Model Predictive Control and Identification is an adaptive control technique which solves an online optimization problem to find process inputs for dual control problem. Its main goal is to bring robustness, to Model Predictive Control, increase the capability to handle uncertainties and time varying parameters in the processes. Theoretical properties, such as feasibility of the optimization pr...

-The generalized predictive control algorithm, it’s parameter adaptive recursive predicting and noise estimator is presented for a class of non-linear discrete-time systems with multiple time delays based on dynamic approximate non-linearization increment minimized recursive predicting model method. Generalized predictive control is employed for nonlinear systems with heavy multiple time delays...

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...

This paper present a new advanced control algorithm based on continuous minimization of predicted tracking errors, to achieve torque, rotor speed and rotor flux amplitude tracking objectives. This algorithm called a new Adaptive Nonlinear Predictive Control to induction motor drive uses a combination of the adaptive observer for rotor flux and Cascaded Nonlinear Predictive Control technique. Th...

Fuzzy control systems and predictive control techniques has been widely accepted for industrial applications. Fuzzy modeling has received particular attention with in the industry and the nance sectors due to the qualitative insight about the behaviour of the modeled process. Predictive control had been focused on linear systems and time variant linear systems (Adaptive Predictive Control). The...

the control of car following is essential due to its safety and its operational efficiency. for this purpose, this paper builds a model of car following behavior based on armax structure from a real traffic dataset and design a model predictive control (mpc) system. based on the relative distance and relative acceleration of each instant, the mpc predicts the future behavior of the leader vehic...

The adaptive generalized predictive control (GPC), which combines the process of system identification using recursive least-squares (RLS) algorithm and the process of generalized predictive feedback control design, has been presented and successfully implemented on testbeds. In this paper, the adaptive GPC algorithm is extended when the disturbance measurement signal is available for feedforwa...

AbstructThis application paper addresses the control of a nonminimum-phase plant with variable large orders and delays. The process concerned is full penetration gas tungsten arc welding. Based on an analysis of accepted adaptive algorithms, the generalized predictive control algorithm presented by Clarke et al. is selected as the principal control strategy. An adaptive generalized predictive d...

Abstact-This application paper addresses the control of a nonminimum-phase plant with variable large orders and delays. The process concerned is futl penetration gas tungsten arc weld' ing. Based on an analysis of accepted adaptive algorithms, the generalized predictive control algorithm presented by Clarke' Mohtadi and Tirffs is selected as the principal control strategy. An adaptive generaliz...

The goal of this paper is to propose suitable control methods for controlling of the highly nonlinear system represented by the mathematical model of the continuous stirred tank reactor (CSTR) with so called van der Vusse reaction inside. Temperature of the reactant is controlled by the heat removal of the cooling liquid in the reactor’s jacket. Two control strategies were suggested – adaptive ...