16,789 research outputs found
Yet Another Tutorial of Disturbance Observer: Robust Stabilization and Recovery of Nominal Performance
This paper presents a tutorial-style review on the recent results about the
disturbance observer (DOB) in view of robust stabilization and recovery of the
nominal performance. The analysis is based on the case when the bandwidth of
Q-filter is large, and it is explained in a pedagogical manner that, even in
the presence of plant uncertainties and disturbances, the behavior of real
uncertain plant can be made almost similar to that of disturbance-free nominal
system both in the transient and in the steady-state. The conventional DOB is
interpreted in a new perspective, and its restrictions and extensions are
discussed
Issues in the design of switched linear systems : a benchmark study
In this paper we present a tutorial overview of some of the issues that arise in the design of switched linear control systems. Particular emphasis is given to issues relating to stability and control system realisation. A benchmark regulation problem is then presented. This problem is most naturally solved by means of a switched control design. The challenge to the community is to design a control system that meets the required performance specifications and permits the application of rigorous analysis techniques. A simple design solution is presented and the limitations of currently available analysis techniques are illustrated with reference to this example
Relaxing Fundamental Assumptions in Iterative Learning Control
Iterative learning control (ILC) is perhaps best decribed as an open loop feedforward control technique where the feedforward signal is learned through repetition of a single task. As the name suggests, given a dynamic system operating on a finite time horizon with the same desired trajectory, ILC aims to iteratively construct the inverse image (or its approximation) of the desired trajectory to improve transient tracking. In the literature, ILC is often interpreted as feedback control in the iteration domain due to the fact that learning controllers use information from past trials to drive the tracking error towards zero. However, despite the significant body of literature and powerful features, ILC is yet to reach widespread adoption by the control community, due to several assumptions that restrict its generality when compared to feedback control. In this dissertation, we relax some of these assumptions, mainly the fundamental invariance assumption, and move from the idea of learning through repetition to two dimensional systems, specifically repetitive processes, that appear in the modeling of engineering applications such as additive manufacturing, and sketch out future research directions for increased practicality: We develop an L1 adaptive feedback control based ILC architecture for increased robustness, fast convergence, and high performance under time varying uncertainties and disturbances. Simulation studies of the behavior of this combined L1-ILC scheme under iteration varying uncertainties lead us to the robust stability analysis of iteration varying systems, where we show that these systems are guaranteed to be stable when the ILC update laws are designed to be robust, which can be done using existing methods from the literature. As a next step to the signal space approach adopted in the analysis of iteration varying systems, we shift the focus of our work to repetitive processes, and show that the exponential stability of a nonlinear repetitive system is equivalent to that of its linearization, and consequently uniform stability of the corresponding state space matrix.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133232/1/altin_1.pd
Parameter Estimation of Sigmoid Superpositions: Dynamical System Approach
Superposition of sigmoid function over a finite time interval is shown to be
equivalent to the linear combination of the solutions of a linearly
parameterized system of logistic differential equations. Due to the linearity
with respect to the parameters of the system, it is possible to design an
effective procedure for parameter adjustment. Stability properties of this
procedure are analyzed. Strategies shown in earlier studies to facilitate
learning such as randomization of a learning sequence and adding specially
designed disturbances during the learning phase are requirements for
guaranteeing convergence in the learning scheme proposed.Comment: 30 pages, 7 figure
Robust Adaptive Control of Linear Parameter-Varying Systems with Unmatched Uncertainties
This paper presents a robust adaptive control solution for linear
parameter-varying (LPV) systems with unknown input gain and unmatched nonlinear
(state- and time-dependent) uncertainties based on the adaptive
control architecture and peak-to-peak gain (PPG) analysis/minimization from
robust control. Specifically, we introduce new tools for stability and
performance analysis leveraging the PPG bound of an LPV system that is
computable using linear matrix inequality (LMI) techniques. A
piecewise-constant estimation law is introduced to estimate the lumped
uncertainty with quantifiable error bounds, which can be systematically
improved by reducing the estimation sampling time. We also present a new
approach to attenuate the unmatched uncertainty based on the PPG minimization
that is applicable to a broad class of systems with linear nominal dynamics. In
addition, we derive transient and steady-state performance bounds in terms of
the input and output signals of the actual closed-loop system as compared to
the same signals of a virtual reference system that represents the possibly
best achievable performance. Under mild assumptions, we prove that the
transient performance bounds can be uniformly reduced by decreasing the
estimation sampling time, which is subject only to hardware limitations. The
theoretical development is validated by extensive simulations on the
short-period dynamics of an F-16 aircraft
- …