摘要
针对现行的智能PID控制算法参数在线整定实现比较复杂、计算量大等问题,提出一种对PID控制器参数进行离线自整定(采用带有目标性初始化粒子群的改进粒子群优化算法进行PID参数整定,即PSOOI)和在线优化(引入基于参考轨迹的虚拟误差进行控制量调整)的控制算法(OOPID)。通过引入信息熵对初始化粒子种群进行调整以提高粒子群算法(PSO)的全局搜索能力和收敛速度。通过引入虚拟误差对控制量进行在线调节提高系统动态性能和稳态性能,且计算量少易于实现。针对典型对象进行PID控制参数自整定,研究结果表明所提出的OO-PID控制算法具有全局优化能力,易于在线实现等优点。
Aiming at the problem that online self-tuning of intelligent PID controller parameters is very complex and large computing,an improved PID control algorithm（OO-PID）is proposed to tune the PID controller parameters,which an improved particle swarm optimization（PSO）based on the objective initialization of particle swarm（PSOOI）is proposed to tune PID parameters offline and an online optimization algorithm of PID control parameters based on virtual error is proposed to tune the control variable.Information entropy is adopted to tune the initialization of particle swarm in order to enhance the convergence speed and global search of PSO.Virtual error is adopted to tune the control variable in order to enhance static and dynamic performance,reduce caculation time and realize easily.It is demonstrated by numerical simulations on the classical objects to tune the parameters of the PID controller that the processed OOPID algorithm has the excellent global optimization performance and the easy online implementation.
出处
《电子测试》
2014年第3期27-32,共6页
Electronic Test
关键词
粒子群优化算法
PID控制
自整定
信息熵
虚拟误差
Particle swarm optimization
PID controller
Self-tuning
Information entropy
Virtual error
