Sliding mode control with disturbance observer for a class of nonlinear systems

This paper is concerned with the stabilization problem for a class of nonlinear systems with disturbance. The disturbance model is unknown and the first derivative of disturbance is bounded. Firstly, a general disturbance observer is proposed to estimate disturbance approximatively. Secondly, since the bound of the disturbance observer error is unknown, an adaptive sliding mode controller is designed to guarantee that the state of system asymptotically converges to zero and the unknown bound can be adjusted by an adaptive law. Finally, an example is given to illustrate the effectiveness of the proposed method.     

Linking teacher beliefs, practices and student inquiry-based learning in a CSCL environment: A tale of two teachers

The links uncovered by research connecting teacher beliefs to classroom practice and student inquiry-based learning are tenuous. This study aims at examining (a) how teacher beliefs influenced practices; and (b) how the influence on practices, in turn, impacted student inquiry learning in a CSCL environment. Through a fine-grained comparative analysis of two cases, this study explores how two teachers with different collections of beliefs enacted the same mathematics lesson on division and fractions in a CSCL environment premised on inquiry principles, and what the connections between different enactments and students’ progressive inquiry process and outcomes were. The findings suggest that the two teachers’ adherence to different beliefs led to different practices, which in turn contributed to different student learning processes and outcomes. We interpret these differences that shaped the students’ opportunities for progressive inquiry in the CSCL environment. We conclude that the teacher holding “innovation-oriented” beliefs tended to enact the lesson in patterns of inquiry-principle-based practices and technology-enhanced orchestration; these patterns interacted with each other to contribute to student inquiry learning and effective use of technology affordances.     

Design of a two‐level controller for an active suspension system

The paper focuses on a control design for a vehicle suspension system in which a balance between different performance demands is achieved. The starting point of the control design is a full–car model which contains nonlinear components, i.e. the dynamics of the dampers and springs and nonlinear actuator dynamics. In order to handle the high complexity of the problem this paper proposes the design of a two‐level controller of an active suspension system. The required control force is computed by applying a high‐level controller, which is designed using a linear parameter varying (LPV) method. For the control design the model is augmented with weighting functions specified by the performance demands and the uncertainty assumptions. The actuator generating the necessary control force is modelled as a nonlinear system for which a low‐level force‐tracking controller is designed. To obtain the low‐level controller a backstepping method is proposed. As an alternative solution a feedback linearization method is also presented. The operation of the controller is illustrated through simulation examples. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

A new time invariant fuzzy time series forecasting method based on particle swarm optimization

In the analysis of time invariant fuzzy time series, fuzzy logic group relationships tables have been generally preferred for determination of fuzzy logic relationships. The reason of this is that it is not need to perform complex matrix operations when these tables are used. On the other hand, when fuzzy logic group relationships tables are exploited, membership values of fuzzy sets are ignored. Thus, in defiance of fuzzy set theory, fuzzy sets’ elements with the highest membership value are only considered. This situation causes information loss and decrease in the explanation power of the model. To deal with these problems, a novel time invariant fuzzy time series forecasting approach is proposed in this study. In the proposed method, membership values in the fuzzy relationship matrix are computed by using particle swarm optimization technique. The method suggested in this study is the first method proposed in the literature in which particle swarm optimization algorithm is used to determine fuzzy relations. In addition, in order to increase forecasting accuracy and make the proposed approach more systematic, the fuzzy c-means clustering method is used for fuzzification of time series in the proposed method. The proposed method is applied to well-known time series to show the forecasting performance of the method. These time series are also analyzed by using some other forecasting methods available in the literature. Then, the results obtained from the proposed method are compared to those produced by the other methods. It is observed that the proposed method gives the most accurate forecasts.     

An efficient initialization approach of Q-learning for mobile robots

This article demonstrates that Q-learning can be accelerated by appropriately specifying initial Q-values using dynamic wave expansion neural network. In our method, the neural network has the same topography as robot work space. Each neuron corresponds to a certain discrete state. Every neuron of the network will reach an equilibrium state according to the initial environment information. The activity of the special neuron denotes the maximum cumulative reward by following the optimal policy from the corresponding state when the network is stable. Then the initial Q-values are defined as the immediate reward plus the maximum cumulative reward by following the optimal policy beginning at the succeeding state. In this way, we create a mapping between the known environment information and the initial values of Q-table based on neural network. The prior knowledge can be incorporated into the learning system, and give robots a better learning foundation. Results of experiments in a grid world problem show that neural network-based Q-learning enables a robot to acquire an optimal policy with better learning performance compared to conventional Q-learning and potential field-based Qlearning.     

Automated analysis for scintigraphic evaluation of gastric emptying using invariant moments

This study introduces a method for automated analysis of the standard solid-meal gastric emptying test. The purpose was to develop a diagnostic tool to characterize more reproducibly abnormalities of solid-phase gastric emptying. The processing of gastric emptying is automated using geometrical moments that are invariant to scaling, rotation, and shift. Twenty subjects were studied. The first step was to obtain images of the stomach using a nuclear gamma camera immediately after the subject had eaten a radio-labeled meal. The second step was to process and analyze the images by a recently developed automated gastric emptying analysis (AGEA) method, which determines the gastric contour and the geometrical properties include such parameters as area, centroid, orientation, and moments of inertia. Statistical tests showed that some of the moments were sensitive to the patient's gastric status (normal versus abnormal). The difference between the normal and abnormal patients became noticeable approximately 1 h after meal ingestion.     

X-ray and electron microscopical studies of thin films of (Cd1−xZnx)S solid solution

A series of (Cd_1–x Zn _x )S powders phosphors and thin layers prepared by thermal evaporation of solid solution were studied. The phosphors used were 41% ZnS: 59% CdS with a cobalt concentration from 0 to 0.325%. The analysis of the structure of films of different thicknesses using X-ray diffraction technique confirms that the calculated relative intensities of the planes show considerable differences from the experimental results. For film thicknesses 70 nm thea axis is normal to the substrate, while at greater thicknesses (273 nm) thec axis is practically normal to the substrate. The effect of the electron beam on the solid solution indicates that layers decomposed leaving the grain boundaries decorated by metallic cadmium and zinc particles.