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 Kal...

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...

hydropower plant and especially micro-hydropower plant is an available, reliable and economical energy source. micro-hydropower plant is one of the most environment-friendly technology, use and development of which leads to reduction of energy consumption sporadically and worldwide. along with the growth of these power plants, the issues related to the control of electrical parameters such as l...

In this research, Generalized Predictivecontrol (GPC) is proposed for the control of a stabilizing loop from a two axis gimbal seeker. In fact, there are some views about using GPC type controller which are two folds. First, it drives the stabilization loops that are made by a DC motor, Rate Gyro, inertia and cross coupling unit in between two channels using the predictive model type controller...

typical production objectives in distillation process require the delivery of products whose compositions meet certain specifications. the distillation control system, therefore, must hold product compositions as near the set points as possible in faces of upset. in this project, inferential model predictive control, that utilizes an artificial neural network estimator and model predictive cont...

In this paper, a neural network based predictive controller is designed for controlling the liquid level of the coupled tank system. The controlled process is a nonlinear system; therefore, a nonlinear prediction method can be a better match in a predictive control strategy. The neural network predictive controller that is discussed in this paper uses a neural network model of a nonlinear plant...

In this report a continuous time model predictive controller is applied to the control of an Unmanned Aerial Vehicle (UAV). The purpose of this work, is to see if it is possible to design a good tracking controller that is capable of simulating a highly nonlinear aircraft system. For the simulations a MATLAB Simulink model of the UAV 'Ariel' is used. The nonlinear Simulink model covers all the ...