RBFNN-Based Optimized PID Control for a 3-DOF Helicopter System: Design and Validation

This paper investigates the attitude control of a perturbated three degrees of freedom (3-DOF) helicopter. From the practical side, the 3-DOF helicopter is a laboratory testbed system that operates on the same principle of that of a real tandem-rotor helicopter. Inspired by the theory of meta-heuristic optimization a hybrid controller is proposed by skillfully combining a Gray Wolf Optimization (GWO) algorithm, a Radial Basis Function Neural Network (RBFNN), and a Proportional-Integral-Derivative (PID) controller. Given the nonlinear coupled dynamics of the system, the designed control structure allows for maintaining tracking performance and dealing with the nonlinearity problem. Furthermore, the GWO algorithm can usefully provide optimal gains whereas the RBFNN has self-tuning mechanism to achieve accurate control. The efficacy of the proposed controller is demonstrated with experimental tests performed on the Quanser 3-DOF helicopter laboratory setup. A comparative study is made involving the proposed PID-GWO-RBFNN strategy and two other controllers. The obtained results show that the suggested controller yields performance improvement regarding transient response and robustness.