PID parameter setting method based on self-adaptation ant colony genetic hybrid algorithm

Abstract

A PID parameter setting method based on self-adaptation ant colony genetic hybrid algorithm includes the following steps: 1) calculating PID parameter values; 2) assuming that the number of ant colony is m, each ant is k, and k=1-3; 3) conducting mixed algorithm parameter initialization and placing all ants at a starting point 0; 4) calculating the probability of the ants to transfer to each node; 5) updating local pheromone after each ant walks through a node; 6) assuming the variable that i=i+1, if i<=15, transfer to step 3); otherwise, transfer to step 6); 7) calculating corresponding PID parameters according to an array and calculating a target function that the ants correspond to; 8) updating overall pheromone and adjusting overall information volatile coefficient in a self-adaptation mode; 9) performing hybridization bounded variable hybridization to generate new individuals; 10) recalculating parameter values; 11) if NC<NCmax, and the whole ant colony is not restrained to walk in the same route, placing all the ants at the starting point 0 again and transfer to step 4); otherwise, finishing the circulation and outputting the optimum route and the corresponding optimum PID parameters.

Description

technical field [0001] The invention belongs to the technical field of optimization and control of a hydraulic drive system of a rotary impact rock bolter, and in particular relates to a PID parameter setting method based on an adaptive ant colony genetic hybrid algorithm. Background technique [0002] In the late 1930s, with the rapid development of modern technology, the industrial automatic control system put forward requirements for the rapidity, accuracy and stability of the system control, so PID control was born [79]. PID controller (Proportion Integration Differentiation. Proportional-integral-differential controller) is a controller composed of proportional unit P (Proportion), integral unit I (Integration) and differential unit D (Differentiation) based on the PID control principle[ 79]. The principle is to use the proportion, integral and differential of the deviation to form a control quantity through a linear combination, and use this control quantity to contro...

AI Technical Summary

Problems solved by technology

However, the initial solution speed is slow. When it is applied to optimize PID parameters, the algorithm lacks pheromone at the beginning, which leads to slow algorithm speed; relevant parameters, such as the selection of the volatility coefficient 1-ρ of the pheromone trajectory, will affect the global search ability of the algorithm. and convergence speed

The genetic algorithm has fast and random global convergence ability, but it cannot make good use of the feedback information of the system, and it is easy to cause repeated iterations and reduce the solution efficiency

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