The strengthening of Islamic values on students through the metaphor of accepting death: an Indonesian perception

Death is a sure entity for every human that cannot be avoided in human life. The purpose of this research was to reveal that the usage of metaphor technique called, “The Acceptance of Death” in group counselling can improve Islamic values on Muslim students. This study employed an action research using The Kemmis Model with the stages of planning, action, observation, and reflection. This research implemented group counselling with metaphor technique of accepting death by students. The research subjects were 20 female students of State Islamic University of Sultan Syarif Kasim Riau who lived in the campus dormitory. The selection of the research subjects was done randomly by choosing the female students who were willing to join the group counselling activity. The research results showed that the practice of metaphor technique of “The Acceptance of Death” in the group counselling can strengthen the Islamic values and their characteristics as Muslims. They understand their previous mistakes and are willing to be better for the sake of their life. They have the commitment to become the best students and the best Muslims

Circle grid fractal plate as a turbulent generator for premixed flame: an overview

This review paper focuses to ascertain a new approach in turbulence generation on the structure of premixed flames and external combustion using a fractal grid pattern. This review paper discusses the relationship between fractal pattern and turbulence flow. Many researchers have explored the fractal pattern as a new concept of turbulence generators, but researchers rarely study fractal turbulence generators on the structure premixed flame. The turbulent flow field characteristics have been studied tand investigated in a premixed combustion application. In terms of turbulence intensity, most researchers used fractal grid that can be tailored so that they can design the characteristic needed in premixed flame. This approach makes it extremely difficult to determine the exact turbulent burning velocity on the velocity fluctuation of the flow. The decision to carry out additional research on the effect circle grid fractal plate as a turbulent generator for premixed flame should depends on the blockage ratio and fractal pattern of the grid. 1

Learning and Reacting with Inaccurate Prediction: Applications to Autonomous Excavation

Motivated by autonomous excavation, this work investigates solutions to a class of problem where disturbance prediction is critical to overcoming poor performance of a feedback controller, but where the disturbance prediction is intrinsically inaccurate. Poor feedback controller performance is related to a fundamental control problem: there is only a limited amount of disturbance rejection that feedback compensation can provide. It is known, however, that predictive action can improve the disturbance rejection of a control system beyond the limitations of feedback. While prediction is desirable, the problem in excavation is that disturbance predictions are prone to error due to the variability and complexity of soil-tool interaction forces. This work proposes the use of iterative learning control to map the repetitive components of excavation forces into feedforward commands. Although feedforward action shows useful to improve excavation performance, the non-repetitive nature of soil-tool interaction forces is a source of inaccurate predictions. To explicitly address the use of imperfect predictive compensation, a disturbance observer is used to estimate the prediction error. To quantify inaccuracy in prediction, a feedforward model of excavation disturbances is interpreted as a communication channel that transmits corrupted disturbance previews, for which metrics based on the sensitivity function exist. During field trials the proposed method demonstrated the ability to iteratively achieve a desired dig geometry, independent of the initial feasibility of the excavation passes in relation to actuator saturation. Predictive commands adapted to different soil conditions and passes were repeated autonomously until a pre-specified finish quality of the trench was achieved. Evidence of improvement in disturbance rejection is presented as a comparison of sensitivity functions of systems with and without the use of predictive disturbance compensation

Investigation in modeling a load-sensing pump using dynamic neural unit based dynamic neural networks

Because of the highly complex structure of the load-sensing pump, its compensators and controlling elements, simulation of load-sensing pump system pose many challenges to researchers. One way to overcome some of the difficulties with creating complex computer model is the use of “black box” approach to create an approximation of the system behaviour by analyzing input/output relationships. That means the details of the physical phenomena are not so much of concern in the “black box” approach. Neural network can be used to implement the black box concept for system identification and it is proven that the neural network have the ability to model very complex behaviour and there is a well defined set of neural and neural network structures. Previous studies have shown the problems and limitations in dynamic system modeling using static neuron based neural networks. Some new neuron structures, Dynamic Neural Units (DNUs), have been developed which open a new area to the research associated with the system modelling.The overall objective of this research was to investigate the feasibility of using a dynamic neural unit (DNU) based dynamic neural network (DNN) in modeling a hydraulic component (specifically a load-sensing pump), and the model could be used in a simulation with any other required component model to aid in hydraulic system design. To be truly representative of the component, the neural network model must be valid for both the steady state and the transient response. Due to three components (compensator, pump and control valve) in a load sensing pump system, there were three different pump model structures (the pump, compensator and valve model, the compensator and pump model, and the “pump only” model) from the practical point of view, and they were analysed thoroughly in this study. In this study, the DNU based DNN was used to model a “pump only” model which was a portion of a complete load sensing pump. After the trained DNN was tested with a wide variety of system inputs and due to the steady state error illustrated by the trained DNN, compensation equation approach and DNN and SNN combination approach were then adopted to overcome the steady state deviation. It was verified, through this work, that the DNU based DNN can capture the dynamics of a nonlinear system, and the DNN and SNN combination can eliminate the steady state error which was generated by the trained DNN. The first major contribution of this research was in investigating the feasibility of using the DNN to model a nonlinear system and eliminating the “error accumulation” problem encountered in the previous work. The second major contribution is exploring the combination of DNN and SNN to make the neural network model valid for both steady state and the transient response

A neural network-based inversion method of a feedback linearization controller applied to a hydraulic actuator

In this work, we use a neural network as a substitute for the traditional analytic functions employed as an inversion set in feedback linearization control algorithms applied to hydraulic actuators. Although very efective and with strong stability guarantees, feedback linearization control depends on parameters that are difcult to determine, requiring large amounts of experimental efort to be identifed accurately. On the other hands, neural networks require little efort regarding parameter identifcation, but pose signifcant hindrances to the development of solid stability analyses and/or to the processing capabilities of the control hardware. Here, we combine these techniques to control the positioning of a hydraulic actuator, without requiring extensive identifcation procedures nor losing stability guarantees for the closed-loop system, at reasonable computing demands. The efectiveness of the proposed method is verifed both theoretically and by means of experimental results

Experimental investigation of feedforward inverse control with disturbance observer for acceleration tracking of electro-hydraulic shake table

Electro-hydraulic shake tables (EHSTs) are indispensable equipments in laboratory for evaluating structural performance subject to vibration environment. A novel feedforward inverse control with disturbance observer strategy is proposed in this paper in order to improve the acceleration tracking performance of the EHST system. The EHST system is firstly controlled by the three variable controller (TVC) to obtain a coarse time waveform replication accuracy, and then the parametric transfer function of the TVC controlled EHST system is identified with the H1 estimation method and complex curving fitting technology. Next, the zero magnitude error tracking control technology is employed to deal with the estimated non-minimum phase transfer function so as to design a stable and casual inverse model, and the proposed controller comprised of feedforward inverse controller and disturbance observer is further established based on the designed inverse model. Therefore, the proposed algorithm combines the virtues of feedforward inverse control and disturbance observer. The proposed algorithm is firstly programmed by MATLAB/Simulink software and then is compiled to an Advantech computer with real-time operating system for implementation. Finally, experiments are carried out on a unidirectional EHST system and the results demonstrate that a better acceleration tracking performance is achieved with the proposed controller than with the other conventional controllers

Motion Control of Hydraulic Winch Using Variable Displacement Motors

The paper II is excluded from the dissertation with respect to copyright.To compete in the open market of the offshore crane industry, it is imperative for the manufacturer to continuously improve crane operability. In this context, the crane operability is expressed by means of a so-called weather window. The weather window is computed from the crane characteristics in combination with that of the vessel and the payload to be handled. It returns a set of boundaries for when it is accepted to perform a planned lift, mainly in terms of current sea-state and wind. The most important crane operability characteristics that enter into the computation of the weather window are maximum wire velocity and load capacity. This thesis focuses on how to improve the operability of active heave compensated offshore cranes. Two ways of achieving that goal have been investigated, namely, an improved control strategy and the use of model-based lift planning. The system investigated is the hydraulic active/passive winch system used by National Oilwell Varco. A new control strategy for the system was developed, tested, and implemented. The new strategy utilizes that variable displacement of the hydraulic motors of the active system of the winch drive. The strategy, semi secondary control, gave significant benefits in terms of reduced peak-pressure, increased load capacity, increased wire-speed capacity, and smoother winch performance at low winch speed. The results were validated and verified through simulations and in-field measurements.publishedVersio

Dynamics and iterative learning control of robots with parallel kinematical structures / Bodo Heimann and Houssem Abdellatif

The paper deals with model-based control of robots with parallel kinematical structure (PKM). At first, an approach for the identification of friction and rigid-body dynamics of complex parallel kinematical structures is presented. The approach is based on optimal excitation trajectories. The trajectories are bounded, such they are easy to befit into the small and hard constraint workspace of PKM. Secondly, some results are presented using feedforward control in order to compensate nonlinear dynamical influences. Thirdly, Iterative Learning Control (ILC) is proposed in this paper for tracking accuracy enhancement of a parallel direct driven manipulator. It is shown both by means of simulation study and experimental results that linear ILC is appropriate for application to the considered high nonlinear and coupled systems