One of the most common problems in humans is a muscle fatigue. Exoskeletons are known as one of the solution to deal with human muscle fatigue. However, several issues related to the development of exoskeletons for such a case have been identified. One of these is the control mechanism. Thus, the objective of this paper is to investigate development of a control strategy for the upper-limb exoskeleton. In this paper, a new control mechanism for an upper-limb exoskeleton is proposed. A fuzzy-based PD controller and PID are used in the proposed control mechanism, and a comparative assessment of the performance of both controllers is made. The results show that the control mechanism with fuzzy-based PD controller performs better than the PID controller in terms of trajectory tracking accuracy and control torque analysis.

This paper presents investigations for development of an assistive exoskeleton device for elderly mobility. This exoskeleton is designed to enhance the lower limb and provide support torque in order to augment the torque of knee and hip during the walking cycle. PID Control is designed and implemented in this work. Due to the complexity in identifying the lower limb musculoskeletal system with traditional mathematical approaches, the visual Nastran 4D software is used for development of simulation model of the exoskeleton and a humanoid. Simulation results demonstrating the performance of the adopted approach are presented and discussed.

No abstract

In Malaysia, banana is a top fruit production which contribute to the economy growth in agriculture field. Hence, it is significant to have a quality production of banana and important to detect the plant diseases at the early stage. There are many types of banana leaf diseases such as Banana Mosaic, Black Sigatoka and Yellow Sigatoka. These three diseases are related to color changes at banana. This research paper is an experiment based and need to identify the best color feature extraction method to classify banana leaf diseases. Total of 48 banana leaf images that are used in this research paper. Four types of color feature extraction methods which are color histogram, color moment, hue, saturation, and value (HSV) histogram and color auto correlogram are experimented to determine the best method for banana leaf diseases classification. While for the classifiers, support vector machine (SVM) and k-Nearest neighbors (k-NN) are used to evaluate the performance and accuracy of each color feature extraction methods. There are also preliminary experiments to identify accurate parameters to use during classification for both classifiers. Our experimental result express that HSV histogram is the best method to classify banana leaf diseases with 83.33% of accuracy and SVM classifier perform better compared to k-NN.

One of the most common issues to human is fatigue. A technology known as exoskeleton has been identified as one of the solutions to address this issue. However, there are two issues that need to be solved. One of them is the control approach. Hence, the main aim of this work, is to investigate the control design for upper-limb exoskeleton. An extended based fuzzy control is proposed to observe the effectiveness of the exoskeleton in dealing with human with different strength. Three conditions of human strength were applied. PID was used for a comparison purpose. It is shown that with the proposed control approach, the exoskeleton can assist human to achieve the desired trajectory accurately with a minimal amount of torque required.