地铁站台空调系统非线性预测控制策略

地铁站台空调系统回路众多且具有强耦合和非线性特性,PID控制方法参数整定困难,无法兼顾乘客舒适性和能效最优,由于系统建模困难,非线性优化算法计算量大,智能控制方法难以实现工程应用.对此,提出一种地铁站台空调系统预测控制策略.首先,根据热湿负荷平衡和能量守恒定律建立地铁站台热动态特性预测模型;然后,将满足乘客舒适性并节省能耗作为系统优化目标,使用神经网络作为优化反馈控制器,将系统优化目标函数作为控制器优化性能指标,结合变分法和随机梯度下降法,对神经网络控制器的权值和阈值进行在线滚动优化,算法计算量小,占用存储空间适中.仿真实验结果表明,所提出的预测控制策略与传统PID控制方法相比,在满足乘客舒适性要求的前提下,系统响应时间可缩短约39.6%,末端风机能耗降低约73.39%.

Nonlinear predictive control for subway station air conditioning systems

There are many loops in the subway station air-conditioning system, and it has strong coupling and nonlinear characteristics, resulting in difficulties to adjust the parameters of the PID controller. And it is impossible for PID to consider both passenger comfort and energy saving. In addition, due to the difficulty of system modeling and the huge computational effort of nonlinear optimization algorithms, it is difficult to realize the engineering application of intelligent control methods. Therefore, a model predictive control strategy is established for the subway platform air conditioning system to improve passengers comfort and achieve energy efficiency. Firstly, based on the heat and humidity load balance and the law of energy conservation, a prediction model for the thermal dynamic characteristics of the subway platform is developed. And then, an artificial neural network(ANN)-based predictive controller is designed, in which the weights and thresholds of the ANN controller are optimized online using the Lagrange-variational-based gradient descent training algorithm, to minimize the system cost function with small computational and storage needs. Simulation results show that the proposed predictive control strategy can shorten the system response time by 39.6% and reduce the energy consumption of the end fan by 73.39% compared with the PID control on the premise of meeting passenger comfort requirements.