基于粒子群算法的大棚温度自适应离散模糊控制系统

Adaptive Discrete Fuzzy Control System of Greenhouse Temperature Based on Particle Swarm Optimization

针对大棚温度调控存在明显的非线性滞后问题,提出一种基于自适应量化因子的离散模糊控制策略,其中量化因子通过粒子群算法自适应优化。首先,依据实验大棚的温度变化规律,对温度进行离散模糊处理,并基于误差建立模糊规则库,应用加权平均法解模糊,构建一种离线计算、在线查表的大棚温度离散论域模糊控制系统。然后,采用粒子群算法自适应优化控制器的输入量化因子和输出比例因子,从而在确保系统稳定的基础上提高系统控制精度。最后,针对某实际大棚模型的温度调节问题,利用所提出的控制策略,在Simulink中建模并进行比较仿真。仿真结果表明,粒子群优化算法能够显著提高系统性能,特别是温度达到稳态的时间缩短了46.3%。

A discrete fuzzy control strategy based on adaptive quantization factor is proposed for the obvious nonlinear lag problem of greenhouse temperature regulation,in which the quantization factor is adaptively optimized by particle swarm algorithm.According to the temperature change law of the experimental greenhouse,the discrete fuzzy processing of temperature is carried out,and the fuzzy rule base is established based on the error.The weighted average method is used to solve the fuzzy,and a discrete universe fuzzy control system of greenhouse temperature is constructed,which is offline calculation and online look-up table.Then,the particle swarm algorithm is used to adaptively optimize the input quantization factor and output scaling factor of the controller,thus improving the system control accuracy on the basis of ensuring system stability.Finally,the temperature regulation problem of an actual shed model is modeled in Simulink and simulated for comparison using the proposed control strategy.Simulation results show that the particle swarm optimization algorithm can significantly improve the system performance,especially the time to reach steady state at temperature is reduced by 46.3%.