深度确定性策略梯度与模糊PID的协同温度控制

Collaborative temperature control of deep deterministic policy gradient and fuzzy PID

针对现有温度控制系统控温时间长、误差大的问题,本文提出了一种基于深度确定性策略梯度(DDPG)和模糊自整定PID的协同温度控制.首先,模糊PID在控制大滞后系统时,控制器不能立刻对产生的干扰起抑制作用,且无法保证大滞后系统的稳定性等问题,本文建立了模糊PID和DDPG算法相结合的温度控制模型,该模型将模糊PID作为主控制器,DDPG算法作为辅助控制,利用双控制器模型实现温度协同控制.接着,利用遗传算法对模糊PID的隶属函数和模糊规则进行寻优,获得模型参数最优解.最后,在仿真实验中验证所提方法的有效性.仿真实验结果表明,本文提出的算法可有效减少噪声干扰,减小控制系统的响应时间、误差和超调量.

The existing temperature control system takes a long time to control the temperature and has large errors,aiming at which this paper proposes an algorithm of temperature collaborative control.The algorithm is based on deep deterministic policy gradient(DDPG)and fuzzy self-tuning PID.First of all,when fuzzy PID controls a large-lag system,the controller cannot suppress the disturbance immediately,and it cannot guarantee the stability of the large-lag system.In this paper,we establish a temperature control model combining fuzzy PID and DDPG.In this model,fuzzy PID is used as the main controller while DDPG is used as the auxiliary one,so that the dual-controller model can realize the goal of temperature cooperative control.Then,we use genetic algorithm to find the optimal solution of the membership function and fuzzy rules of fuzzy PID,by which we can obtain the optimal solution of model parameters.Finally,the effectiveness of our method is verified in simulation experiments.The results show that the algorithm proposed in this paper can effectively reduce the noise interference and the response time,errors and overshoot of the control system.