Matching Items (4)
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- All Subjects: System identification
- Creators: Platte, Rodrigo
- Creators: Rivera, Daniel E
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
Description
This thesis considers the application of basis pursuit to several problems in system identification. After reviewing some key results in the theory of basis pursuit and compressed sensing, numerical experiments are presented that explore the application of basis pursuit to the black-box identification of linear time-invariant (LTI) systems with both finite (FIR) and infinite (IIR) impulse responses, temporal systems modeled by ordinary differential equations (ODE), and spatio-temporal systems modeled by partial differential equations (PDE). For LTI systems, the experimental results illustrate existing theory for identification of LTI FIR systems. It is seen that basis pursuit does not identify sparse LTI IIR systems, but it does identify alternate systems with nearly identical magnitude response characteristics when there are small numbers of non-zero coefficients. For ODE systems, the experimental results are consistent with earlier research for differential equations that are polynomials in the system variables, illustrating feasibility of the approach for small numbers of non-zero terms. For PDE systems, it is demonstrated that basis pursuit can be applied to system identification, along with a comparison in performance with another existing method. In all cases the impact of measurement noise on identification performance is considered, and it is empirically observed that high signal-to-noise ratio is required for successful application of basis pursuit to system identification problems.
ContributorsThompson, Robert C. (Author) / Platte, Rodrigo (Thesis advisor) / Gelb, Anne (Committee member) / Cochran, Douglas (Committee member) / Arizona State University (Publisher)
Created2012
Description
Behavioral health problems such as physical inactivity are among the main causes of mortality around the world. Mobile and wireless health (mHealth) interventions offer the opportunity for applying control engineering concepts in behavioral change settings. Social Cognitive Theory (SCT) is among the most influential theories of health behavior and has been used as the conceptual basis of many behavioral interventions. This dissertation examines adaptive behavioral interventions for physical inactivity problems based on SCT using system identification and control engineering principles. First, a dynamical model of SCT using fluid analogies is developed. The model is used throughout the dissertation to evaluate system identification approaches and to develop control strategies based on Hybrid Model Predictive Control (HMPC). An initial system identification informative experiment is designed to obtain basic insights about the system. Based on the informative experimental results, a second optimized experiment is developed as the solution of a formal constrained optimization problem. The concept of Identification Test Monitoring (ITM) is developed for determining experimental duration and adjustments to the input signals in real time. ITM relies on deterministic signals, such as multisines, and uncertainty regions resulting from frequency domain transfer function estimation that is performed during experimental execution. ITM is motivated by practical considerations in behavioral interventions; however, a generalized approach is presented for broad-based multivariable application settings such as process control. Stopping criteria for the experimental test utilizing ITM are developed using both open-loop and robust control considerations.
A closed-loop intensively adaptive intervention for physical activity is proposed relying on a controller formulation based on HMPC. The discrete and logical features of HMPC naturally address the categorical nature of the intervention components that include behavioral goals and reward points. The intervention incorporates online controller reconfiguration to manage the transition between the behavioral initiation and maintenance training stages. Simulation results are presented to illustrate the performance of the system using a model for a hypothetical participant under realistic conditions that include uncertainty. The contributions of this dissertation can ultimately impact novel applications of cyberphysical system in medical applications.
A closed-loop intensively adaptive intervention for physical activity is proposed relying on a controller formulation based on HMPC. The discrete and logical features of HMPC naturally address the categorical nature of the intervention components that include behavioral goals and reward points. The intervention incorporates online controller reconfiguration to manage the transition between the behavioral initiation and maintenance training stages. Simulation results are presented to illustrate the performance of the system using a model for a hypothetical participant under realistic conditions that include uncertainty. The contributions of this dissertation can ultimately impact novel applications of cyberphysical system in medical applications.
ContributorsMartín Moreno, César Antonio (Author) / Rivera, Daniel E (Thesis advisor) / Hekler, Eric B. (Committee member) / Peet, Matthew M (Committee member) / Tsakalis, Konstantinos S (Committee member) / Arizona State University (Publisher)
Created2016
Description
The lack of healthy behaviors - such as physical activity and balanced diet - in
modern society is responsible for a large number of diseases and high mortality rates in
the world. Adaptive behavioral interventions have been suggested as a way to promote
sustained behavioral changes to address these issues. These adaptive interventions
can be modeled as closed-loop control systems, and thus applying control systems
engineering and system identification principles to behavioral settings might provide
a novel way of improving the quality of such interventions.
Good understanding of the dynamic processes involved in behavioral experiments
is a fundamental step in order to design such interventions with control systems ideas.
In the present work, two different behavioral experiments were analyzed under the
light of system identification principles and modelled as dynamic systems.
In the first study, data gathered over the course of four days served as the basis for
ARX modeling of the relationship between psychological constructs (negative affect
and self-efficacy) and the intensity of physical activity. The identified models suggest
that this behavioral process happens with self-regulation, and that the relationship
between negative affect and self-efficacy is represented by a second order underdamped
system with negative gain, while the relationship between self-efficacy and physical
activity level is an overdamped second order system with positive gain.
In the second study, which consisted of single-bouts of intense physical activity,
the relation between a more complex set of behavioral variables was identified as a
semi-physical model, with a theoretical set of system equations derived from behavioral
theory. With a prescribed set of physical activity intensities, it was found that less fit
participants were able to get higher increases in affective state, and that self-regulation
processes are also involved in the system.
modern society is responsible for a large number of diseases and high mortality rates in
the world. Adaptive behavioral interventions have been suggested as a way to promote
sustained behavioral changes to address these issues. These adaptive interventions
can be modeled as closed-loop control systems, and thus applying control systems
engineering and system identification principles to behavioral settings might provide
a novel way of improving the quality of such interventions.
Good understanding of the dynamic processes involved in behavioral experiments
is a fundamental step in order to design such interventions with control systems ideas.
In the present work, two different behavioral experiments were analyzed under the
light of system identification principles and modelled as dynamic systems.
In the first study, data gathered over the course of four days served as the basis for
ARX modeling of the relationship between psychological constructs (negative affect
and self-efficacy) and the intensity of physical activity. The identified models suggest
that this behavioral process happens with self-regulation, and that the relationship
between negative affect and self-efficacy is represented by a second order underdamped
system with negative gain, while the relationship between self-efficacy and physical
activity level is an overdamped second order system with positive gain.
In the second study, which consisted of single-bouts of intense physical activity,
the relation between a more complex set of behavioral variables was identified as a
semi-physical model, with a theoretical set of system equations derived from behavioral
theory. With a prescribed set of physical activity intensities, it was found that less fit
participants were able to get higher increases in affective state, and that self-regulation
processes are also involved in the system.
ContributorsSeixas, Gustavo Mesel Lobo (Author) / Rivera, Daniel E (Thesis advisor) / Peet, Matthew M (Committee member) / Alford, Terry L. (Committee member) / Arizona State University (Publisher)
Created2016