Higher order mathematical modeling and discrete fast output sampling control synthesis for PHWR (Pressurized Heavy Water Reactor)-type nuclear power plant is presented in this paper. The nonlinear dynamic model of PHWR power reactor is developed based on reactor, logarithmic amplifier, moderator level control valve and reactivity system. The nonlinear model is characterized by 15 state variables and one input. This higher order nonlinear model is linearized at full power operating point. A standard higher order state space model is realized for controller design purpose. An advanced discrete controller is synthesized for state space model of PHWR using fast output sampling methodology. Noise sensitivity is a typical problem with this type o...
This work proposes adaptive control-based design strategies to control a pressurized water reactor (...
This study proposes a feedback linearization-based control using a dynamic neural network to control...
This study proposes a feedback linearization-based control using a dynamic neural network to control...
In this paper, a nominal SISO (Single Input Single Output) model of PHWR (Pressurized Heavy Water Re...
In this paper, a nominal SISO (Single Input Single Output) model of PHWR (Pressurized Heavy Water R...
This article presents an integrated non-linear dynamic model of a Pressurized Water-type Nuclear Rea...
The paper presents the design of state feedback control for a large pressurized heavy water reactor ...
A method is presented to design a spatial control system of a large pressurised heavy water reactor ...
This paper presents a novel approach to design a sliding mode control using a new computationally ef...
This work proposes a new hybrid control strategy for a pressurized water type nuclear power plant by...
A suboptimal controller has been developed for a Boiling Water Reactor Nuclear Power Plant, using th...
This paper presents an application of H-infinity optimal control to a nuclear power plant. Data of a...
This paper presents an application of H-infinity-optimal control to a nuclear power plant. Data from...
The present work aims to introduce a nonlinear control scheme that combines intelligent feedback lin...
The present work aims to introduce a nonlinear control scheme that combines intelligent feedback lin...
This work proposes adaptive control-based design strategies to control a pressurized water reactor (...
This study proposes a feedback linearization-based control using a dynamic neural network to control...
This study proposes a feedback linearization-based control using a dynamic neural network to control...
In this paper, a nominal SISO (Single Input Single Output) model of PHWR (Pressurized Heavy Water Re...
In this paper, a nominal SISO (Single Input Single Output) model of PHWR (Pressurized Heavy Water R...
This article presents an integrated non-linear dynamic model of a Pressurized Water-type Nuclear Rea...
The paper presents the design of state feedback control for a large pressurized heavy water reactor ...
A method is presented to design a spatial control system of a large pressurised heavy water reactor ...
This paper presents a novel approach to design a sliding mode control using a new computationally ef...
This work proposes a new hybrid control strategy for a pressurized water type nuclear power plant by...
A suboptimal controller has been developed for a Boiling Water Reactor Nuclear Power Plant, using th...
This paper presents an application of H-infinity optimal control to a nuclear power plant. Data of a...
This paper presents an application of H-infinity-optimal control to a nuclear power plant. Data from...
The present work aims to introduce a nonlinear control scheme that combines intelligent feedback lin...
The present work aims to introduce a nonlinear control scheme that combines intelligent feedback lin...
This work proposes adaptive control-based design strategies to control a pressurized water reactor (...
This study proposes a feedback linearization-based control using a dynamic neural network to control...
This study proposes a feedback linearization-based control using a dynamic neural network to control...