Assist as needed control strategy for upper limb rehabilitation robot in eating activity - Zainul Azlan, Norsinnira; Lukman, Nurul Syuhadah (International Islamic University Malaysia, 2020)
The slacking behaviour or lack of participation from impaired patients during robotic rehabilitation therapy is one of the factors that slows down the recovery speed. The implementation of Assist As Needed (AAN) control law in the robotic assisted rehabilitation treatment may alleviate this problem and encourage the patients to be actively involved in the rehabilitation exercises. This paper presents a new Assist As Needed control strategy for an upper limb rehabilitation robot in assisting subjects with various level of capabilities to regain their original upper limb’s functionality in realizing basic motions in eating activity. The controller consists of Proportional, Integral, Derivative (PID) controller in the feedback loop, with an adjustable gain K that varies according to the user’s level of capability. A Force Sensing Resistor (FSR) is used to identify the user’s upper extremity capability level. The controller regulates the necessary amount of assistance provided by the robot based on the information obtained from the sensor. The automatic adjustment of the robot’s assistance to the subjects leads them to put in their own effort in accomplishing the desired movements. The proposed control strategy is simple, easy to be programmed and mathematically less complicated. A prototype of the wearable upper limb rehabilitation robot has been built and a Graphical User Interface (GUI) has been developed using MATLAB software to facilitate the rehabilitation process and for progress monitoring. The simulation and experimental results have proven that the proposed control strategy is successful in regulating the necessary amount of robot assistance according to the patients’ level of capability. The proposed controller has effectively driven the upper limb rehabilitation robot to achieve the desired trajectory with zero steady state error, percentage overshoot less than 8% and settling time below 6 seconds, whilst providing the correct amount of robotic assistance in accordance to the subjects’ capability level.