Tracking-error model-based predictive control for mobile robots in real time

In this paper, a model-predictive trajectory-tracking control applied to a mobile robot is presented. Linearized tracking-error dynamics is used to predict future system behavior and a control law is derived from a quadratic cost function penalizing the system tracking error and the control effort. Experimental results on a real mobile robot are presented and a comparison of the control obtained with that of a time-varying state-feedback controller is given. The proposed controller includes velocity and acceleration constraints to prevent the mobile robot from slipping and a Smith predictor is used to compensate for the vision-system dead-time. Some ideas for future work are also discussed.     

The effect of zirconium addition on the microstructure and properties of chopped carbon fiber/carbon composites

Carbon/carbon composites containing zirconium were prepared using chopped carbon fiber, mesophase pitch and Zr powder by the traditional process including molding, carbonization, densification and graphitization. The influence of Zr on the microstructure and properties of the composites were investigated. Results show that Zr can improve the interface bonding, promote more perfect and larger crystallites and enhance the conductive/mechanical properties of the composites. The high in-plane thermal conductivity of 464 W/(m K) and excellent bending strength of 83.6 MPa was obtained for a Zr content of 13.9 wt% at heat treatment temperature(HTT) of 2500 °C. However the conductive/mechanical properties of the composites decrease dramatically for an higher HTT of 3000 °C. SEM micrograph of the fracture surface for the composites shows that lower disorder crystallite arrangement of fiber and carbon matrix come into being in the composites during HTT of 3000 °C, which should be responsible for the low properties. Correlation between the content of Zr and the microstructure and properties are discussed.     

Experiments and simulations of the mechanical performance of repaired panels

Firstly, the compress experiment is undertaken to investigate the efficiency of repaired panels in this paper, and then modeling of the mechanical behavior of the repaired composite panel under compressive static load is conducted by using of the finite element method. The effect of geometric non-linearity on the stress–strain response is considered in the numeric analysis. Fatherly, the user material subroutine (UMAT) is integrated with the ABAQUS package with the geometric non-linearity effect for studying the damage initiation and its progression in the composite structure, and quadrilateral, linear, thick shell elements (S8R) are adopted. Finally, the predicted strain distribution, damage evolution and strength of the laminate are compared with the test results.     

A new method of strength calculation and lifetime prediction of pipe bends operating in the creep range

An easy to use method for an approximate creep life prediction for pipe bends was developed within a recent R & D-project in Germany. The new algorithm is based on a Finite-Element parameter study comprising approximately 200 pipe bends. The creep behaviour of the materials primarily used for main steam and hot reheat piping is described using the Graham–Walles creep law. Since the new algorithm considers the time dependent out-of-roundness and stress redistribution of the pipe bend, it yields a more precise creep life prediction, compared to standard piping codes. It is possible to integrate this new calculation into existing online lifetime-monitoring-systems.     

Machine-learning paradigms for selecting ecologically significant input variables

Harmful algal blooms, which are considered a serious environmental problem nowadays, occur in coastal waters in many parts of the world. They cause acute ecological damage and ensuing economic losses, due to fish kills and shellfish poisoning as well as public health threats posed by toxic blooms. Recently, data-driven models including machine-learning (ML) techniques have been employed to mimic dynamics of algal blooms. One of the most important steps in the application of a ML technique is the selection of significant model input variables. In the present paper, we use two extensively used ML techniques, artificial neural networks (ANN) and genetic programming (GP) for selecting the significant input variables. The efficacy of these techniques is first demonstrated on a test problem with known dependence and then they are applied to a real-world case study of water quality data from Tolo Harbour, Hong Kong. These ML techniques overcome some of the limitations of the currently used techniques for input variable selection, a review of which is also presented. The interpretation of the weights of the trained ANN and the GP evolved equations demonstrate their ability to identify the ecologically significant variables precisely. The significant variables suggested by the ML techniques also indicate chlorophyll-a (Chl-a) itself to be the most significant input in predicting the algal blooms, suggesting an auto-regressive nature or persistence in the algal bloom dynamics, which may be related to the long flushing time in the semi-enclosed coastal waters. The study also confirms the previous understanding that the algal blooms in coastal waters of Hong Kong often occur with a life cycle of the order of 1–2 weeks.     

Thermoelastic evaluation of composite laminates using digital imaging processing

The study of the variation of the curvature of non-symmetric composite laminates with temperature provides a measure of the magnitude of the thermal stresses and the mechanical behavior of the material with temperature. In the present work, an experimental method based on the pioneer use of a digital camera and image processing method is proposed to perform such measurements. Taking advantage of the method and by comparing experimental and CLT, extended-CLT and FEM simulation results, an evaluation of the residual stresses is described and an approach providing more precise determination of the residual thermal stresses in non-symmetric laminates is proposed.     

Experimental Study on Energy Consumption of Combined Conventional and Dehumidification Drying

Conventional drying, dehumidification drying, and combined conventional-dehumidification drying of wood were experimentally studied in this article. The results showed that the energy consumption in the dehumidification drying is the least but its drying time is the longest. The energy consumption in the combined drying is more than that in the dehumidification drying but less than that in the conventional drying, and the drying time is half of that in the dehumidification drying. The advantages of the combined conventional and dehumidification drying are discussed from the experiments results.