A novel optimization algorithm based on epsilon constraint-RBF neural network for tuning PID controller in decoupled HVAC system

The energy efficiency of a heating, ventilating and air conditioning (HVAC) system optimized using a radial basis function neural network (RBFNN) combined with the epsilon constraint (EC) method is reported. The new method adopts the advanced algorithm of RBFNN for the HVAC system to estimate the residual errors, increase the control signal and reduce the error results. The objective of this study is to develop and simulate the EC-RBFNN for a self tuning PID controller for a decoupled bilinear HVAC system to control the temperature and relative humidity (RH) produced by the system. A case study indicates that the EC-RBFNN algorithm has a much better accuracy than optimization PID itself and PID-RBFNN, respectively.