Design Of Multiple Models Research Articles

FPGA-based Design of Multiple Models for Industry 4.0 Cyber Security

Industry 4.0 computing infrastructure depends on traditional processing platforms, and all these platforms perform well and achieving acceptable throughput. Two significant problems confront with these platforms are the delay in data processing and security hazard for potential threats. A design coding has been written using VHDL based on behaviour temporal parallelism for Industry 4.0 cybersecurity model. The models are divided into three namely, single, dual and quad models. Every model has three main components include UART, FIFO and DES algorithm for data encryption and decryption. Encryption is achieved more than one block at a time and over the number of designs to find the most suitable application for data processing. A DES algorithm based efficient implementing the design of 16-round multiple parallel models for low-cost, scalable, and robust encryption solution of a maximum clock frequency of 227MHz and it is capable of encrypting or decrypting data blocks at a rate of throughput 58,288Mbps.

A direct MRAC based multivariable multiple-model switching control scheme

In this paper a direct model reference adaptive control (MRAC) based multiple-model switching control scheme is developed for linear multivariable plants. Such a scheme is capable of ensuring desired system performance, avoiding control singularity and possible persistent control switching. A plant signal identity is used to derive a bank of parameter estimators which are initialized from different parameter subregions. A bank of adaptive controllers are constructed, whose parameters are directly updated from the estimators with globally stable adaptive laws for desired parameter adaptation without control gain matrix inversion to avoid control singularity. A control switching mechanism is designed with performance indexes formed from estimation errors which are directly used in the controller parameter update laws and are closely related to the tracking error, together with the use of a lower threshold switching parameter to ensure the eventual settle-down of the control switching. Closed-loop stability and output tracking are analyzed, and some extensions of the multiple-model design are given. Simulation results are presented to show the desired system performance, especially, improved system transient responses.

Multiple models decentralized coordinated control of doubly fed induction generator

• The correlative measured model of doubly feed induction generator is obtained. • A multiple model optimal tracking control based on correlative measured model is proposed. • The obtained control law can be considered as a decentralized coordinate control. • Various disturbances were applied in a hybrid power system to illustrate the contributions. In this paper, a multiple model optimal tracking control (MOTC) design method for the double fed induction generator (DFIG) using correlative measured technique is proposed. The DFIG is represented by a third-order model, where electro-magnetic transients of stator are neglected. By using the correlative measured technique, the correlative measured matrix (CMM) of wind power system is obtained firstly. Then, a nonstandard state space equation of DFIG is obtained with the correlative measured vectors (CMVs), which reflect interactions between the DFIGs and grid. In order to cope with nonlinearities and continuous variation in the operating points, a multiple model design method is proposed in the discrete domain. The obtained control law, synthesized by using Bayesian probability, only depends on the local measured parameters. Hence, the MOTC can be regarded as a decentralized coordinated control, which can simplify the control structure and improve the transient stability of DFIG. To illustrate the effectiveness of the proposed MOTC strategy, simulations on a hybrid wind thermal power (HWTP) system are performed. The results show that the proposed MOTC strategy can provide acceptable performance throughout the whole operating region. Comparing to the conventional PID control, transient stability, damping, and fault ride-through capability of DFIG with the proposed MOTC design method have been improved effectively.

다물체시스템의 중앙집중 연속학습제어 복수모형 확률설계기법

본 논문은 다물체 복합시스템 인자의 변위에 강인한 중앙집중식 연속반복학습제어기(Repetitive Controller, RC)를 설계하는 방법을 소개하고자 한다. 이때 사용되는 불확실 인자들은 확률분포함수에 의해 무작위로 설정되게 된다. 분포함수를 직접 적용하는 대신, 본 제어기는 설정된 확률함수로부터 생성된 모형을 기본으로 설계하였다. 이러한 중앙집중식 복수모형 설계기법으로 엄의의 분포함수로 구성된 수많은 불확실 인자들을 다룰 수 있다. 그러므로, 제어기는 반복영역에서 수렴성을 보장하는 비용함수를 주파수영역에서 최소화함으로써 유도할 수 있다. 다물체 복합시스템에서 중앙집중식 복수모형설계 기법을 단수모형 설계기법과 함께 제안하였다. This paper presents a method to design a centralized repetitive controller that is robust to variations in the multiple system parameters. The uncertain parameters are specified probabilistically by their probability distribution functions. Instead of working with the distribution functions directly, the centralized repetitive controller is designed from a set of models that are generated from the specified probability functions. With this multiple-model design approach, any number of uncertain parameters that follow any type of distribution functions can be treated. Furthermore, the controller is derived by minimizing a frequency-domain based cost function that produces monotonic convergence of the tracking error as a function of repetition number. Numerical illustrations show how the proposed multiple-model design method produces a repetitive controller that is significantly more robust than an optimal repetitive controller designed from a single nominal model of the multiple system.

Cheap Joint Probabilistic Data Association filters in an Interacting Multiple Model design

This paper presents an approach to fuse multiple sensors in an Interacting Multiple Model design. Visual features like shadow and symmetry, treated as independent stand-alone virtual sensors, are employed for detection and tracking of vehicles for driver assistance tasks. Cheap Joint Probabilistic Data Association is utilised to account for the large amount of clutter in the measurements provided by these sensors. Special attention is devoted to the different noise characteristics of the measurements. The individual sensors are considered in a sequential manner, leading to a versatile fusion architecture that allows easy integration of further sensor modules.

Multiple Models Control of a Remotely Operated Vehicle: Analysis of Models Structure and Complexity

The multiple models control problem of an underwater vehicle used in the exploitation of combustible gas deposits at great water depths is investigated. In particular, the linear models obtained by linearization and discretization of ROV dynamics around the operating points are compared with the linear models obtained by parametric identification methods based on experimental data collected in real operative conditions. The results of this analysis produce a significative estimation of the parameters variability of the considered class of linear models. This investigation is of main relevance in the design of multiple models controllers for choosing the number of linear models and their distribution in the parameter space.

Supervisory Multiple Model Control

This article discusses the ccxnplete controller design strategy required for implementing Multiple Model Control (MMC), applied to nonlinear systems. It is shown that the multiple model design can be recast into a supervisory arrangement, where a global supervisor is utilised to select the appropriate controller from a fixed family set. Unlike current techniques where Fuzzy Validity Functions or Bayesian Estimates are utilised in the selection mechanism, the apprоаϿһ of a Multiple Model Observer (MMO) is employed for the selection architecture within the supervisor. This notion, is a natural extension of the MMC design. Switching between the individual controllers is accomplished bumplessly by using a Multiple Model Bumpless Transfer Mechanism, thus producing a smooth and continuous control signal as the plant alters continuously. The design of the complete controller is segmented into two sections; Supervisory and Global Regulator Controllers, with bumpless transfer implemented within the regulatory controller design. All sections ensure stability and robustness in face of plant uncertainties.

Optimal modeling for complex system design [data compression

The article begins with a brief introduction to the theory describing optimal data compression systems and their performance. A brief outline is then given of a representative algorithm that employs these lessons for optimal data compression system design. The implications of rate-distortion theory for practical data compression system design is then described, followed by a description of the tensions between theoretical optimality and system practicality and a discussion of common tools used in current algorithms to resolve these tensions. Next, the generalization of rate-distortion principles to the design of optimal collections of models is presented. The discussion focuses initially on data compression systems, but later widens to describe how rate-distortion theory principles generalize to model design for a wide variety of modeling applications. The article ends with a discussion of the performance benefits to be achieved using the multiple-model design algorithms.