Multivariable non-linear system prediction function control method based on Hammerstein model

Abstract

The present invention provides a controlling method of a multivariate nonlinear system prediction function based on the hammerstein model, characterized in that the method includes following steps: (1) establishing the hammerstein model according to the process characteristic and the input output data; (2) solving the prediction function control rate of the multivariate linear subsystem according to the hammerstein model linear part model parameters, set values and practical process output of; (3) solving the equation V(k)=F(U(k)) to obtain optimal control law U(k) according to the hammerstein model nonlinear part model parameters and the multivariate linear multivariate nonlinear system prediction function control rate; (4) solving and implementing the optimal control law according to multivariate nonlinear system prediction function controller. The invention realizes one prediction function control of multivariate nonlinear system, realizing that the control problem of the nonlinear system is transformed to the linear system control problem and the solving problem of the nonlinear equation, improving the solving speed of the optimal control law, reducing the on-line computational complexity, ensuring the real-time requirement of the control system.

Description

technical field [0001] The invention relates to the field of industrial process control, in particular to a Hammerstein model-based multivariable nonlinear system predictive function control method. Background technique [0002] Most industrial controls have constraints and have nonlinear characteristics, time-varying and uncertainties. Most objects with weak nonlinearities can be approximated by linearized models. As a model mismatch, through the robust design of the system Or online identification of model parameters to overcome the influence of weak nonlinearity, so that these algorithms can be applied to weak nonlinear systems, and apply the existing research results of linear control theory to obtain better control results. Although most industrial processes can be modeled and controlled by local linearization methods near the working point, for some nonlinear controlled objects with special structures, it is difficult to obtain satisfactory control results with convent...

AI Technical Summary

Problems solved by technology

But for the predictive control of the neural network, there are many difficulties at present. The learning process of the artificial neural network is a very slow and long process, and the predictive control has a large amount of calculation due to the introduction of a multi-step prediction mechanism. The online learning process further increases the computational burden

Although multiple neural networks can be connected in series to obtain multi-step output prediction, this will increase the complexity of the controller and directly affect the solution of the control quantity

At the same time, if online model identification is required, online network training will take a lot of time, which will affect the real-time performance of control

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