Recent development and implementation on intelligent control system has lead to the development of virtual sensing system technology. Virtual sensing system allows immeasureable state variables to be accurately predicted, which is very beneficial to reduce the amount of sensors required to monitor and control a system, especially for the case of controlling a quadrator. This paper proposes a novel technique to design a PID control using virtual sensing system, consisting of Diagonal Recurrent Neural Network (DRNN) and Extended Kalman Filter (EKF), which predicts the immeasureable states of the quadrator system based on the current states and control inputs. A bio-inspired optimization technique, Particle Swarm Optimization (PSO), is proposed to be applied in DRNN to avoid any possibilities from local extreme condition. Further, a PSO based PID position controller is also developed to be integrated with the designed virtual sensing system to control a quadrator.
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