2 research outputs found
Deep Model Reference Adaptive Control
We present a new neuroadaptive architecture: Deep Neural Network based Model
Reference Adaptive Control (DMRAC). Our architecture utilizes the power of deep
neural network representations for modeling significant nonlinearities while
marrying it with the boundedness guarantees that characterize MRAC based
controllers. We demonstrate through simulations and analysis that DMRAC can
subsume previously studied learning based MRAC methods, such as concurrent
learning and GP-MRAC. This makes DMRAC a highly powerful architecture for
high-performance control of nonlinear systems with long-term learning
properties.Comment: Accepted in IEEE CDC-201
RKHS Embedding for Estimating Nonlinear Piezoelectric Systems
Nonlinearities in piezoelectric systems can arise from internal factors such
as nonlinear constitutive laws or external factors like realizations of
boundary conditions. It can be difficult or even impossible to derive detailed
models from the first principles of all the sources of nonlinearity in a
system. As a specific example, in traditional modeling techniques that use
electric enthalpy density with higher-order terms, it can be problematic to
choose which polynomial nonlinearities are essential. This paper introduces
adaptive estimator techniques to estimate the nonlinearities that can arise in
certain piezoelectric systems. Here an underlying assumption is that the
nonlinearities can be modeled as functions in a reproducing kernel Hilbert
space (RKHS). Unlike traditional modeling approaches, the approach discussed in
this paper allows the development of models without knowledge of the precise
form or structure of the nonlinearity. This approach can be viewed as a
data-driven method to approximate the unknown nonlinear system. This paper
introduces the theory behind the adaptive estimator and studies the
effectiveness of this approach numerically for a class of nonlinear
piezoelectric composite beams.Comment: 20 pages, 15 figures, 2 tables, 1 algorith