1,430 research outputs found
Output feedback NN control for two classes of discrete-time systems with unknown control directions in a unified approach
10.1109/TNN.2008.2003290IEEE Transactions on Neural Networks19111873-1886ITNN
Adaptive Predictive Control Using Neural Network for a Class of Pure-feedback Systems in Discrete-time
10.1109/TNN.2008.2000446IEEE Transactions on Neural Networks1991599-1614ITNN
Fuzzy-logic-based control, filtering, and fault detection for networked systems: A Survey
This paper is concerned with the overview of the recent progress in fuzzy-logic-based filtering, control, and fault detection problems. First, the network technologies are introduced, the networked control systems are categorized from the aspects of fieldbuses and industrial Ethernets, the necessity of utilizing the fuzzy logic is justified, and the network-induced phenomena are discussed. Then, the fuzzy logic control strategies are reviewed in great detail. Special attention is given to the thorough examination on the latest results for fuzzy PID control, fuzzy adaptive control, and fuzzy tracking control problems. Furthermore, recent advances
on the fuzzy-logic-based filtering and fault detection problems are reviewed. Finally, conclusions are given and some possible future research directions are pointed out, for example, topics on two-dimensional networked systems, wireless networked control systems, Quality-of-Service (QoS) of networked systems, and fuzzy access control in open networked systems.This work was supported in part by the National Natural Science Foundation of China under Grants 61329301,
61374039, 61473163, and 61374127, the Hujiang Foundation of China under Grants C14002 andD15009, the Engineering and Physical Sciences Research Council (EPSRC) of the UK, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany
Bayesian Nonparametric Adaptive Control using Gaussian Processes
This technical report is a preprint of an article submitted to a journal.Most current Model Reference Adaptive Control
(MRAC) methods rely on parametric adaptive elements, in
which the number of parameters of the adaptive element are
fixed a priori, often through expert judgment. An example of
such an adaptive element are Radial Basis Function Networks
(RBFNs), with RBF centers pre-allocated based on the expected
operating domain. If the system operates outside of the expected
operating domain, this adaptive element can become
non-effective in capturing and canceling the uncertainty, thus
rendering the adaptive controller only semi-global in nature.
This paper investigates a Gaussian Process (GP) based Bayesian
MRAC architecture (GP-MRAC), which leverages the power and
flexibility of GP Bayesian nonparametric models of uncertainty.
GP-MRAC does not require the centers to be preallocated, can
inherently handle measurement noise, and enables MRAC to
handle a broader set of uncertainties, including those that are
defined as distributions over functions. We use stochastic stability
arguments to show that GP-MRAC guarantees good closed loop
performance with no prior domain knowledge of the uncertainty.
Online implementable GP inference methods are compared in
numerical simulations against RBFN-MRAC with preallocated
centers and are shown to provide better tracking and improved
long-term learning.This research was supported in part by ONR MURI Grant
N000141110688 and NSF grant ECS #0846750
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