New system identification model for predictive functional control with observer for an intelligent pneumatic actuator

This thesis presents System Identification (SI) model development and controller design using Predictive Functional Control with Observer (PFC-O) algorithm for realtime control of Intelligent Pneumatic Actuator (IPA). An application of Ankle-Foot Rehabilitation Exerciser (AFRE) device uses the IPA system. The plant mathematical model in discrete transfer functions was approximated using the MATLAB system identification toolbox for open-loop input-output experimental data. The SI process was conducted through a series of activities including observation and data gathering, Auto Regressive with Exogenous Input (ARX) model structure selection, model estimation, model validation and the implementation of PFC-O algorithm designed to prove the operation of IPA is acceptable. PFC-O algorithm was selected as a new control strategy for IPA to overcome the real-time nonlinearities and uncertain characteristics. PFC-O algorithm was used for position control, force control and realized compliance control for stiffness characteristic through MODBUS communication protocol. Performance assessment of the controller was programmed into MATLAB and validated through two real-time experiments: Personal Computer (PC) based (using National Instrument (NI) devices) and embedded based (using Programmable System on Chip (PSoC) microcontroller). The results between simulation, theoretical calculation and both realtime experiment matched closely and achieved the control objectives. Towards the AFRE application, the IPA can be configured through MATLAB Graphical User Interface (GUI) via personal computer where user can adjust the required Range of Motion (ROM) and resistance in real-time. The AFRE system testing was conducted successfully on selected subjects for various ROM and resistance using the proposed algorithm. The significant finding demonstrates that the new PFC-O control algorithm reduces the control effort and gives better performance in terms of tracking accuracy as compared to the existing control algorithm.