model predictive controller is widely used in industrial plants. uncertainty is one of the critical issues in real systems. in this paper, the direct adaptive simplified model predictive control (smpc) is proposed for unknown or time varying plants with uncertainties. by estimating the plant step response in each sample, the controller is designed and the controller coefficients are directly ca...

Model predictive controller is widely used in industrial plants. Uncertainty is one of the critical issues in real systems. In this paper, the direct adaptive Simplified Model Predictive Control (SMPC) is proposed for unknown or time varying plants with uncertainties. By estimating the plant step response in each sample, the controller is designed and the controller coefficients are directly ca...

this paper presents a theoretical approach to implementation of the “multi-realization of nonlinear mimo systems”. this method aims to find state-variable realization for a set of systems, sharing as many parameters as possible. in this paper a special nonlinear multi-realization problem, namely the multi-realization of feedback linearizable nonlinear systems is considered and an algorithm for ...

an adaptive input-output linearization method for general nonlinear systems is developed without using states of the system. another key feature of this structure is the fact that, it does not need model of the system. in this scheme, neurolinearizer has few weights, so it is practical in adaptive situations.  online training of neurolinearizer is compared to model predictive recurrent training...

this paper presents fuzzy and conventional performance of model reference adaptive control(mrac) to control a dc drive. the aims of this work are achieving better match of motor speed with reference speed, decrease of noises under load changes and disturbances, and increase of system stability. the operation of nonadaptive control and the model reference of fuzzy and conventional adaptive contr...

this paper presents a discrete-time robust control for electrically driven robot manipulators in the task space. a novel discrete-time model-free control law is proposed by employing an adaptive fuzzy estimator for the compensation of the uncertainty including model uncertainty, external disturbances and discretization error. parameters of the fuzzy estimator are adapted to minimize the estimat...

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...