Motion control of a 1-DOF pneumatic muscle actuator positioning system

A positioning system driven by a pneumatic muscle actuator was built in order to study the applicability and adaptability of the system into real time applications such as exoskeleton robots and industrial machines. PMA system has many advantages including high power to weight and power to volume ratio, light weight, clean, autonomous and safe. However, the highly nonlinear characteristics of PMA system made it difficult to control. This has been the main challenge in proposing a robust controller for positioning and tracking performance. This study aims to clarify a practical and easy to design controller design procedure for positioning of a PMA system. In addition to positioning performance, the present study focuses on the realization of easy to design a controller without the need for exact model parameters and knowledge in control theory for systems with high nonlinearities. A PI and PID controller using Ziegler-Nicholas design law is proposed and its PTP performance is presented. Finally, the robustness of the proposed controller have been tested in a tracking environment by using triangular and sinusoidal waveform

Joint unscented kalman filter for dual estimation in a bifilar pendulum for a small UAV

It has always been difficult to accurately estimate the moment of inertia of an object, e.g. an unmanned aerial vehicle (UAV). Whilst various offline estimation methods exist to allow accurate parametric estimation by minimizing an error cost function, they require large memory consumption, high computational effort, and a long convergence time. The initial estimate's accuracy is also vital in attaining convergence. In this paper, a new real time solution to the model identification problem is provided with the use of a Joint Unscented Kalman Filter for dual estimation. The identification procedures can be easily implemented using a microcontroller, a gyroscope sensor, and a simple bifilar pendulum setup. Accuracy, robustness, and convergence speed are achieved.published_or_final_versio

Optimization and effect of supercritical carbon dioxide extraction conditions on global oil yield and eugenol from piper betle leaves

Todays, medicinal plants have been of great importance to the health of people and societies in Malaysia, and the entire world. Piper betle leaves, a member of family Piperaceae is an edible plant. The leaves of Piper betle have been traditionally utilized in India for inhibition of oral diseases. Scientific research shows that the leaves possess many biological activities with a good medicinal and commercial value. Nowadays, advance technologies have been used to develop high quality products. This study concentrates on supercritical fluid extraction technology which carbon dioxide, CO2 play as a solvent. The purpose of this study was to optimize and look into the effects of supercritical CO2 (SC-CO2) extraction process variables, namely pressure (10–30 MPa), temperature (40–80 °C) and CO2 flowrate (2-8 mL/min) on global oil yield and percentage of Eugenol in Piper betle Leaves. The result shows that as the pressure, temperature and flow rate of CO2 increased the oil yield of Piper betle leaves increased. However, further increased, resulting in decreasing the amount of global oil yield. Meanwhile, the percentage of Eugenol increased as the CO2 flow rate increased. However, as the pressure and temperature increased, the percentage of Eugenol decreased. Second order polynomial model was used to express the extracted oil and percentage of Eugenol with the both results was satisfactory. The best conditions to maximize the global oil yields and percentage of Eugenol extracted were 19.0 MPa, 40.0 °C and 7.0 mL/min leading to 0.228g of oil and 8.21 % of Eugenol. The most dominant factor for both responses was CO2 flowrate. The results show a good fit to the proposed model and the optimal conditions obtained were within the experimental range with the value of R2 was 69.06% for global oil yield and 82.79% for amount of Eugenol

Framework of Lower-Limb Musculoskeletal Modeling for FES Control System Development

In recent years, the demand of interest in functional electrical stimulation (FES) is increasing due to the applications especially on spinal cord injury (SCI) patients. Numerous studies have been done to regain mobility function and for health benefits especially due to FES control development for the paralyzed person. In this paper, the existing general framework modeling methods have been reviewed and the new modeling framework approach has been discussed. In general modeling and simulation can greatly facilitate to test and tune various FES control strategies. In fact, the modeling of musculoskeletal properties in people with SCI is significantly challenging for researchers due to the complexity of the system. The complexities are due to the complex structural anatomy, complicated movement and dynamics, as well as indeterminate muscle function. Although there are some models have been developed, the complexities of the system resulting mathematical representation that have a large number of parameters which make the model identification process even more difficult. Therefore, a new approach of modeling has been presented which is comparatively less burdened compared with mathematical representations. Hence this musculoskeletal model can be used for FES control system development

A REVIEW ON LOWER APPENDICULAR MUSCULOSKELETAL SYSTEM OF HUMAN BODY

Rehabilitation engineering plays an important role in designing various autonomous robots to provide better therapeutic exercise to disabled patients. Hence it is necessary to study human musculoskeletal system and also needs to be presented in scientific manner in order to describe and analyze the biomechanics of human body motion. This review focuses on lower appendicular musculoskeletal structure of human body to represent joints and links architectures; to identify muscle attachments and functions; and to illustrate muscle groups which are responsible for a particular joint movement. Firstly, human lower skeletal structure, linking systems, joint mechanisms, and their functions are described with a conceptual representation of joint architecture of human skeleton. This section also represents joints and limbs by comparing with mechanical systems. Characteristics of ligaments and their functions to construct skeletal joints are also discussed briefly in this part. Secondly, the study focuses on muscular system of human lower limbs where muscle structure, functions, roles in moving endoskeleton structure, and supporting mechanisms are presented elaborately. Thirdly, muscle groups are tabulated based on functions that provide mobility to different joints of lower limbs. Finally, for a particular movement action of lower extremity, muscles are also grouped and tabulated to have a better understanding on functions of individual muscle. Basically the study presents an overview of the structure of human lower limbs by characterizing and classifying skeletal and muscular systems. KEYWORDS:   Musculoskeletal system; Human lower limbs; Muscle groups; Joint motion; Biomechatronics; Rehabilitation

Component-wise analysis of metaheuristic algorithms for novel fuzzy-meta classifier

Metaheuristic research has proposed promising results in science, business, and engineering problems. But, mostly high-level analysis is performed on metaheuristic performances. This leaves several critical questions unanswered due to black-box issue that does not reveal why certain metaheuristic algorithms performed better on some problems and not on others. To address the significant gap between theory and practice in metaheuristic research, this study proposed in-depth analysis approach using component-view of metaheuristic algorithms and diversity measurement for determining exploration and exploitation abilities. This research selected three commonly used swarm-based metaheuristic algorithms – Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Cuckoo Search (CS) – to perform component-wise analysis. As a result, the study able to address premature convergence problem in PSO, poor exploitation in ABC, and imbalanced exploration and exploitation issue in CS. The proposed improved PSO (iPSO), improved ABC (iABC), and improved CS (iCS) outperformed standard algorithms and variants from existing literature, as well as, Grey Wolf Optimization (GWO) and Animal Migration Optimization (AMO) on ten numerical optimization problems with varying modalities. The proposed iPSO, iABC, and iCS were then employed on proposed novel Fuzzy-Meta Classifier (FMC) which offered highly reduced model complexity and high accuracy as compared to Adaptive Neuro-Fuzzy Inference System (ANFIS). The proposed three-layer FMC produced efficient rules that generated nearly 100% accuracies on ten different classification datasets, with significantly reduced number of trainable parameters and number of nodes in the network architecture, as compared to ANFIS