In view of the traditional PID algorithm parameters to an optimal or near optimal identification is more difficult. A kind of quantum particle swarm optimization ( QPSO) algorithm for PID parameters put forward , and squared error moments of the integral function is used to the fitness criterion, in order to overcome the genetic algorithm (GA) to optimize the efficiency is not high, the local search ability is weaker. Using servo motor mathematical model a simulation is made , the results show that the quantum particle swarm optimization of PID parameters is fast algorithm and avoids premature defects, and show the effectiveness of the proposed algorithm and the superiority of the designed controller.%针对传统PID算法参数最优或接近最优确定较为困难,提出一种量子粒子群(QPSO)优化PID参数的算法.并用平方误差矩积分函数作为适应度判据,以克服PID算法自适应能力较差及遗传算法(GA)优化效率不高,其局部搜索能力较弱的缺陷.并使用伺服电动机数学模型进行仿真,结果表明量子粒子群优化PID参数速度快,避免早熟缺陷,同时表明了所提出算法的有效性和所设计控制器的优越性.
展开▼
