基于改进正弦- soa算法的PID控制器参数优化

Ma You, Yanjuan Wu, Yunliang Wang, Xiyang Xie, Chen Xu
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引用次数: 3

摘要

针对传统PID控制器性能不理想、参数不能调整到最佳状态、控制系统不能达到良好控制效果的问题,提出了一种基于改进正弦混沌映射的改进海鸥优化算法(SOA)对PID控制器参数进行优化。采用正弦映射策略,使初始海鸥种群均匀分布在搜索空间中,改善海鸥优化算法求解精度低、收敛速度慢、容易陷入过早收敛的缺点,提高算法的收敛速度和收敛精度。对8个标准测试函数进行了测试,将改进的海鸥优化算法与未改进的海鸥算法、粒子群优化算法(PSO)、甲虫天线搜索算法(BAS)、粒子群优化-甲虫天线搜索算法(PSO-BAS)和导引头优化算法(TSOA)进行了比较,验证了改进的海鸥优化算法具有更好的优化效果。将改进算法应用于二阶系统和双闭环直流电机调速系统,对PID控制器参数进行优化。结果表明,该算法具有精度高、原理简单、收敛精度好、收敛速度快等优点。
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Parameter Optimization of PID Controller Based on Improved Sine-SOA Algorithm
Aiming at the problem that the traditional PID controller was not ideal, the parameters could not be adjusted to the best state, and the control system could not achieve good control effect, an improved seagull optimization algorithm (SOA) based on improved Sine chaotic mapping was proposed to optimize the parameters of PID controller. Sine mapping strategy was adopted to make the initial seagull population evenly distributed in the search space, to improve the shortcomings of the seagull optimization algorithm, such as low solution accuracy, slow convergence speed and easy to fall into premature convergence, and improve the convergence speed and convergence accuracy of the algorithm. Eight standard test functions were tested, and the improved gull optimization algorithm was compared with the unimproved gull algorithm, particle swarm optimization algorithm (PSO), beetle antennae search algorithm (BAS), particle swarm optimization -beetle antennae search algorithm (PSO-BAS) and the seeker optimization algorithm (TSOA), to verify that the improved gull optimization algorithm has better optimization effect. The improved algorithm is applied to a second-order system and double closed-loop DC motor speed regulation system to optimize the parameters of PID controller. The results show that the algorithm has high precision, simple principle, better convergence precision and faster convergence speed.
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