Self-Tuning Neural Network Controller Based on Fuzzy Logic for Multiple Positions Tracking of a Pneumatic Driven Soft Endoscope Actuator

This paper presents the dynamic modelling and end effector position control of a soft endoscope. Soft endoscope system under study consists mainly of a pneumatic driven soft actuator (PDSA) with four independently chambers. Recurrent neural network (RNN) and dynamic back propagation (DBP) training algorithm are used to obtain PDSA dynamic model. PDSA position controller is based on a feedforward neural network (FNN) and it is trained using PDSA closed loop system (CLS) and DBP training algorithm. CLS stability analysis concludes that it is not possible control PDSA end effector position using one position controller for different end effector initial and desired positions. Fuzzy logic methodology is used to integrate several positions controllers in a one controller valid for all operation range. The controller implementation and close loop system simulation are performed in MATLAB for different end effector desired position to validate system performance in all range of operation.