This paper reviews the identification and optimal control problems using recurrent neural
networks and reinforcement learning for nonlinear systems both in discrete-and continuous …

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 …

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 …

Despite the remarkable theoretical accomplishments and successful applications of
adaptive control, the field is not sufficiently mature to solve challenging control problems …

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 …

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 …

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 …

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 …

Multiple model methodology is an important approach in modelling, identification and
control of complicated systems with large uncertainties (parameter uncertainty or even …

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 …