Determining the appropriate natural fibers for intelligent green wearable devices made from biomaterials via multi-attribute decision making model.

Intelligent and green wearable technology becomes essential for new modern societies. This work introduces a multi criteria decision making model to properly assess and compare relative desired criteria for selecting the most suitable constituents for green body wearable bio-products made from bio-based materials. It aims to enhance the sustainability of intelligent green wearable devices by providing support in the selection process of lightweight, eco-friendly materials suitable for personal body wearable bio-products made of natural fiber composites to improve qualities that may help in better monitoring human vital signs and thereby address the health care concern. The relative intrinsic characteristic and merits of various natural fibers were utilized to compare and evaluate their relative performance in bio-composites. The model considered several evaluation factors like mechanical performance including tensile strength and modulus of elasticity, comfortability including size and weight, availability, fiber orientation, cellulose content, and cost. Results have demonstrated different priorities of the considered natural fibers relative to each evaluation factor. However, the model was capable of properly evaluating and ranking the best fibers relative to the whole conflicting evaluation criteria simultaneously. The closeness of priorities in several cases emphasizes upon using such decision making models to be able to judge the relative merits of natural fibers for such applications. It can also help designers to avoid bias during determining the best alternatives considering several conflicting evaluation criteria.

Generalized predictive control for a coupled four tank MIMO system using a continuous-discrete time observer.

This paper deals with the problem of the observer based control design for a coupled four-tank liquid level system. For this MIMO system's dynamics, motivated by a desire to provide precise and sensorless liquid level control, a nonlinear predictive controller based on a continuous-discrete observer is presented. First, an analytical solution from the model predictive control (MPC) technique is developed for a particular class of nonlinear MIMO systems and its corresponding exponential stability is proven. Then, a high gain observer that runs in continuous-time with an output error correction time that is updated in a mixed continuous-discrete fashion is designed in order to estimate the liquid levels in the two upper tanks. The effectiveness of the designed control schemes are validated by two tests; The first one is maintaining a constant level in the first bottom tank while making the level in the second bottom tank to follow a sinusoidal reference signal. The second test is more difficult and it is made using two trapezoidal reference signals in order to see the decoupling performance of the system's outputs. Simulation and experimental results validate the objective of the paper.