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...

laguerre function has many advantages such as good approximation capability for different systems, low computational complexity and the facility of on-line parameter identification. therefore, it is widely adopted for complex industrial process control. in this work, laguerre function based adaptive model predictive control algorithm (ampc) was implemented to control continuous stirred tank rea...

Model-based predictive control (MPC) is one of the most efficient techniques that is widely used in industrial applications. In such controllers, increasing the prediction horizon results in better selection of the optimal control signal sequence. On the other hand, increasing the prediction horizon increase the computational time of the optimization process which make it impossible to be imple...

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...