Matching Items (2)
Description
Sliding-Mode Control (SMC) has several benefits over traditional Proportional-Integral-Differential (PID) control in terms of fast transient response, robustness to parameter and component variations, and low sensitivity to loop disturbances. An All-Digital Sliding-Mode (ADSM) controlled DC-DC converter, utilizing single-bit oversampled frequency domain digitizers is proposed. In the proposed approach, feedback and reference digitizing Analog-to-Digital Converters (ADC) are based on a single-bit, first order Sigma-Delta frequency to digital converter, running at 32MHz over-sampling rate. The ADSM regulator achieves 1% settling time in less than 5uSec for a load variation of 600mA. The sliding-mode controller utilizes a high-bandwidth hysteretic differentiator and an integrator to perform the sliding control law in digital domain. The proposed approach overcomes the steady state error (or DC offset), and limits the switching frequency range, which are the two common problems associated with sliding-mode controllers. The IC is designed and fabricated on a 0.35um CMOS process occupying an active area of 2.72mm-squared. Measured peak efficiency is 83%.
ContributorsDashtestani, Ahmad (Author) / Bakkaloglu, Bertan (Thesis advisor) / Thornton, Trevor (Committee member) / Song, Hongjiang (Committee member) / Kiaei, Sayfe (Committee member) / Arizona State University (Publisher)
Created2013
Description
This thesis describes an approach to system identification based on compressive sensing and demonstrates its efficacy on a challenging classical benchmark single-input, multiple output (SIMO) mechanical system consisting of an inverted pendulum on a cart. Due to its inherent non-linearity and unstable behavior, very few techniques currently exist that are capable of identifying this system. The challenge in identification also lies in the coupled behavior of the system and in the difficulty of obtaining the full-range dynamics. The differential equations describing the system dynamics are determined from measurements of the system's input-output behavior. These equations are assumed to consist of the superposition, with unknown weights, of a small number of terms drawn from a large library of nonlinear terms. Under this assumption, compressed sensing allows the constituent library elements and their corresponding weights to be identified by decomposing a time-series signal of the system's outputs into a sparse superposition of corresponding time-series signals produced by the library components. The most popular techniques for non-linear system identification entail the use of ANN's (Artificial Neural Networks), which require a large number of measurements of the input and output data at high sampling frequencies. The method developed in this project requires very few samples and the accuracy of reconstruction is extremely high. Furthermore, this method yields the Ordinary Differential Equation (ODE) of the system explicitly. This is in contrast to some ANN approaches that produce only a trained network which might lose fidelity with change of initial conditions or if facing an input that wasn't used during its training. This technique is expected to be of value in system identification of complex dynamic systems encountered in diverse fields such as Biology, Computation, Statistics, Mechanics and Electrical Engineering.
ContributorsNaik, Manjish Arvind (Author) / Cochran, Douglas (Thesis advisor) / Kovvali, Narayan (Committee member) / Kawski, Matthias (Committee member) / Platte, Rodrigo (Committee member) / Arizona State University (Publisher)
Created2011