Chen Xuzhi

Helicopter engine performance prediction based on cascade-forward process neural network

In view of the difficulty of predicting engine performance effectively in traditional methods, a prediction method based on CFPNN (Cascade-Forward Process Neural Network) is proposed. By introducing a set of appropriate orthogonal basis functions into the input space, the input functions and weight functions are expanded. The time aggregation operation of the process neurons is simplified by this way. The RBP (Resilient Back-Propagation) learning algorithm based on orthogonal basis function expansion is proposed. The CFPNN based on RBP learning algorithm is compared with FFPNN (Feed-Forward Process Neural Network) based on RBP learning algorithm and CFPNN based on ABP (Adaptive Back-Propagation) learning algorithm respectively. The results show that the CFPNN based on RBP learning algorithm possesses good convergence and high accuracy. It provides an effective way for helicopter engine performance prediction.

Dual-redundancy smart rotating speed sensor design for unmanned helicopter

For the complex condition of unmanned helicopter and its high requirements of reliability, a dual-redundancy smart rotating speed sensor is proposed using hardware and software redundancy. It is composed of two MCUs(Micro Control Units), dual-port RAM(Random Access Memory), pulse signal generator circuitry, logic circuitry, CAN(Controller Area Network) bus interface and so on, and can be applied to distributed control system of unmanned helicopter. The sensors reliability is improved by two channels which detect and backup each other. The experiment indicates that the reliability and real-time response of the dual-redundancy smart rotating speed sensor is satisfactory and the relative error of rotating speed measurement is less than 0.5%. The dual-redundancy smart rotating speed sensor can switch exactly and send alarm signals when there are malfunctions.