8,954 research outputs found
Robust nonlinear control of vectored thrust aircraft
An interdisciplinary program in robust control for nonlinear systems with applications to a variety of engineering problems is outlined. Major emphasis will be placed on flight control, with both experimental and analytical studies. This program builds on recent new results in control theory for stability, stabilization, robust stability, robust performance, synthesis, and model reduction in a unified framework using Linear Fractional Transformations (LFT's), Linear Matrix Inequalities (LMI's), and the structured singular value micron. Most of these new advances have been accomplished by the Caltech controls group independently or in collaboration with researchers in other institutions. These recent results offer a new and remarkably unified framework for all aspects of robust control, but what is particularly important for this program is that they also have important implications for system identification and control of nonlinear systems. This combines well with Caltech's expertise in nonlinear control theory, both in geometric methods and methods for systems with constraints and saturations
Nonlinear and adaptive control
The primary thrust of the research was to conduct fundamental research in the theories and methodologies for designing complex high-performance multivariable feedback control systems; and to conduct feasibiltiy studies in application areas of interest to NASA sponsors that point out advantages and shortcomings of available control system design methodologies
PAC: A Novel Self-Adaptive Neuro-Fuzzy Controller for Micro Aerial Vehicles
There exists an increasing demand for a flexible and computationally
efficient controller for micro aerial vehicles (MAVs) due to a high degree of
environmental perturbations. In this work, an evolving neuro-fuzzy controller,
namely Parsimonious Controller (PAC) is proposed. It features fewer network
parameters than conventional approaches due to the absence of rule premise
parameters. PAC is built upon a recently developed evolving neuro-fuzzy system
known as parsimonious learning machine (PALM) and adopts new rule growing and
pruning modules derived from the approximation of bias and variance. These rule
adaptation methods have no reliance on user-defined thresholds, thereby
increasing the PAC's autonomy for real-time deployment. PAC adapts the
consequent parameters with the sliding mode control (SMC) theory in the
single-pass fashion. The boundedness and convergence of the closed-loop control
system's tracking error and the controller's consequent parameters are
confirmed by utilizing the LaSalle-Yoshizawa theorem. Lastly, the controller's
efficacy is evaluated by observing various trajectory tracking performance from
a bio-inspired flapping-wing micro aerial vehicle (BI-FWMAV) and a rotary wing
micro aerial vehicle called hexacopter. Furthermore, it is compared to three
distinctive controllers. Our PAC outperforms the linear PID controller and
feed-forward neural network (FFNN) based nonlinear adaptive controller.
Compared to its predecessor, G-controller, the tracking accuracy is comparable,
but the PAC incurs significantly fewer parameters to attain similar or better
performance than the G-controller.Comment: This paper has been accepted for publication in Information Science
Journal 201
The economic basis of periodic enzyme dynamics
Periodic enzyme activities can improve the metabolic performance of cells. As
an adaptation to periodic environments or by driving metabolic cycles that can
shift fluxes and rearrange metabolic processes in time to increase their
efficiency. To study what benefits can ensue from rhythmic gene expression or
posttranslational modification of enzymes, I propose a theory of optimal enzyme
rhythms in periodic or static environments. The theory is based on kinetic
metabolic models with predefined metabolic objectives, scores the effects of
harmonic enzyme oscillations, and determines amplitudes and phase shifts that
maximise cell fitness. In an expansion around optimal steady states, the
optimal enzyme profiles can be computed by solving a quadratic optimality
problem. The formulae show how enzymes can increase their efficiency by
oscillating in phase with their substrates and how cells can benefit from
adapting to external rhythms and from spontaneous, intrinsic enzyme rhythms.
Both types of behaviour may occur different parameter regions of the same
model. Optimal enzyme profiles are not passively adapted to existing substrate
rhythms, but shape them actively to create opportunities for further fitness
advantage: in doing so, they reflect the dynamic effects that enzymes can exert
in the network. The proposed theory combines the dynamics and economics of
metabolic systems and shows how optimal enzyme profiles are shaped by network
structure, dynamics, external rhythms, and metabolic objectives. It covers
static enzyme adaptation as a special case, reveals the conditions for
beneficial metabolic cycles, and predicts optimally combinations of gene
expression and posttranslational modification for creating enzyme rhythms
Tracking system study
A digital computer program was generated which mathematically describes an optimal estimator-controller technique as applied to the control of antenna tracking systems used by NASA. Simulation studies utilizing this program were conducted using the IBM 360/91 computer. The basic ideas of applying optimal estimator-controller techniques to antenna tracking systems are discussed. A survey of existing tracking methods is given along with shortcomings and inherent errors. It is explained how these errors can be considerably reduced if optimal estimation and control are used. The modified programs generated in this project are described and the simulation results are summarized. The new algorithms for direct synthesis and stabilization of the systems including nonlinearities, are presented
Adaptive Control
Adaptive control has been a remarkable field for industrial and academic research since 1950s. Since more and more adaptive algorithms are applied in various control applications, it is becoming very important for practical implementation. As it can be confirmed from the increasing number of conferences and journals on adaptive control topics, it is certain that the adaptive control is a significant guidance for technology development.The authors the chapters in this book are professionals in their areas and their recent research results are presented in this book which will also provide new ideas for improved performance of various control application problems
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Dynamic optimization of energy systems with thermal energy storage
textThermal energy storage (TES), the storage of heat or cooling, is a cost-effective energy storage technology that can greatly enhance the performance of the energy systems with which it interacts. TES acts as a buffer between transient supply and demand of energy. In solar thermal systems, TES enables the power output of the plant to be effectively regulated, despite fluctuating solar irradiance. In district energy systems, TES can be used to shift loads, allowing the system to avoid or take advantage of peak energy prices. The benefit of TES, however, can be significantly enhanced by dynamically optimizing the complete energy system. The ability of TES to shift loads gives the system newfound degrees of freedom which can be exploited to yield optimal performance. In the hybrid solar thermal/fossil fuel system explored in this work, the use of TES enables the system to extract nearly 50% more solar energy when the system is optimized. This requires relaxing some constraints, such as fixed temperature and power control, and dynamically optimizing the over a one-day time horizon. In a district cooling system, TES can help equipment to run more efficiently, by shifting cooling loads, not only between chillers, but temporally, allowing the system to take advantage of the most efficient times for running this equipment. This work also highlights the use of TES in a district energy system, where heat, cooling and electrical power are generated from central locations. Shifting the cooling load frees up electrical generation capacity, which is used to sell power to the grid at peak prices. The combination of optimization, TES, and participation in the electricity market yields a 16% cost savings. The problems encountered in this work require modeling a diverse range of systems including the TES, the solar power plant, boilers, gas and steam turbines, heat recovery equipment, chillers, and pumps. These problems also require novel solution methods that are efficient and effective at obtaining workable solutions. A simultaneous solution method is used for optimizing the solar power plant, while a static/dynamic decoupling method is used for the district energy system.Chemical Engineerin
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