wind energy today, has attracted widespread interest from among a variety of sources of renewable energy in the world. owing to the increasing demand for production of electrical energy for electricity networks by using wind power, it is essential that wind power plants are actively incorporated in the network’s performance using an appropriate control system. in general, these wind power plant...

an adaptive version of growing and pruning rbf neural network has been used to predict the system output and implement linear model-based predictive controller (lmpc) and non-linear model-based predictive controller (nmpc) strategies. a radial-basis neural network with growing and pruning capabilities is introduced to carry out on-line model identification.an unscented kalman filter (ukf) algor...

the present sought to draw a comparison between model predictive control performance and two other controllers named simple pi and selective pi in controlling large-scale natural gas transport networks. a nonlinear dynamic model of representative gas pipeline was derived from pipeline governing rules and simulated in simulink® environment of matlab®. control schemes were designed to provide a s...

in this paper standing balance control of a biped with toe-joint is presented. the model consists of an inverted pendulum as the upper body and the foot contains toe-joint. the biped is actuated by two torques at ankle-joint and toe-joint to regulate the upper body in upright position. to model the interaction between foot and the ground, configuration constraints are defined and utilized. to s...

the control of fluidized-bed operations processes is still one of the major areas of research due to the complexity of the process and the inherent nonlinearity and varying dynamics involved in its operation. there are varieties of problems in chemical engineering that can be formulated as nonlinear programming (nlps). the quality of the developed solution significantly affects the performance ...

this article deals with the issues associated with developing a new design methodology for the nonlinear model-predictive control (mpc) of a chemical plant. a combination of multiple neural networks is selected and used to model a nonlinear multi-input multi-output (mimo) process with time delays.  an optimization procedure for a neural mpc algorithm based on this model is then developed. the p...