# Investigation of 2 DOF PID Controller for Physio-Therapeutic Application for Elbow Rehabilitation  
[PERSON]  
1 Mechatronics Engineering, International Islamic University Malaysia, Kula Lumpur 43200, Malaysia; mahbub@6 gmail.com (M.R. ); [EMAIL_ADDRESS] (S.M.) [PERSON]  
2 School of Engineering, RMIT University, Melbourne, VIC 3000, Australia; [EMAIL_ADDRESS] 2  
[PERSON]  
1 Mechatronics Engineering, International Islamic University Malaysia, Kula Lumpur 43200, Malaysia; mahbub@6 gmail.com (M.R. ); [EMAIL_ADDRESS] (S.M.) [PERSON]  
1 Mechatronics Engineering, International Islamic University Malaysia, Kula Lumpur 43200, Malaysia; mahbub@6 gmail.com (M.R. ); [EMAIL_ADDRESS] (S.M.) [PERSON]  
3 School of Industrial & System Engineering, University of Oklahoma, Norman, OK 73019, USA 3,4,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,1  
control the exoskeletons, a suitable controller is an inevitable component of the system which ensures a fast and perfect response for the wearer. The literatures exhibit several types of controllers, such as linear, nonlinear, learning based controllers, motion based bilateral rehabilitation and EMG signal-based models [25; 26; 27]. [PERSON] et al. introduces a physio-therapeutic robot, ETS-MARSE, in order to overcome the impairment of the upper extremity that a nonlinear controller controls, such as the sliding mode control [28]. [PERSON] et al. consider a linear, model-free PID-based impedance controller intended to operate another exoskeleton robot, EXO-UL7, where the parameters are chosen based on human impedance properties [29]. [PERSON] et al. propose a learning-based controller, electromyogram (EMG) based neural network control approach to control, with high accuracy in the motion of the upper limb exoskeleton in response to the wearer's motion intention [30]. [PERSON] et al. [31] present an analysis of machine learning-based assessments for elbow spasticity. [PERSON] et al. [32] investigate an adaptive fast terminal sliding mode controller for an exercise-assisted robotic arm intended to assist in elbow joint rehabilitation. Additionally, in previous research, it has been established that higher control levels, such as learning motion primitives (behaviors) and optimizing them, may need to be achieved in order to acquire human-like movements in mechanical limbs operated by patients [33; 34]. While there are many advanced methods available, are some limitations of the PID controller remain, such as a long settling time, steady state error, amplifying high frequency noise and narrower range of stability. Keeping this in mind, the 2 DOF PID controller is investigated in this study in order to examine the performance of the system for its flexion and extension movements with the controller. This work exhibits a 2 DOF PID controller to control an upper limb exoskeleton system that can offer physiotherapy to patients who suffer from elbow impairment. This controller is different from regular PID controllers because it introduces a set point weighting into the algorithm. As a result, this controller is able to reject disturbance from the system at a significantly fast rate [35]. This, in particular, is one of the most important objectives of a controller, other than ensuring stability in the exoskeleton during such an application. In addition, this controller is simple to design, easy to implement, and fast in ensuring the stability of a system. In contrast, nonlinear controllers are sophisticated in design but are sometimes difficult to apply in hardware. Hence, PID controllers are used in more than 90% of industrial applications nowadays [36]. This study is the continuation of earlier research, [37] which investigated a system controlled by a LQG controller. In that work, the black box model approach was taken into consideration in order to generate the system's transfer function.