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Title DERIVATIVE-FREE KALMAN FILTER-BASED CONTROL OF NONLINEAR SYSTEMS WITH APPLICATION TO TRANSFEMORAL PROSTHESES
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Publication Date
Degree MSin Electrical Engineering
Discipline/Department Washkewicz College of Engineering
Degree Level masters
University/Publisher Cleveland State University
Abstract Derivative-free Kalman filtering (DKF) for estimation-based control of a special class of nonlinear systems is presented. The method includes a standard Kalman filter for the estimation of both states and unknown inputs, and a nonlinear system that is transformed to controllable canonical state space form through feedback linearization (FL). A direct current (DC) motor with an input torque that is a nonlinear function of the state is considered as a case study for a nonlinear single-input-single-output (SISO) system. A three degree-of-freedom (DOF) robot / prosthesis system, which includes a robot that emulates human hip and thigh motion and a powered (active) transfemoral prosthesis disturbed by ground reaction force (GRF), is considered as a case study for a nonlinear multi-input-multi-output (MIMO) system. A PD/PI control term is used to compensate for the unknown GRF. Simulation results show that FL can compensate for the system's nonlinearities through a virtual control term, in contrast to Taylor series linearization, which is only a first-order linearization method. FL improves estimation performance relative to the extended Kalman filter, and in some cases improves the initial condition region of attraction as well. A stability analysis of the DKF-based control method, considering both estimation and unknown input compensation, is also presented. The error dynamics are studied in both frequency and time domains. The derivative of the unknown input plays a key role in the error dynamics and is the primary limiting factor of the closed-loop estimation-based control system stability. It is shown that in realistic systems the derivative of the unknown input is the primary determinant of the region of convergence. It is shown that the tracking error asymptotically converges to the derivative of the unknown input.
Subjects/Keywords Robotics; Electrical Engineering; Mechanical Engineering; Mathematics
Contributors Simon, Dan (Advisor)
Language en
Rights unrestricted ; This thesis or dissertation is protected by copyright: some rights reserved. It is licensed for use under a Creative Commons license. Specific terms and permissions are available from this document's record in the OhioLINK ETD Center. [Always confirm rights and permissions with the source record.]
Country of Publication us
Format application/pdf
Record ID oai:etd.ohiolink.edu:csu1502481884476173
Repository ohiolink
Date Retrieved
Date Indexed 2017-09-11
Grantor Cleveland State University

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