696 research outputs found

    Optimized state feedback regulation of 3DOF helicopter system via extremum seeking

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    In this paper, an optimized state feedback regulation of a 3 degree of freedom (DOF) helicopter is designed via extremum seeking (ES) technique. Multi-parameter ES is applied to optimize the tracking performance via tuning State Vector Feedback with Integration of the Control Error (SVFBICE). Discrete multivariable version of ES is developed to minimize a cost function that measures the performance of the controller. The cost function is a function of the error between the actual and desired axis positions. The controller parameters are updated online as the optimization takes place. This method significantly decreases the time in obtaining optimal controller parameters. Simulations were conducted for the online optimization under both fixed and varying operating conditions. The results demonstrate the usefulness of using ES for preserving the maximum attainable performance

    The Dynamic Brain in Action: Cortical Oscillations and Coordination Dynamics

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    Cortical oscillations are electrical activities with rhythmic and/or repetitive nature generated spontaneously and in response to stimuli. Study of cortical oscillations has become an area of converging interests since the last two decades and has deepened our understanding of its physiological basis across different behavioral states. Experimental and modeling work has taught us that there is a wide diversity of cellular and circuit mechanisms underlying the generation of cortical rhythms. A wildly diverse set of functions has pertained to synchronous oscillations but their significance in cognition should be better appraised in the more general framework of correlation between spike times of neurons. Oscillations are the core mechanism in adjusting neuronal interactions and shaping temporal coordination of neural activity. In the first part of this thesis, we review essential feature of cortical oscillations in membrane potentials and local field potentials recorded from turtle ex vivo preparation. Then we develop a simple computational model that reproduces the observed features. This modeling investigation suggests a plausible underlying mechanism for rhythmogenesis through cellular and circuit properties. The second part of the thesis is about temporal coordination dynamics quantified by signal and noise correlations. Here, again, we present a computational model to show how temporal coordination and synchronous oscillations can be sewn together. More importantly, what biophysical ingrediants are necessary for a network to reproduce the observed coordination dynamics

    The Fifth NASA Symposium on VLSI Design

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    The fifth annual NASA Symposium on VLSI Design had 13 sessions including Radiation Effects, Architectures, Mixed Signal, Design Techniques, Fault Testing, Synthesis, Signal Processing, and other Featured Presentations. The symposium provides insights into developments in VLSI and digital systems which can be used to increase data systems performance. The presentations share insights into next generation advances that will serve as a basis for future VLSI design

    Distributed Control and State Estimation of DC Microgrids Based on Constrained Communication Networks.

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    PhD ThesesThe intermittent nature of renewable energy sources (RES) such as wind turbines and photovoltaic panels, requires advanced control systems to provide the balance between energy supply and demand in any power system. For better management of power quality and security issues, energy storage systems (ESSs) are deployed to compensate for the temporary mismatch of supply and demand. Furthermore, in rural areas with no connection to the main grid, ESSs such as batteries are deployed in large quantities as a solution for temporary power stabilization during RES unavailability. However, the control complexity of the power system increases as more ESSs are getting installed due to the need for coordination of the power transfer among them. This thesis undertakes a thorough analysis of distributed control and state estimation designs for direct current (DC) microgrids with ESSs based on constrained communication networks. The developed distributed control and estimation strategies are designed for operation over constrained communication networks. They don't require a central coordinator for synchronization of the control tasks between the ESSs. This forms a multi-agent environment where the controllers cooperatively achieve the DC microgrid objectives, i.e. voltage stabilization, proportional power-sharing, and balancing of ESSs' energy level. To overcome the communication network constraints, event-based controllers and estimators are designed, which e ectively reduce the network tra c and as a result, provide higher throughput with reduced delays for the real-time control loops of the DC microgrids. The controllers are designed to be distributed, leading to use cases such as autonomous islanded microgrids, smart villages, and plug-and-play mobile microgrids. The feasibility and performance of the proposed control and estimation strategies are con rmed in several experimental test benches by showing the higher reliability and robustness in the delivered power quality. The results have shown considerable reduction in the network tra c, meanwhile the control system provided high performance in terms of stability, robustness, power quality and endurabilit

    Automated Debugging Methodology for FPGA-based Systems

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    Electronic devices make up a vital part of our lives. These are seen from mobiles, laptops, computers, home automation, etc. to name a few. The modern designs constitute billions of transistors. However, with this evolution, ensuring that the devices fulfill the designer’s expectation under variable conditions has also become a great challenge. This requires a lot of design time and effort. Whenever an error is encountered, the process is re-started. Hence, it is desired to minimize the number of spins required to achieve an error-free product, as each spin results in loss of time and effort. Software-based simulation systems present the main technique to ensure the verification of the design before fabrication. However, few design errors (bugs) are likely to escape the simulation process. Such bugs subsequently appear during the post-silicon phase. Finding such bugs is time-consuming due to inherent invisibility of the hardware. Instead of software simulation of the design in the pre-silicon phase, post-silicon techniques permit the designers to verify the functionality through the physical implementations of the design. The main benefit of the methodology is that the implemented design in the post-silicon phase runs many order-of-magnitude faster than its counterpart in pre-silicon. This allows the designers to validate their design more exhaustively. This thesis presents five main contributions to enable a fast and automated debugging solution for reconfigurable hardware. During the research work, we used an obstacle avoidance system for robotic vehicles as a use case to illustrate how to apply the proposed debugging solution in practical environments. The first contribution presents a debugging system capable of providing a lossless trace of debugging data which permits a cycle-accurate replay. This methodology ensures capturing permanent as well as intermittent errors in the implemented design. The contribution also describes a solution to enhance hardware observability. It is proposed to utilize processor-configurable concentration networks, employ debug data compression to transmit the data more efficiently, and partially reconfiguring the debugging system at run-time to save the time required for design re-compilation as well as preserve the timing closure. The second contribution presents a solution for communication-centric designs. Furthermore, solutions for designs with multi-clock domains are also discussed. The third contribution presents a priority-based signal selection methodology to identify the signals which can be more helpful during the debugging process. A connectivity generation tool is also presented which can map the identified signals to the debugging system. The fourth contribution presents an automated error detection solution which can help in capturing the permanent as well as intermittent errors without continuous monitoring of debugging data. The proposed solution works for designs even in the absence of golden reference. The fifth contribution proposes to use artificial intelligence for post-silicon debugging. We presented a novel idea of using a recurrent neural network for debugging when a golden reference is present for training the network. Furthermore, the idea was also extended to designs where golden reference is not present

    Estimation and stability of nonlinear control systems under intermittent information with applications to multi-agent robotics

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    This dissertation investigates the role of intermittent information in estimation and control problems and applies the obtained results to multi-agent tasks in robotics. First, we develop a stochastic hybrid model of mobile networks able to capture a large variety of heterogeneous multi-agent problems and phenomena. This model is applied to a case study where a heterogeneous mobile sensor network cooperatively detects and tracks mobile targets based on intermittent observations. When these observations form a satisfactory target trajectory, a mobile sensor is switched to the pursuit mode and deployed to capture the target. The cost of operating the sensors is determined from the geometric properties of the network, environment and probability of target detection. The above case study is motivated by the Marco Polo game played by children in swimming pools. Second, we develop adaptive sampling of targets positions in order to minimize energy consumption, while satisfying performance guarantees such as increased probability of detection over time, and no-escape conditions. A parsimonious predictor-corrector tracking filter, that uses geometrical properties of targets\u27 tracks to estimate their positions using imperfect and intermittent measurements, is presented. It is shown that this filter requires substantially less information and processing power than the Unscented Kalman Filter and Sampling Importance Resampling Particle Filter, while providing comparable estimation performance in the presence of intermittent information. Third, we investigate stability of nonlinear control systems under intermittent information. We replace the traditional periodic paradigm, where the up-to-date information is transmitted and control laws are executed in a periodic fashion, with the event-triggered paradigm. Building on the small gain theorem, we develop input-output triggered control algorithms yielding stable closed-loop systems. In other words, based on the currently available (but outdated) measurements of the outputs and external inputs of a plant, a mechanism triggering when to obtain new measurements and update the control inputs is provided. Depending on the noise environment, the developed algorithm yields stable, asymptotically stable, and Lp-stable (with bias) closed-loop systems. Control loops are modeled as interconnections of hybrid systems for which novel results on Lp-stability are presented. Prediction of a triggering event is achieved by employing Lp-gains over a finite horizon in the small gain theorem. By resorting to convex programming, a method to compute Lp-gains over a finite horizon is devised. Next, we investigate optimal intermittent feedback for nonlinear control systems. Using the currently available measurements from a plant, we develop a methodology that outputs when to update the control law with new measurements such that a given cost function is minimized. Our cost function captures trade-offs between the performance and energy consumption of the control system. The optimization problem is formulated as a Dynamic Programming problem, and Approximate Dynamic Programming is employed to solve it. Instead of advocating a particular approximation architecture for Approximate Dynamic Programming, we formulate properties that successful approximation architectures satisfy. In addition, we consider problems with partially observable states, and propose Particle Filtering to deal with partially observable states and intermittent feedback. Finally, we investigate a decentralized output synchronization problem of heterogeneous linear systems. We develop a self-triggered output broadcasting policy for the interconnected systems. Broadcasting time instants adapt to the current communication topology. For a fixed topology, our broadcasting policy yields global exponential output synchronization, and Lp-stable output synchronization in the presence of disturbances. Employing a converse Lyapunov theorem for impulsive systems, we provide an average dwell time condition that yields disturbance-to-state stable output synchronization in case of switching topology. Our approach is applicable to directed and unbalanced communication topologies.\u2
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