Controls and Control Theory Commons^™

Smart City Management Using Machine Learning Techniques, Mostafa Zaman

Theses and Dissertations

In response to the growing urban population, "smart cities" are designed to improve people's quality of life by implementing cutting-edge technologies. The concept of a "smart city" refers to an effort to enhance a city's residents' economic and environmental well-being via implementing a centralized management system. With the use of sensors and actuators, smart cities can collect massive amounts of data, which can improve people's quality of life and design cities' services. Although smart cities contain vast amounts of data, only a percentage is used due to the noise and variety of the data sources. Information and communication technology (ICT) …

Automated Flight Controller Adaptive Compensation For Actuator Failures In Transport Type Aircraft, Daniel James Fresella

Graduate Theses and Dissertations

Aircraft operate over a wide range of conditions including atmospheric, weight, and center ofgravity changes. This presents a substantial challenge to automatic control system designers. When these operating conditions are merged with a partial or full control surface failure, automatic flight control is near impossible with conventional controllers. Additionally, when pilots experience control failure emergencies during flight, workload and fatigue increase drastically. The continued research of automatic flight control systems that can seamlessly adapt to unmodelled failures will enable a new generation of robust aircraft control. In this paper a 9th order 6 degree of freedom aircraft model is used …

Implementation Of Fuzzy Logic Control Into An Equivalent Minimization Strategy For Adaptive Energy Management Of A Parallel Hybrid Electric Vehicle, Jared Alexander Diethorn

Graduate Theses, Dissertations, and Problem Reports

As government agencies continue to tighten emissions regulations due to the continued increase in greenhouse gas production, automotive industries are seeking to produce increasingly efficient vehicle technology. Electric vehicles have been introduced by the industry, showing promising signs of reducing emissions production in the automotive sector. However, many consumers may be hesitant to purchase fully electric vehicles due to several uncertainty variables including available charging stations. Hybrid electric vehicles (HEVs) have been introduced to reduce problems while improving fuel economy. HEVs have led to the demand of creating more advanced controls software to consider multiple components for propulsive power in …

Easily Verifiable Controller Design With Application To Automotive Powertrains, Mohammad Reza Amini

Dissertations, Master's Theses and Master's Reports

Bridging the gap between designed and implemented model-based controllers is a major challenge in the design cycle of industrial controllers. This gap is mainly created due to (i) digital implementation of controller software that introduces sampling and quantization imprecisions via analog-to-digital conversion (ADC), and (ii) uncertainties in the modeled plant’s dynamics, which directly propagate through the controller structure. The failure to identify and handle these implementation and model uncertainties results in undesirable controller performance and costly iterative loops for completing the controller verification and validation (V&V) process.

This PhD dissertation develops a novel theoretical framework to design controllers that are …

Fast And Low-Frequency Adaptation In Neural Network Control, Yongping Pan, Qin Gao, Haoyong Yu

Yongping Pan

In adaptive neural network (NN) control, fast adaptation through high-gain learning rates can cause high-frequency oscillations in control response resulting in system instability. This paper presents a simple adaptive NN with proportional-derivative (PD) control strategy to achieve fast and low-frequency adaptation for a class of uncertain nonlinear systems. Variable-gain PD control without the knowledge of plant bounds is proposed to semiglobally stabilize the plant so that NN approximation is applicable. A low-pass filter-based modification is applied to the adaptive law to filter out high-frequency content so that tracking performance can be safely improved by the increase of learning rates. The …

Output Feedback Adaptive Neural Control Without Seeking Spr Condition, Yongping Pan, Meng Joo Er, Rongjun Chen, Haoyong Yu

Yongping Pan

For output-feedback adaptive control of affine nonlinear systems based on feedback linearization and function approximation, the observation error dynamics usually should be augmented by a low-pass filter to satisfy a strictly positive real (SPR) condition so that output feedback can be realized. Yet, this manipulation results in filtering basis functions of approximators, which makes the order of the controller dynamics very large. This paper presents a novel output-feedback adaptive neural control (ANC) scheme to avoid seeking the SPR condition. A saturated output-feedback control law is introduced based on a state-feedback indirect ANC structure. An adaptive neural network (NN) observer is …