A neural network assisted cascade control system for air handling unit

In the centralized heating, ventilating and air-conditioning (HVAC) system, air handling units (AHUs) are traditionally controlled by single-loop proportional-integral-derivative (PID) controllers. The control structure is simple, but the performance is usually not satisfactory. In this paper, we propose a cascade control strategy for temperature control of AHU. Instead of a fixed PID controller in the classical cascade control scheme, a neural network (NN) controller is used in the outer control loop. This approach not only overcomes the tedious tuning procedure for the inner and outer loop PID parameters of a classical cascade control system, but also makes the whole control system be adaptive and robust. The multilayer NN is trained online by a special training algorithm-simultaneous perturbation stochastic approximation (SPSA)-based training algorithm. With the SPSA-based training algorithm, the weight convergence of the NN and stability of the control system is guaranteed. The novel cascade control system has been implemented on an experimental HVAC system. Testing results demonstrate the effectiveness of the proposed algorithm over the classical cascade control system.