Neurological disorders such as cerebral paralysis, spinal cord injuries, and strokes, result in the impairment of motor control and induce functional difficulties to human beings like walking, standing, etc. Physical injuries due to accidents and muscular weaknesses caused by aging affect people and can cause them to lose their ability to perform daily routine functions. In order to help people recover or improve their dysfunctional activities and quality of life after accidents or strokes, assistive devices like exoskeletons and orthoses are developed. Control strategies for control of exoskeletons are developed with the desired intention of improving the quality of treatment. Amongst recent control strategies used for rehabilitation robots, active disturbance rejection control (ADRC) strategy is a systematic way out from a robust control paradox with possibilities and promises. In this modern era, we always try to find the solution in order to have minimum resources and maximum output, and in robotics-control, to approach the same condition observer-based control strategies is an added advantage where it uses a state estimation method which reduces the requirement of sensors that is used for measuring every state. This paper introduces improved active disturbance rejection control (I-ADRC) controllers as a combination of linear extended state observer (LESO), tracking differentiator (TD), and nonlinear state error feedback (NLSEF). The proposed controllers were evaluated through simulation by investigating the sagittal plane gait trajectory tracking performance of two degrees of freedom, Lower Limb Robotic Rehabilitation Exoskeleton (LLRRE). This multiple input multiple output (MIMO) LLRRE has two joints, one at the hip and other at the knee. In the simulation study, the proposed controllers show reduced trajectory tracking error, elimination of random, constant, and harmonic disturbances, robustness against parameter variations, and under the influence of noise, with improvement in performance indices, indicates its enhanced tracking performance. These promising simulation results would be validated experimentally in the next phase of research.
Manual harvesting is still prevalent in the agricultural industry. Accordingly, it is one of the largest contributors toward work-related musculoskeletal disorder. The cutting task in oil palm harvesting uses a long pole and involves repetitive and forceful motion of the upper limbs. Exoskeleton technology is increasingly explored to assist manual tasks performance in manufacturing and heavy industries, mainly for reducing discomfort and injuries, and improving productivity. This paper reports an initial investigation on the feasibility of using an upper limb exoskeleton to assist oil palm harvesting tasks. Previous studies highlighted that exoskeletons for agricultural activities should be adaptable to changing field tasks, tools and equipment. The immediate difference in the activity of three muscles were analyzed for a range of harvesting-simulated tasks. Lower activities were observed for tasks involving overhead work when using the prototype. Nevertheless, users' feedback highlighted that its design should be optimized for better acceptance.