Adaptive System Technique Research Articles

Run-time requirements verification for reconfigurable systems

Context: Modern software systems often are distributed, run on virtualized platforms, implement complex tasks and operate on dynamically changing and unpredictable environments. Such systems need to be dynamically reconfigured or evolve in order to continue to meet their functional and non-functional requirements, as load and computation need to change. Such reconfiguration and/or evolution actions may cause other requirements to fail.Objective: Given models that describe with a degree of confidence the requirements that should hold in a running software system, along with their inter-dependencies, our objective is to propose a framework that can process these models and estimate the degree requirements hold as the system is dynamically altered or adapted.Method: We present an approach where requirements and their inter-dependencies are modeled using conditional goal models with weighted contributions. These models can be translated into fuzzy rules, and fuzzy reasoners can determine whether and to what degree, a requirement may be affected by a system change, or by actions related of other requirements.Results: The proposed framework is evaluated for its performance and stability on goal models of varying size and complexity. The experimental results indicate that the approach is tractable even for large models and allows for dealing with models where contribution links are of varying importance or weight.Conclusion: The use of conditional weighted goal models combined with fuzzy reasoners allowed for the tractable run-time evaluation of the degree by which system requirements are believed to hold, when such systems are dynamically altered or adapted. The approach aims to shed light towards the development of run-time requirements verification and validation techniques for adaptive systems or systems that undergo continuous, or frequent evolution.

Discrete-Time Parameter Identification of a Surface-Mounted Permanent Magnet Synchronous Machine

A novel online discrete-time parameter identification algorithm suitable for surface-mounted permanent magnet synchronous machines (SPMs) is presented in this paper. It is developed by means of the model reference adaptive system technique and the Popov Hyperstability Criterion in order to identify SPM discrete-time model parameters. In particular, good accuracy of discrete-time parameters is required by digital control systems, particularly by predictive control algorithms, which present a low robustness against parameter mismatches. Hence, an extensive simulation study is first carried out in the Matlab Simulink environment with the aim of testing the effectiveness and robustness of the proposed identification algorithm against inverter unidealities. Then, the proposed identification procedure is experimentally validated on a predictive controlled radial-flux SPM, driven by a field programmable gate arrays control board.

An MRAS-Based Diagnosis of Open-Circuit Fault in PWM Voltage-Source Inverters for PM Synchronous Motor Drive Systems

In this paper, a simple and low-cost open-circuit fault detection and identification method for a pulse-width modulated (PWM) voltage-source inverter (VSI) employing a permanent magnet synchronous motor is proposed. An open-circuit fault of a power switch in the PWM VSI changes the corresponding terminal voltage and introduces the voltage distortions to each phase voltage. The proposed open-circuit fault diagnosis method employs the model reference adaptive system techniques and requires no additional sensors or electrical devices to detect the fault condition and identify the faulty switch. The proposed method has the features of fast diagnosis time, simple structure, and being easily inserted to the existing control algorithms as a subroutine without major modifications. The simulations and experiments are carried out and the results show the effectiveness of the proposed method.

Model Reference Adaptive System Based Adaptive Speed Estimation for Sensorless Vector Control with Initial Rotor Position Estimation for Interior Permanent Magnet Synchronous Motor Drive

The rotor position/speed sensor used to control the permanent magnet synchronous motor drive has increased size and cost, decreased reliability, and a need for shaft extension and mounting arrangements. Sensorless control is currently adopted in many industrial applications for reasons of robustness, cost, cabling, and reliability. In order to achieve correct operation from zero speed startup of a sensorless salient-pole permanent-magnet synchronous motor (the interior permanent magnet synchronous motor), the initial rotor position of the motor is required. A model reference adaptive system technique has been used for speed estimation in sensorless speed control of the interior permanent magnet synchronous motor with space vector pulse width modulation. This article presents a new approach to improved initial rotor position detection of an interior permanent magnet synchronous motor. The method uses a suitable sequence of short and long time voltage pulses applied to the stator coil at standstill. Short time voltage pulses are applied to the motor terminals to detect the initial rotor position. Long time voltage pulses are used for detection of the magnet polarity based on the effect of saturation. The measurement of the current peaks values gives information about the initial rotor position. Simulation and experimental results are presented and show the validity of the proposed method, which is capable of reliable initial rotor position detection, including the polarity of the magnet at standstill. The error initial rotor position detection is less than 5 mechanical degrees. Experimental results are presented for an interior permanent magnet synchronous motor obtained on a fixed-point digital signal processor based control system. Experimental results show that the modeled reference adaptive system technique speed estimation provides high-performance sensorless control and initial rotor position estimation algorithms of the interior permanent magnet synchronous motor drive.

Swarm intelligence: new techniques for adaptive systems to provide learning support

The notion of a system adapting itself to provide support for learning has always been an important issue of research for technology-enabled learning. One approach to provide adaptivity is to use social navigation approaches and techniques which involve analysing data of what was previously selected by a cluster of users or what worked for previous users or learners of such system and to use such analysis to guide and help the current user. New techniques are now available that rely on emergent or self-organising methods to analyse a large amount of interaction data being generated by large-scale online learning communities, interactions with learning resources or learning management mechanisms. One such technique is swarm intelligence, a set of computing algorithms in the form of multi-agent systems that simulate how swarms of insects or birds move or work. It has been applied to various intelligent functionalities such as adaptive content planning, computer-adaptive testing and assessment paper generation. In this article, we provide a survey of the various approaches in swarm intelligence that explores potential mechanisms to adapt and individualise learning and assessment. We hope that this article can inspire future studies in this exciting area.

Attack of Against Simplified Data Encryption Standard Cipher System Using Neural Networks

Problem statement: The problem in cryptanalysis can be described as an unknown and the neural networks are ideal tools for black-box syste m identification. In this study, a mathematical bla ck- box model is developed and system identification te chniques are combined with adaptive system techniques, to construct the Neuro-Identifier. Approach: The Neuro-Identifier was discussed as a black-box model to attack the target cipher systems . Results: In this study this model is a new addition in cryptography that presented the methods of block (SDES) crypto systems discussed. The constructing of Neuro-Identifier mode achieved two objectives: The first one was to construct emulator of Neuro-model for the target cipher system, while the second was to (cryptanalysis) determine the key from given plaintext-ciphertext pair. Conclusion: Present the idea of the equivalent cipher system, which is identical 100% to the unknown system and that means that an unknown hardware, or software cipher system could be reconstructed without known the internal circuitry or algorithm of it.

Sliding-mode observer for speed-sensorless induction motor drives

A sliding-mode observer for the speed-sensorless direct torque vector control of induction motors is proposed. The observer estimates the motor speed, the rotor flux, the angular position of the rotor flux and the motor torque from measured terminal voltages and currents. The use of the nonlinear sliding-mode technique provides very good performance for both low- and high-speed motor operation and robustness in motor losses and load variations. The proposed observer does not uses a PI controller and complicated observer gains and that establishes an easy setup for any type of motor. Also, the convergence of the proposed observer is examined using the Lyapunov theory. Experimental results using a TMS320F240 digital signal processor are presented for low–speed operation (5 Hz), full speed operation (50 Hz), and forward to reverse operation. Finally, the proposed observer is compared with other recently reported model-reference adaptive system techniques.

A new instantaneous torque control of PM synchronous motor for high-performance direct-drive applications

A new instantaneous torque-control strategy is presented for high-performance control of a permanent magnet (PM) synchronous motor. In order to deal with the torque pulsating problem of a PM synchronous motor in a low-speed region, new torque estimation and control techniques are proposed. The linkage flux of a PM synchronous motor is estimated using a model reference adaptive system technique, and the developed torque is instantaneously controlled by the proposed torque controller combining a variable structure control (VSC) with a space-vector pulse-width modulation (PWM). The proposed control provides the advantage of reducing the torque pulsation caused by the nonsinusoidal flux distribution. This control strategy is applied to the high-torque PM synchronous motor drive system for direct-drive applications and implemented by using a software of the digital signal processor (DSP) TMS320C30. The simulations and experiments are carried out for this system, and the results demonstrate the effectiveness of the proposed control.

The pupillary control system: Its non-linear adaptive and stochastic engineering design characteristics

In the decade since the pupillary light reflex to light was first studied using a linear transfer function approach [18, 20], many developments in control theory and in research into biological control systems have occurred. Optimal control theory, Liapunov stability methods, Wiener-G functional analysis, general systems identification procedures and pattern recognition techniques for adaptive systems have influenced experimental approaches to a wide variety of neurological control systems. An intensive reinvestigation using classical electrophysiological techniques to define interesting design features of the underlying neurophysiological mechanisms of these systems is now proceeding, and it appears that clinical applications are on the horizon. Three contemporary experimental approaches to the pupillary system will be described in detail and shown to result in complementary views. 1. (A) Wiener-G functional analysis using both random and time domain inputs [10]. 2. (B) Statistical communication analysis of pupil noise [12, 16], and 3. (C) Neurophysiological techniques embedded within a systems approach [19].