基于遗传因子的自适应蚁群算法最优PID控制

蚁群算法是一种新型的模拟进化算法,重点始于组合优化问题的求解.作者运用该算法优化PID控制参数,但在基本蚁群算法中,存在收敛速度较慢,易出现停滞,以及全局搜索能力较低的缺陷.论文提出了一种具有遗传因子的自适应蚁群算法最优PID控制参数的方法,设计出参数优化图.该方法克服了基本蚁群算法的不足,能够满意地实现PID控制参数优化.仿真结果与Z-N法、遗传算法、基本蚁群算法相比较,优化效果明显得到改善.实验表明,该方法对于控制其他对象和过程也具有应用价值.

Optimal PID Control of Self-Adapted Ant Colony Algorithm Based on Genetic Gene

Ant colony algorithm is a brand-new type of simulative evolution algorithm, which focus on its solution to conform optimized question. The author utilizes this algorithm to optimize PID control parameter, but in basic ant colony algorithm, there are some defects of slow convergence speed, easy to get stagnate, and low ability of full search. This paper presents a method of optimized PID control of self-adapted ant colony algorithm based on genetic gene and design out the parameter optimized diagram. This method not only overcomes the shortage of basic ant colony algorithm, but also perfectly realizes the optimization of PID control parameter. Compared to the result of simulation with Z- N optimization, genetic algorithm and basic ant colony algorithm, results of optimization can be greatly improved. The experiments show that this method has its practical value on controlling other objection and process.