Dynamic modelling and control of a twin-rotor system using adaptive neuro-fuzzy inference system techniques

This article presents an online non-linear dynamic modelling and control approach based on adaptive neuro-fuzzy inference system (ANFIS) for a twin-rotor multi-input multi-output system (TRMS), in the vertical plane motion. The TRMS can be considered as a flexible aerodynamic test rig that resembles the behaviour of a helicopter. The TRMS and similar manoeuvring systems are often subjected to random disturbances arising from various sources such as driving motors and external environmental sources. For such highly non-linear systems with varying operating conditions, adaptive control approaches are suitable tools to cope with plant uncertainties. An inverse-model control of the TRMS is developed using online ANFIS learning algorithm. The consequent and antecedent parameters of a Takagi–Sugeno fuzzy inference system are optimized online using recursive least squares and gradient descent algorithms, respectively. In order to reduce the computation complexity, the training process is minimized based on global system error tolerance. The optimal initialization of the ANFIS parameters is achieved through an off-line training process. The developed strategy is compared to other control laws in terms of tracking performance, disturbance rejection, and response to external noise. The obtained simulation results demonstrate the efficiency of the online inverse control scheme.