Identification and optimal control of nonlinear systems using recurrent neural networks and reinforcement learning: An overview
A Perrusquía, W Yu - Neurocomputing, 2021 - Elsevier
This paper reviews the identification and optimal control problems using recurrent neural
networks and reinforcement learning for nonlinear systems both in discrete-and continuous …
networks and reinforcement learning for nonlinear systems both in discrete-and continuous …
[BOOK][B] Switched linear systems: control and design
Z Sun - 2006 - books.google.com
Switched linear systems have a long history in the control literature but-along with hybrid
systems more generally-they have enjoyed a particular growth in interest since the 1990s …
systems more generally-they have enjoyed a particular growth in interest since the 1990s …
Nonlinear adaptive control using neural networks and multiple models
In this paper, adaptive control of a class of nonlinear discrete time dynamical systems with
boundedness of all signals is established by using a linear robust adaptive controller and a …
boundedness of all signals is established by using a linear robust adaptive controller and a …
Multiple model adaptive control with mixing
M Kuipers, P Ioannou - IEEE transactions on automatic control, 2010 - ieeexplore.ieee.org
Despite the remarkable theoretical accomplishments and successful applications of
adaptive control, the field is not sufficiently mature to solve challenging control problems …
adaptive control, the field is not sufficiently mature to solve challenging control problems …
Adaptive control using multiple models, switching and tuning
The past decade has witnessed a great deal of interest in both the theory and practice of
adaptive control using multiple models, switching, and tuning. The general approach was …
adaptive control using multiple models, switching, and tuning. The general approach was …
Nonlinear multivariable adaptive control using multiple models and neural networks
Y Fu, T Chai - Automatica, 2007 - Elsevier
In this paper, a multivariable adaptive control approach is proposed for a class of unknown
nonlinear multivariable discrete-time dynamical systems. By introducing a k-difference …
nonlinear multivariable discrete-time dynamical systems. By introducing a k-difference …
A practical multiple model adaptive strategy for multivariable model predictive control
D Dougherty, D Cooper - Control engineering practice, 2003 - Elsevier
Model predictive control (MPC) has become the leading form of advanced multivariable
control in the chemical process industry. The objective of this work is to introduce a multiple …
control in the chemical process industry. The objective of this work is to introduce a multiple …
Stationary algorithmic balancing for dynamic email re-ranking problem
Email platforms need to generate personalized rankings of emails that satisfy user
preferences, which may vary over time. We approach this as a recommendation problem …
preferences, which may vary over time. We approach this as a recommendation problem …
Survey and tutorial on multiple model methodologies in modelling, identification and control
W Zhang, L Zhao - International Journal of Modelling …, 2019 - inderscienceonline.com
Multiple model methodology is an important approach in modelling, identification and
control of complicated systems with large uncertainties (parameter uncertainty or even …
control of complicated systems with large uncertainties (parameter uncertainty or even …
Adaptive control based on retrospective cost optimization
We present a discrete-time adaptive control law for stabilization, command-following, and
disturbance rejection that is effective for systems that are unstable, multi-input/multi-output …
disturbance rejection that is effective for systems that are unstable, multi-input/multi-output …