This paper studies neural learning control. Based on an earlier result for deterministic learning of unknown system dynamics from a stable control process, this paper provides detailed analysis on how the learned knowledge can be effectively exploited to achieve stability and unproved control performance. Comparisons on neural learning control with adaptive neural control and linear control are also included. The effectiveness of the neural learning control approach is demonstrated using simulations.link_to_subscribed_fulltex
This paper focuses on dynamic learning from neural control for a class of nonlinear strict-feedback ...
This paper reviews the architecture, representation capability, training and learning ability of a c...
The modern stage of development of science and technology is characterized by a rapid increase in th...
This paper studies deterministic learning for nonlinear systems in the sense that an appropriately d...
This paper shows, how wellknown supervised learning techniques can be applied to learn control of un...
The paper explains an unconventional learning control method based on assumptions in the literature ...
Department Head: Stephen B. Seidman.2000 Summer.Includes bibliographical references (pages 227-231)....
ABSTRACT: Neural networks can be used to solve highly nonlinear control problems. This paper shows h...
Abstract—In this paper, a stabilization method based on the input–output conicity criterion is prese...
In this paper the Learning Feed Forward Control (LFFC) scheme is considered. This type of controller...
In this paper the learning feedforward control (LFFC) scheme is considered. This type of controller ...
The performance of sub-optimal feedback controllers can be improved in several ways. In this paper a...
This paper addresses the problem of composite tracking control and adaptive learning for discrete-ti...
This paper addresses the problem of composite tracking control and adaptive learning for discrete-ti...
This paper addresses the problem of composite tracking control and adaptive learning for discrete-ti...
This paper focuses on dynamic learning from neural control for a class of nonlinear strict-feedback ...
This paper reviews the architecture, representation capability, training and learning ability of a c...
The modern stage of development of science and technology is characterized by a rapid increase in th...
This paper studies deterministic learning for nonlinear systems in the sense that an appropriately d...
This paper shows, how wellknown supervised learning techniques can be applied to learn control of un...
The paper explains an unconventional learning control method based on assumptions in the literature ...
Department Head: Stephen B. Seidman.2000 Summer.Includes bibliographical references (pages 227-231)....
ABSTRACT: Neural networks can be used to solve highly nonlinear control problems. This paper shows h...
Abstract—In this paper, a stabilization method based on the input–output conicity criterion is prese...
In this paper the Learning Feed Forward Control (LFFC) scheme is considered. This type of controller...
In this paper the learning feedforward control (LFFC) scheme is considered. This type of controller ...
The performance of sub-optimal feedback controllers can be improved in several ways. In this paper a...
This paper addresses the problem of composite tracking control and adaptive learning for discrete-ti...
This paper addresses the problem of composite tracking control and adaptive learning for discrete-ti...
This paper addresses the problem of composite tracking control and adaptive learning for discrete-ti...
This paper focuses on dynamic learning from neural control for a class of nonlinear strict-feedback ...
This paper reviews the architecture, representation capability, training and learning ability of a c...
The modern stage of development of science and technology is characterized by a rapid increase in th...