Adaptive Flight-Control Design Using Neural-Network-Aided Optimal Nonlinear Dynamic Inversion

A neural-network-aided nonlinear dynamic inversion-based hybrid technique of model reference adaptive control flight-control system design is presented in this paper. Here, the gains of the nonlinear dynamic inversion-based flight-control system are dynamically selected in such a manner that the resulting controller mimics a single network, adaptive control, optimal nonlinear controller for state regulation. Traditional model reference adaptive control methods use a linearized reference model, and the presented control design method employs a nonlinear reference model to compute the nonlinear dynamic inversion gains. This innovation of designing the gain elements after synthesizing the single network adaptive controller maintains the advantages that an optimal controller offers, yet it retains a simple closed-form control expression in state feedback form, which can easily be modified for tracking problems without demanding any a priori knowledge of the reference signals. The strength of the technique is demonstrated by considering the longitudinal motion of a nonlinear aircraft system. An extended single network adaptive control/nonlinear dynamic inversion adaptive control design architecture is also presented, which adapts online to three failure conditions, namely, a thrust failure, an elevator failure, and an inaccuracy in the estimation of C-M alpha. Simulation results demonstrate that the presented adaptive flight controller generates a near-optimal response when compared to a traditional nonlinear dynamic inversion controller.

Design and morphology control of a thiophene derivative through electrospraying using various solvents

In the present work, electrospraying of an organic molecule is carried out using various solvents, obtaining fibril structures along with a range of distinct morphologies. Solvent characteristics play a major role in determining the morphology of the organic material. A thiophene derivative (7,9-di(thiophen-2-yl)-8H-cyclopentaa]acenaphthylen-8-one) (DTCPA) of donor-acceptor-donor (DAD) architecture is used to study this solvent effect. Seven solvents with decreasing vapour pressure are selected for experiments. Electrospraying is conducted at a solution concentration of 1.5 wt% and a constant applied voltage of 15 kV. Gradual transformation in morphology of the electrospun product from spiked-spheres to only spikes is observed. A mechanism describing this transformation is proposed based on electron micrograph analysis and XRD analysis. These data indicate that the morphological change is due to the synergistic effect of both vapour pressure and dielectric constant of the solvents. Through a reasonable control of the crystallite size and morphology along with the proposal of the transformation mechanism, this study elucidates electrospraying as a prospective method for designing architectures in organic electronics.

Component-based approach to the holonic control of a robot assembly cell

The aim of this paper is to describe the implementation of a new approach for the introduction of so called 'holonic manufacturing' principles into existing production control systems. Such an approach is intended to improve the reconfigurability of the control system to cope with the increasing requirements of production change. A conceptual architecture is described and implemented in a robot assembly cell to demonstrate that this approach can lead to a manufacturing control system which can adapt relatively simply to long-term change. A design methodology and migration strategy for achieving these solutions using conventional hardware is proposed to develop execution level of manufacturing control systems.

Limits and tradeoffs in the control of autocatalytic systems

Despite the complexity of biological networks, we find that certain common architectures govern network structures. These architectures impose fundamental constraints on system performance and create tradeoffs that the system must balance in the face of uncertainty in the environment. This means that while a system may be optimized for a specific function through evolution, the optimal achievable state must follow these constraints. One such constraining architecture is autocatalysis, as seen in many biological networks including glycolysis and ribosomal protein synthesis. Using a minimal model, we show that ATP autocatalysis in glycolysis imposes stability and performance constraints and that the experimentally well-studied glycolytic oscillations are in fact a consequence of a tradeoff between error minimization and stability. We also show that additional complexity in the network results in increased robustness. Ribosome synthesis is also autocatalytic where ribosomes must be used to make more ribosomal proteins. When ribosomes have higher protein content, the autocatalysis is increased. We show that this autocatalysis destabilizes the system, slows down response, and also constrains the system’s performance. On a larger scale, transcriptional regulation of whole organisms also follows architectural constraints and this can be seen in the differences between bacterial and yeast transcription networks. We show that the degree distributions of bacterial transcription network follow a power law distribution while the yeast network follows an exponential distribution. We then explored the evolutionary models that have previously been proposed and show that neither the preferential linking model nor the duplication-divergence model of network evolution generates the power-law, hierarchical structure found in bacteria. However, in real biological systems, the generation of new nodes occurs through both duplication and horizontal gene transfers, and we show that a biologically reasonable combination of the two mechanisms generates the desired network.

Neural network design for switching network control

A neural network is a highly interconnected set of simple processors. The many connections allow information to travel rapidly through the network, and due to their simplicity, many processors in one network are feasible. Together these properties imply that we can build efficient massively parallel machines using neural networks. The primary problem is how do we specify the interconnections in a neural network. The various approaches developed so far such as outer product, learning algorithm, or energy function suffer from the following deficiencies: long training/ specification times; not guaranteed to work on all inputs; requires full connectivity.

Alternatively we discuss methods of using the topology and constraints of the problems themselves to design the topology and connections of the neural solution. We define several useful circuits-generalizations of the Winner-Take-All circuitthat allows us to incorporate constraints using feedback in a controlled manner. These circuits are proven to be stable, and to only converge on valid states. We use the Hopfield electronic model since this is close to an actual implementation. We also discuss methods for incorporating these circuits into larger systems, neural and nonneural. By exploiting regularities in our definition, we can construct efficient networks. To demonstrate the methods, we look to three problems from communications. We first discuss two applications to problems from circuit switching; finding routes in large multistage switches, and the call rearrangement problem. These show both, how we can use many neurons to build massively parallel machines, and how the Winner-Take-All circuits can simplify our designs.

Next we develop a solution to the contention arbitration problem of high-speed packet switches. We define a useful class of switching networks and then design a neural network to solve the contention arbitration problem for this class. Various aspects of the neural network/switch system are analyzed to measure the queueing performance of this method. Using the basic design, a feasible architecture for a large (1024-input) ATM packet switch is presented. Using the massive parallelism of neural networks, we can consider algorithms that were previously computationally unattainable. These now viable algorithms lead us to new perspectives on switch design.

Optimization and Control of Power Flow in Distribution Networks

Climate change is arguably the most critical issue facing our generation and the next. As we move towards a sustainable future, the grid is rapidly evolving with the integration of more and more renewable energy resources and the emergence of electric vehicles. In particular, large scale adoption of residential and commercial solar photovoltaics (PV) plants is completely changing the traditional slowly-varying unidirectional power flow nature of distribution systems. High share of intermittent renewables pose several technical challenges, including voltage and frequency control. But along with these challenges, renewable generators also bring with them millions of new DC-AC inverter controllers each year. These fast power electronic devices can provide an unprecedented opportunity to increase energy efficiency and improve power quality, if combined with well-designed inverter control algorithms. The main goal of this dissertation is to develop scalable power flow optimization and control methods that achieve system-wide efficiency, reliability, and robustness for power distribution networks of future with high penetration of distributed inverter-based renewable generators.

Proposed solutions to power flow control problems in the literature range from fully centralized to fully local ones. In this thesis, we will focus on the two ends of this spectrum. In the first half of this thesis (chapters 2 and 3), we seek optimal solutions to voltage control problems provided a centralized architecture with complete information. These solutions are particularly important for better understanding the overall system behavior and can serve as a benchmark to compare the performance of other control methods against. To this end, we first propose a branch flow model (BFM) for the analysis and optimization of radial and meshed networks. This model leads to a new approach to solve optimal power flow (OPF) problems using a two step relaxation procedure, which has proven to be both reliable and computationally efficient in dealing with the non-convexity of power flow equations in radial and weakly-meshed distribution networks. We will then apply the results to fast time- scale inverter var control problem and evaluate the performance on real-world circuits in Southern California Edison’s service territory.

The second half (chapters 4 and 5), however, is dedicated to study local control approaches, as they are the only options available for immediate implementation on today’s distribution networks that lack sufficient monitoring and communication infrastructure. In particular, we will follow a reverse and forward engineering approach to study the recently proposed piecewise linear volt/var control curves. It is the aim of this dissertation to tackle some key problems in these two areas and contribute by providing rigorous theoretical basis for future work.

Sistema de control multipropósito programable por pulsación de teclas con álgebra RPN.

[ES]Para la elaboración del presente proyecto, primeramente se explicara los requerimientos técnicos y el origen del microprocesador a utilizar para poder situar y centrar el tema del trabajo. Una vez acotado y delimitado el tema objeto de estudio, se planteara una arquitectura de software sobre la posibilidad de generar unas “pseudolibrerias” de mayor nivel de programación que el ensamblador. Posteriormente, se verificara la posible viabilidad o no de tal planteamiento, exponiendo sus resultados y las consideraciones oportunas a las que nos ha llevado su estudio. Para ello se analizara en una primera instancia el microprocesador a utilizar, que será el PIC16F887, centrándonos en el debido al amplio manejo y conocimiento que poseemos sobre este microprocesador. El objeto de este escrito será presentar una oferta económica relativa al desarrollo e instalación de dichos microprocesadores para la mejora en el ámbito industrial. Finalmente, realizaremos un estudio sobre la implementación de este tipo de arquitectura software en diferentes microprocesadores de mayores prestaciones, estudiando si la infraestructura será eficiente, funcional y económica.

Gated three-terminal device architecture to eliminate persistent photoconductivity in oxide semiconductor photosensor arrays

The composition of amorphous oxide semiconductors, which are well known for their optical transparency, can be tailored to enhance their absorption and induce photoconductivity for irradiation with green, and shorter wavelength light. In principle, amorphous oxide semiconductor-based thin-film photoconductors could hence be applied as photosensors. However, their photoconductivity persists for hours after illumination has been removed, which severely degrades the response time and the frame rate of oxide-based sensor arrays. We have solved the problem of persistent photoconductivity (PPC) by developing a gated amorphous oxide semiconductor photo thin-film transistor (photo-TFT) that can provide direct control over the position of the Fermi level in the active layer. Applying a short-duration (10 ns) voltage pulse to these devices induces electron accumulation and accelerates their recombination with ionized oxygen vacancy sites, which are thought to cause PPC. We have integrated these photo-TFTs in a transparent active-matrix photosensor array that can be operated at high frame rates and that has potential applications in contact-free interactive displays. © 2012 Macmillan Publishers Limited. All rights reserved.

feedback fuzzy-dvs scheduling of control tasks

To consider the energy-aware scheduling problem in computer-controlled systems is necessary to improve the control performance, to use the limited computing resource sufficiently, and to reduce the energy consumption to extend the lifetime of the whole system. In this paper, the scheduling problem of multiple control tasks is discussed based on an adjustable voltage processor. A feedback fuzzy-DVS (dynamic voltage scaling) scheduling architecture is presented by applying technologies of the feedback control and the fuzzy DVS. The simulation results show that, by using the actual utilization as the feedback information to adjust the supply voltage of processor dynamically, the high CPU utilization can be implemented under the precondition of guaranteeing the control performance, whilst the low energy consumption can be achieved as well. The proposed method can be applied to the design in computer-controlled systems based on an adjustable voltage processor.

Gold nanowire assembling architecture for H2O2 electrochemical sensor

Morphological control of nanomaterials is of great interest due to their size and shape-dependent chemical and physical properties and very important applications in many fields such as biomedicine, sensors, electronics and others. In this paper, we reported a simple strategy for synthesizing gold nanowire assembling architecture at room temperature. It is found that two important factors, the proper volume ratio of ethanol to water and poly(vinyl pyrrolidone) (PVP), will play important roles in synthesizing flower-like short gold nanowire assembling spheres. Furthermore, the obtained flower-like gold assembling spheres with high surface-to-volume ratio have been employed as enhancing materials for electrochemical sensing H2O2. The present electrochemical sensing platform exhibited good electrocatalytic activity towards the reduction of H2O2. The detection limit for H2O2 was found to be 1.2 mu M, which was lower than certain enzyme-based biosensors.

Robust Agent Control of an Autonomous Robot with Many Sensors and Actuators

This thesis presents methods for implementing robust hexpod locomotion on an autonomous robot with many sensors and actuators. The controller is based on the Subsumption Architecture and is fully distributed over approximately 1500 simple, concurrent processes. The robot, Hannibal, weighs approximately 6 pounds and is equipped with over 100 physical sensors, 19 degrees of freedom, and 8 on board computers. We investigate the following topics in depth: distributed control of a complex robot, insect-inspired locomotion control for gait generation and rough terrain mobility, and fault tolerance. The controller was implemented, debugged, and tested on Hannibal. Through a series of experiments, we examined Hannibal's gait generation, rough terrain locomotion, and fault tolerance performance. These results demonstrate that Hannibal exhibits robust, flexible, real-time locomotion over a variety of terrain and tolerates a multitude of hardware failures.

Dexterous Robotic Hands: Kinematics and Control

This report presents issues relating to the kinematics and control of dexterous robotic hands using the Utah-MIT hand as an illustrative example. The emphasis throughout is on the actual implementation and testing of the theoretical concepts presented. The kinematics of such hands is interesting and complicated owing to the large number of degrees of freedom involved. The implementation of position and force control algorithms on such tendon driven hands has previously suffered from inefficient formulations and a lack of sophisticated computer hardware. Both these problems are addressed in this report. A multiprocessor architecture has been built with high performance microcomputers on which real-time algorithms can be efficiently implemented. A large software library has also been built to facilitate flexible software development on this architecture. The position and force control algorithms described herein have been implemented and tested on this hardware.

Learning and Control in Assistive Robotics for the Elderly

Q. Meng and M. H. Lee, Learning and Control in Assistive Robotics for the Elderly, IEEE Conference on Robotics, Automation and Mechatronics (RAM), Singapore, 2004.

Efficient End-Host Architecture for High Performance Communication Using User-level Sandboxing

Current low-level networking abstractions on modern operating systems are commonly implemented in the kernel to provide sufficient performance for general purpose applications. However, it is desirable for high performance applications to have more control over the networking subsystem to support optimizations for their specific needs. One approach is to allow networking services to be implemented at user-level. Unfortunately, this typically incurs costs due to scheduling overheads and unnecessary data copying via the kernel. In this paper, we describe a method to implement efficient application-specific network service extensions at user-level, that removes the cost of scheduling and provides protected access to lower-level system abstractions. We present a networking implementation that, with minor modifications to the Linux kernel, passes data between "sandboxed" extensions and the Ethernet device without copying or processing in the kernel. Using this mechanism, we put a customizable networking stack into a user-level sandbox and show how it can be used to efficiently process and forward data via proxies, or intermediate hosts, in the communication path of high performance data streams. Unlike other user-level networking implementations, our method makes no special hardware requirements to avoid unnecessary data copies. Results show that we achieve a substantial increase in throughput over comparable user-space methods using our networking stack implementation.

Concurrency Admission Control Management in ACCORD

We propose and evaluate admission control mechanisms for ACCORD, an Admission Control and Capacity Overload management Real-time Database framework-an architecture and a transaction model-for hard deadline RTDB systems. The system architecture consists of admission control and scheduling components which provide early notification of failure to submitted transactions that are deemed not valuable or incapable of completing on time. In particular, our Concurrency Admission Control Manager (CACM) ensures that transactions which are admitted do not overburden the system by requiring a level of concurrency that is not sustainable. The transaction model consists of two components: a primary task and a compensating task. The execution requirements of the primary task are not known a priori, whereas those of the compensating task are known a priori. Upon the submission of a transaction, the Admission Control Mechanisms are employed to decide whether to admit or reject that transaction. Once admitted, a transaction is guaranteed to finish executing before its deadline. A transaction is considered to have finished executing if exactly one of two things occur: Either its primary task is completed (successful commitment), or its compensating task is completed (safe termination). Committed transactions bring a profit to the system, whereas a terminated transaction brings no profit. The goal of the admission control and scheduling protocols (e.g., concurrency control, I/O scheduling, memory management) employed in the system is to maximize system profit. In that respect, we describe a number of concurrency admission control strategies and contrast (through simulations) their relative performance.