PID controller parameter setting algorithm based on improved PSO (particle swarm optimization) algorithm

Abstract

The invention discloses a PID controller parameter setting algorithm based on an improved PSO (particle swarm optimization) algorithm, and the algorithm comprises the following steps: 1, initializing the algorithm parameters; 2, switching to an iterative loop, and carrying out the updating of the position and speed of each particle; 3, randomly searching a new position in the neighborhood of a current position; 4, calculating the adaptability difference between two positions, and judging whether to accept the new position or not through a simulated annealing mechanism when the adaptability of the new position is inferior to the adaptability of an original position but is superior to the adaptability of a global optimal position; 5, updating the global optimal position of a population, carrying out the natural selection operation, carrying out the arrangement of all particles according to the adaptability values, and employing the information of a part of better particles to replace the information of the other half particles; 6, judging whether to stop the iteration or not; 7, outputting PID controller parameters or executing step 2 again. The method can achieve the automatic setting of control parameters, irons out a defect that a conventional PSO algorithm is very liable to be caught in local optimization, achieves the complementation of the simulated annealing operation and a natural selection strategy, improves the convergence precision of the algorithm under the condition that the number of convergence times of the algorithm is guaranteed, is higher in robustness and precision, and enables the PID controller to generate a more excellent control effect.

Description

technical field [0001] The patent of the present invention relates to the field of intelligent algorithms, and relates to PID control based on optimization algorithms. Background technique [0002] In industrial control, PID is a commonly used regulator control method. The performance of the PID controller mainly depends on the optimization of the three parameters of the controller. Different control parameters have different effects on the control system. The optimization of PID controller parameters has always been Research hotspots in the field of automatic control. [0003] Particle swarm optimization algorithm (PSO) is an evolutionary computing technology, which is an optimization algorithm developed by imitating biological activities. Its basic idea is to find the optimal solution through coordination and information sharing among individuals in the group. It has been widely used in function optimization, neural network training, fuzzy system control and PID controll...

AI Technical Summary

Problems solved by technology

The core idea of ​​the PSO algorithm is that a single particle gathers to its own historical best position and the best historical position of the group to form a rapid convergence effect of the particle population, but it is easy to fall into local extremum, premature convergence or stagnation, etc. These shortcomings are in PSO optimization. PID controller parameters will lead to premature maturity, and the optimization result will fall into local optimum, which will eventually lead to a large error in the control effect of the PID controller.

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