The Flight Control System Based On Multivariable PID Neural Network For Small-Scale Unmanned Helicopter

How to design the flight control system (FCS) of small-scale unmanned helicopter is still a difficult challenge today. In this paper, one novel control approach based on Multivariable PID Neural Network (MPIDNN) was firstly used to design the FCS of small-scale unmanned helicopter. MPIDNN is suitable for controlling the multi-input multi-output (MIMO), nonlinear, highly coupled, uncertain and dynamic system such as helicopter. The training algorithm based on target function and MPIDNN forwards algorithm was designed in this control system. The sensor module, embedded control board and communication module was designed to provide an operational hardware platform for the control system. The result of simulation indicates that the training algorithm can solve the offline training and study problem of small-scale unmanned helicopter. The forwards algorithm can control the flight of helicopter well and its maximum magnitude of error is about 1%. Simulation shows that the performance of our control approach is perfect.