Mian Ashfaq Ali

Adaptive impedance control for upper limb assist exoskeleton

Need to develop human bodys posture supervised robots, gave the push to researchers to think over dexterous design of exoskeleton robots. It requires to develop quantitative techniques to assess motor function and generate the command for the robots to act accordingly with complex human structure. In this paper, we present a new technique for the upper limb power exoskeleton robot in which load is gripped by the human subject and not by the robot while the robot assists. Main challenge is to find non-biological signal based human desired motion intention to assist as needed. For this purpose, we used newly developed Muscle Circumference Sensor (MCS) instead of electromyogram (EMG) sensors. MCS together with the force sensors is used to estimate the human interactive force from which desired human motion is extracted using adaptive Radial Basis Function Neural Network (RBFNN). Developed Upper limb power exoskeleton has seven degrees of freedom (DOF) in which five DOF are passive while two are active. Active joints include shoulder and elbow in Sagittal plane while abduction and adduction motion in shoulder joint is provided by the passive joints. To ensure high quality performance model reference based adaptive impedance controller is employed. Exoskeleton performance is evaluated experimentally by a neurologically intact subject which validates the effectiveness.

A study on the improvement of the tire force distribution method for rear wheel drive electric vehicle with in-wheel motor

This paper propose the tire force distribution method for a rear wheel drive electric vehicle. This method is developed to improve the vehicle stability under the high speed cornering condition and save the electric energy. To control the lateral vehicle motion, the desired yaw moment is calculated by yaw rate control using PID theory. And Total desired longitudinal force is also determined by acceleration pedal signal. The tire force distribution method calculates the desired longitudinal tire forces at left and right wheel using the desired yaw moment and total desired longitudinal force and Maximum longitudinal tire force. And Maximum longitudinal tire force is determined by tire friction circle theory. The proposed method is verified by the simulation using CarSim software. And it is found from simulation results that the proposed method provides improvement vehicle stability and saving the electric energy under the high speed cornering condition.

Passivity Based Adaptive Control and Its Optimization for Upper Limb Assist Exoskeleton Robot

The need for human body posture robots has led researchers to develop dexterous design of exoskeleton robots. Quantitative techniques to assess human motor function and generate commands for robots were required to be developed. In this paper, we present a passivity based adaptive control algorithm for upper limb assist exoskeleton. The proposed algorithm can adapt to different subject parameters and provide efficient response against the biomechanical variations caused by subject variations. Furthermore, we have employed the Particle Swarm Optimization technique to tune the controller gains. Efficacy of the proposed algorithm method is experimentally demonstrated using a seven degree of freedom upper limb assist exoskeleton robot. The proposed algorithm was found to estimate the desired motion and assist accordingly. This algorithm in conjunction with an upper limb assist exoskeleton robot may be very useful for elderly people to perform daily tasks.