Composite PID (Proportion Integration Differentiation) neural network control method based on nonlinear dynamic factor

Abstract

The invention discloses a composite PID (Proportion Integration Differentiation) neural network control method based on a nonlinear dynamic factor. In the method, creative improved research is carried out on the basis of stabilizing a PID controller based on a dynamic nonlinear factor by adopting a Ziegler-Nichols method abroad, and the defect of learning a mathematical model of a controlled object by confirming a PID gain parameter through utilizing a root locus method according to the Ziegler-Nichols method is effectively overcome. The invention aims to recombine three gain parameters and three factors in the prior art abroad to obtain six weight factors so as to obtain a composite PID neural network control law based on the nonlinear dynamic factor, which has six neural network weight factors, constructs a neural network model according to the nonlinear control law, and trains the weight factors in the composite PID controller based on the nonlinear dynamic factor on line in real time by using a neural network method to realize intelligent control on a nonlinear system. The invention can rapidly and accurately control a nonlinear object and has high robustness.

Description

technical field [0001] The invention belongs to the field of automatic control, and relates to an intelligent control method for online self-stabilization of parameters based on dynamic factor-based nonlinear composite PID operation unit integrated into hidden layer neurons. Background technique [0002] Proportional, integral, and differential (P, I, D) control according to the deviation is the control method with the longest history and the strongest vitality. Although advanced control strategies are gradually being promoted today, more than 90% of the control loops currently in operation are still PID controllers. However, with the increase of the complexity of the system and the increase of the uncertain factors of the object, the traditional linear PID control is no longer applicable, but the nonlinear PID control can truly reflect the nonlinear relationship between the control amount and the deviation signal. To a certain extent, it overcomes the shortcomings of the l...

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Problems solved by technology

However, on the one hand, this method involves two-layer neural network weight adjustment, and there is a phenomenon of weight coupling, so the algorithm converges slowly and requires a large amount of calculation, which is not conducive to the real-time control of the fast sampling system; on the other hand, this method only uses linear PID The computing unit is integrated into the hidden layer neurons of the neural network after clipping processing, and its nonlinear control ability is not strong

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