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Neural-network techniques are investigated in an application to the identification and subsequent on-line control of a process exhibiting nonlinearities and typical disturbances. The method proposed consists from a novel identification technique based on extended memory adaptation (EMA) and an efficient implementation of the predictive control based on a nonlinear programming method. An forced circulation...
In multivariable control the study of loop interactions is of prime importance. The P or V-canonical form structures, where loop interactions are dealt with as feed-forward couplings, are popular transfer function representations used to describe multivariable processes. Another alternative design consists of a bank of several Single Input Single Output (SISO) controllers linked by feed-forward terms...
This paper presents a new approach to deal with the nonlinearity of control system by using Multi Model Predictive Control (MPC) strategies. The idea of this research is using Fuzzy model to divide the nonlinear system into several sub linear systems which can be applied linear MPC controller. Firstly, the structure of Takagi-Sugeno (T-S) Fuzzy model is developed and optimized using Subtractive Clustering...
The predictive controls are one of the major control strategies in the process control. However, in general, an exact model of the controlled system is required in order to predict the system's outputs accurately. In this paper, we propose an adaptive predictive control using an adaptive output estimator with a simple structure for uncertain controlled systems. In the proposed method, the stability...
As the network is introduced into the system, it will increase complexity of system dynamics and plant modeling, data-driven control theory is proposed to design the controller independent of the model in this paper. Construct the output data matrix and the input data matrix using input / output current data and historical data, establish a linear relationship between the two, thus get the predictive...
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