Force control using predictive functional control (PFC) algorithm for two chambers soft actuator

Abstract

The current trend in the world of automation and robotics heavily applies metal structure type of the actuators which are heavy, rigid, difficult and expensive to develop. Other areas of applications like medical, agriculture, biological and welfare requires less rigid and safer robots. Thus, a biologically inspired robot is required to meet this certain criteria. This lead to the recent attraction in the study and development of Pneumatic Soft Actuator (PSA) because it has more advantages over hard actuators to suit the applications mentioned above due to its simple structure, low cost, high efficiency, high compliance, high power to weight ratio and ensures safe and more natural way of interaction. Despite the advantages of pneumatic soft actuator it has nonlinearity and hysteresis, which makes them difficult to model and control. The main objective of this study was to obtain mathematical model and control the force of a two chamber pneumatic soft actuator. Obtaining nonlinear mathematical model accurately to be used in controller design needs to determine all physical parameters of the real system which is very expensive and time consuming, to simplifying this procedure, model of system was analysed and obtained using system identification toolbox in MATLAB software. Input and output data was acquired from an experimental setup which was used to obtain a transfer function force model of the system. The best model was accepted based on the best fit criterion through SI toolbox. Predictive Functional Controller (PFC) was designed and simulated for the model via MATLAB/Simulink. The results showed that PFC controller provides better output than a conventional PID controller when tested using several references. PFC controller exhibits faster response to the system with desired transient error. The study represented in this project can be further broaden by taken position control into account and validation of the simulated control can be done on the experimental setup.