Design and Analysis of Fuzzy Based Proportional-Integral-Derivative Controller for Elbow-Forearm Rehabilitation Robot

Abstract

Nowadays, the use of Rehabilitation Robots for stroke patients has been growing rapidly. However, there was a limited scope of using such Rehabilitation Robots for patients suffer from an accidental physical fracture. Since the pain condition of such accidents needs a critical treatment, precise control of such robotic manipulators is mandatory. This paper presents the design and control of the Elbow-Forearm Rehabilitation Robot by considering the pain level of the patient. This design consists of the mechatronic design processes including mechanical design, controller design, and Virtual prototyping using ADAMS-MATLAB Co-simulation. The pain level is estimated using three parameters i.e the patient general condition, the muscle strain, and the duration of exercise from the beginning of rehabilitation. Based on these three input parameters, the manipulator's desired range of motion has been determined using the Fuzzy Logic System. The output of this fuzzy logic system would be an input to the main control system. ADAMS-MATLAB Cosimulation is carried out based on three reference inputs i.e Step, sinusoidal and the proposed fuzzy reference input. Using step input, we have discussed the step response characteristics of the developed system. The Co-simulation of the ADAMS dynamic model is realized with a 30 degree oscillating motion by providing a sinusoidal input. Finally, using the developed fuzzy reference input, we have done a Co-simulation of ADAMS plant. The simulation result demonstrates that the proposed PID controller with gains Kp=0.001 and Ki=0.01 yields 99.6% of accuracy in the tracking of the reference input as compared to the simulation without introducing controller which has an accuracy of 94.9%. The simulation also shows that derivative gain (Kd) of the PID controller has no effect on the system so that it is over damping system. From the above three simulation schemes, we can conclude that the Elbow-Forearm rehabilitation robot could be controlled as per the desired signal. Since this desired signal is developed from the pain level of the patient, we can say that the system is controlled as per the pain level of the patient.

Research on fuzzy impedance control of upper-limb rehabilitation robot based on membership functions online optimization learning strategy

“…Although the above fuzzy control methods can assist the impaired limb to complete the rehabilitation training, the optimal impedance parameters need to be selected to further improve the tracking ability of fuzzy impedance control system and the rehabilitation effect. Addisie et al (2020) observed that the robot manipulator needs to be precisely controlled during the treatment of stroke patients, and designed a rehabilitation robot based on fuzzy PID control, so that the rehabilitation robot can drive the patient's elbow and forearm according to the patient's pain level. Modares et al (2016) proposed a novel control strategy that uses machine learning methods to find the optimal impedance parameters and minimize the tracking error of the impedance control system.…”

“…Addisie et al (2020) observed that the robot manipulator needs to be precisely controlled during the treatment of stroke patients, and designed a rehabilitation robot based on fuzzy PID control, so that the rehabilitation robot can drive the patient’s elbow and forearm according to the patient’s pain level. Modares et al (2016) proposed a novel control strategy that uses machine learning methods to find the optimal impedance parameters and minimize the tracking error of the impedance control system.…”