Design of an adaptive fuzzy sliding mode control for uncertain discrete-time nonlinear systems based on noisy measurements

This paper presents the design of an adaptive fuzzy sliding mode control (AFSMC) for uncertain discrete-time nonlinear dynamic systems. The dynamic systems are described by a discrete-time state equation with nonlinear uncertainties, and the uncertainties include the modelling errors and the external disturbances to be unknown but nonlinear with the bounded properties. The states are measured by the restriction of measurement sensors and the contamination with independent measurement noises. The nonlinear uncertainties are approximated by using the fuzzy IF-THEN rules based on the universal approximation theorem, and the approximation error is compensated by adding an adaptive complementary term to the proposed AFSMC. The fuzzy inference approach based on the extended single input rule modules is proposed to reduce the number of the fuzzy IF-THEN rules. The estimates for the un-measurable states and the adjustable parameters are obtained by using the weighted least squares estimator and its simplified one. It is proved that under some conditions the estimation errors will remain in the vicinity of zero as time increases, and the states are ultimately bounded subject to the proposed AFSMC. The effectiveness of the proposed method is indicated through the simulation experiment of a simple numerical system.     

Basic constructs of the SIM operating system

The basic constructs of SIMPOS (Sequential Inference Machine Programming and Operating System) are explained. SIMPOS is an operating system for a super-personal computer (SIM), based on logic programming language (a modified Prolog, called KL0). Our design principle is simplicity both in concept and structure. The entire system will be constructed using these basic constructs.     

Theory of ion mobility in liquid3He

An ion interacting with quasiparticles in liquid³He is treated theoretically by summing most divergent terms in perturbation series of the self-energy of the ion and the vertex part. The ion Green's function is renormalized by a factorZ(T), and the vertex part byZ(T) ^?1, where \({\text{Z(T)}} = {\text{(T/T}}_F {\text{)}}^{{\text{2V}}_{{\text{0}}^{\rho ^{\text{2}} } }^{\text{2}} } \) , forT ₀?T?T _F. Here,T ₀=(m/M)T _F, withm the³He mass andM the ion mass, and \({\text{V}}_{0^\rho } \) is the strength of the interaction. The factor explains the weak temperature dependence of the mobility around the minimum atT ₀; we also discuss its effect on the behavior of the mobility in³He-B nearT _c.     

Theoretical and evolutionary parameter tuning of neural oscillators with a double-chain structure for generating rhythmic signals

A neural oscillator with a double-chain structure is one of the central pattern generator models used to simulate and understand rhythmic movements in living organisms. However, it is difficult to reproduce desired rhythmic signals by tuning an enormous number of parameters of neural oscillators. In this study, we propose an automatic tuning method consisting of two parts. The first involves tuning rules for both the time constants and the amplitude of the oscillatory outputs based on theoretical analyses of the relationship between parameters and outputs of the neural oscillators. The second involves an evolutionary tuning method with a two-step genetic algorithm (GA), consisting of a global GA and a local GA, for tuning parameters such as neural connection weights that have no exact tuning rule. Using numerical experiments, we confirmed that the proposed tuning method could successfully tune all parameters and generate sinusoidal waves. The tuning performance of the proposed method was less affected by factors such as the number of excitatory oscillators or the desired outputs. Furthermore, the proposed method was applied to the parameter-tuning problem of some types of artificial and biological wave reproduction and yielded optimal parameter values that generated complex rhythmic signals in Caenorhabditis elegans without trial and error.     

A high‐performance sorting algorithm for multicore single‐instruction multiple‐data processors

Many sorting algorithms have been studied in the past, but there are only a few algorithms that can effectively exploit both single‐instruction multiple‐data (SIMD) instructions and thread‐level parallelism. In this paper, we propose a new high‐performance sorting algorithm, called aligned‐access sort (AA‐sort), that exploits both the SIMD instructions and thread‐level parallelism available on today's multicore processors. Our algorithm consists of two phases, an in‐core sorting phase and an out‐of‐core merging phase. The in‐core sorting phase uses our new sorting algorithm that extends combsort to exploit SIMD instructions. The out‐of‐core algorithm is based on mergesort with our novel vectorized merging algorithm. Both phases can take advantage of SIMD instructions. The key to high performance is eliminating unaligned memory accesses that would reduce the effectiveness of SIMD instructions in both phases. We implemented and evaluated the AA‐sort on PowerPC 970MP and Cell Broadband Engine platforms. In summary, a sequential version of the AA‐sort using SIMD instructions outperformed IBM's optimized sequential sorting library by 1.8 times and bitonic mergesort using SIMD instructions by 3.3 times on PowerPC 970MP when sorting 32 million random 32‐bit integers. Also, a parallel version of AA‐sort demonstrated better scalability with increasing numbers of cores than a parallel version of bitonic mergesort on both platforms. Copyright © 2011 John Wiley & Sons, Ltd.     

Helium gas permeability of montmorillonite/epoxy nanocomposites

Helium gas permeability of silicate clay (montmorillonite) particles/epoxy nanocomposites was examined. The incorporation of increasing amounts of montmorillonite particles reduced the helium gas permeability. Based on Fick’s law, gas permeation behavior of the nanocomposite was evaluated. With the increase of montmorillonite loading, gas diffusivity decreased, while gas solubility increased. Helium diffusion behavior is in agreement to the numerical results based on the Hatta–Taya–Eshelby theory. It has been revealed that dispersion of nanoscale platelets in polymer is effective in improving gas barrier property.     

Wide-gap layered oxychalcogenide semiconductors: Materials, electronic structures and optoelectronic properties

Applying the concept of materials design for transparent conductive oxides to layered oxychalcogenides, several p-type and n-type layered oxychalcogenides were proposed as wide-gap semiconductors and their basic optical and electrical properties were examined. The layered oxychalcogenides are composed of ionic oxide layers and covalent chalcogenide layers, which bring wide-gap and conductive properties to these materials, respectively. The electronic structures of the materials were examined by normal/inverse photoemission spectroscopy and energy band calculations. The results of the examinations suggested that these materials possess unique features more than simple wide-gap semiconductors. Namely, the layered oxychalcogenides are considered to be extremely thin quantum wells composed of the oxide and chalcogenide layers or 2D chalcogenide crystals/molecules embedded in an oxide matrix. Observation of step-like absorption edges, large band gap energy and large exciton binding energy demonstrated these features originating from 2D density of states and quantum size effects in these layered materials.     

Reusing Primitive and Acquired Motion Knowledge for Gait Generation of a Six-legged Robot Using Genetic Programming

There has been growing interest in motion planning problems for mobile robots. In this field, the main research is to generate a motion for a specific robot and task without previously acquired motions. However it is too wasteful not to use hard-earned acquired motions for other tasks. Here, we focus on a mechanism of reusing acquired motion knowledge and study a motion planning system able to generate and reuse motion knowledge. In this paper, we adopt a tree-based representation for expressing knowledge of motion, and propose a hierarchical knowledge for realizing a reuse mechanism. We construct a motion planning system using hierarchical knowledge as motion knowledge and using genetic programming as a learning method. We apply a proposed method for the gait generation task of a six-legged locomotion robot and show its availability with computer simulation.     

Sensor Fusion System Using Recurrent Fuzzy Inference

In robotic and manufacturing systems, it is difficult to measure the state of systems accurately because of many uncertain factors and noise, and it is very important to estimate the state of systems. We must measure the phenomena of systems by multiple sensors and estimate the state of systems by acquiring information of sensors. However, we can not acquire all of sensor information synchronically, because each sensor has particular sensor information and measuring time. For estimating the state of systems by multiple sensors, a multi-sensor fusion system fusing various sensory information is needed. In this paper, we propose a Recurrent Fuzzy Inference (RFI) with recurrent inputs and apply it to a multi-sensor fusion system for estimating the state of systems. The membership functions of RFI are expressed by Radial Basis Function (RBF) with insensitive ranges. The shape of the membership functions can be adjusted by a learning algorithm. The learning algorithm is based on the steepest descent method and incremental learning which can add new fuzzy rules. The effectiveness of the multi-sensor fusion system using RFI will be shown through a numerical experiment of moving robot and estimation of surface roughness in grinding process.