Iterative learning control (ILC) system is modelled and treated as a 2D system in this paper. Based on single-batch and multi-batch cost functions, 2D model predictive iterative learning control (2D-MPILC) schemes are developed in the framework of model predictive control (MPC) for the 2D system. Structure analysis shows that the resulted 2D-MPILC laws are causal and they implicitly combine a t...

An unconstrained model predictive control (MPC) strategy is presented for single input-single output (SISO) control of a reactive distillation column. The predictive model of the MPC is based on the autoregressive moving average (ARX) model structure, whose parameters are updated on-line using the process measurement information. The design and performance of the control strategy is evaluated b...

This paper addresses the problem of dynamic optimization of ethanol production. This process is described by a nonlinear model. A Model Predictive Control (MPC) has been implemented in order to optimize the bioprocess dynamically. Two algorithms were used together with a MPC: the Pattern Search (PS) and the Iterative Ant Colony Algorithm (IACA). They were compared with an open-loop control expe...

in this paper, a recurrent fuzzy-neural network (rfnn) controller with neural network identifier in direct control model is designed to control the speed and exhaust temperature of the gas turbine in a combined cycle power plant. since the turbine operation in combined cycle unit is considered, speed and exhaust temperature of the gas turbine should be simultaneously controlled by fuel command ...

Model Predictive Control (MPC) is a control strategy that has been used successfully in numerous and diverse application areas. The aim of the present article is to discuss how the basic ideas of MPC can be extended to problems involving random model uncertainty with known probability distribution. We discuss cost indices, constraints, closed loop properties and implementation issues.

A simple Model Predictive Control (MPC) algorithm of velocity (incremental) form is presented. The proposed MPC controller is insensitive to slowly varying system and measurement trends and therefore has integral action. The presented algorithm is illustrated by both simulations and practical experiments on a quadruple tank MIMO process.

Model Predictive Control (MPC) is increasingly being proposed for application to miniaturized devices, fast and/or embedded systems. A major obstacle to this is its computation time requirement. Continuing our previous studies of implementing constrained MPC on Field Programmable Gate Arrays (FPGA), this paper begins to exploit the possibilities of parallel computation, with the aim of speeding...

Model Predictive Control (MPC) is used to improve the performance of energy control for swinging up a pendulum. A new MPC method is developed in continuous time, but it explicitly considers its digital implementation letting the control signal be piecewise constant. The stability properties of the algorithm are analyzed in terms of the free MPC design parameters. The achieved performance improv...

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 ...