for reconfiguration in civil aviation

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2000.Includes bibliographical references (p. 215-223).A new concept for retrofitting a reconfiguration module to an existing control law is reported in this thesis. The concept is motivated by the need for low cost, add-on modules that improve air safety in the existing fleet of civil air transport vehicles. A direct adaptive approach that accommodates control surface nonlinearities is adopted, which uses a slowly adapting model of the closed-loop aircraft as the reference model. The motivation, benefits, and components of the architecture are presented. In addition, the issues of control surface magnitude and rate saturation are addressed. A proof of stability is outlined for input-error adaptation when position and rate saturation are present. The reconfiguration architecture is demonstrated using an F/A-18 and a generic transport nonlinear simulator. General issues associated with commercial transport reconfiguration are highlighted. In both the longitudinal and directional axes, the control surfaces are not well balanced from a reconfiguration viewpoint. As a result, a novel reconfiguration control allocation scheme was devised that blends in all the control effectors in a given axis to perform the reconfiguration task. The simulation results revealed that the reconfiguration architecture does provide reconfiguration functionality for a wide variety of control surface failures. The reconfiguration potential is illustrated through comparisons of post-failure performance with and without reconfiguration via non-linear simulations. Additionally, comparisons between post-failure performance and nominal performance are made through non-linear simulations, closed-loop frequency responses, and aircraft handling qualities. For all of the failure scenarios illustrated, the simulation results showed that the aircraft without reconfiguration departs; with reconfiguration, nominal performance is achieve provided that adequate control authority exists post-failure.by Jerry M. Wohletz.Ph.D

Algorithm/Architecture Co-Design for Low-Power Neuromorphic Computing

The development of computing systems based on the conventional von Neumann architecture has slowed down in the past decade as complementary metal-oxide-semiconductor (CMOS) technology scaling becomes more and more difficult. To satisfy the ever-increasing demands in computing power, neuromorphic computing has emerged as an attractive alternative. This dissertation focuses on developing learning algorithm, hardware architecture, circuit components, and design methodologies for low-power neuromorphic computing that can be employed in various energy-constrained applications. A top-down approach is adopted in this research. Starting from the algorithm-architecture co-design, a hardware-friendly learning algorithm is developed for spiking neural networks (SNNs). The possibility of estimating gradients from spike timings is explored. The learning algorithm is developed for the ease of hardware implementation, as well as the compatibility with many well-established learning techniques developed for classic artificial neural networks (ANNs). An SNN hardware equipped with the proposed on-chip learning algorithm is implemented in CMOS technology. In this design, two unique features of SNNs, the event-driven computation and the inferring with a progressive precision, are leveraged to reduce the energy consumption. In addition to low-power SNN hardware, accelerators for ANNs are also presented to accelerate the adaptive dynamic programing algorithm. An efficient and flexible single-instruction-multiple-data architecture is proposed to exploit the inherent data-level parallelism in the inference and learning of ANNs. In addition, the accelerator is augmented with a virtual update technique, which helps improve the throughput and energy efficiency remarkably. Lastly, two techniques in the architecture-circuit level are introduced to mitigate the degraded reliability of the memory system in a neuromorphic hardware owing to the aggressively-scaled supply voltage and integration density. The first method uses on-chip feedback to compensate for the process variation and the second technique improves the throughput and energy efficiency of a conventional error-correction method.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144149/1/zhengn_1.pd