Model structure selection using an integrated forward orthogonal search algorithm assisted by squared correlation and mutual information

Model structure selection plays a key role in non-linear system identification. The first step in non-linear system identification is to determine which model terms should be included in the model. Once significant model terms have been determined, a model selection criterion can then be applied to select a suitable model subset. The well known Orthogonal Least Squares (OLS) type algorithms are one of the most efficient and commonly used techniques for model structure selection. However, it has been observed that the OLS type algorithms may occasionally select incorrect model terms or yield a redundant model subset in the presence of particular noise structures or input signals. A very efficient Integrated Forward Orthogonal Search (IFOS) algorithm, which is assisted by the squared correlation and mutual information, and which incorporates a Generalised Cross-Validation (GCV) criterion and hypothesis tests, is introduced to overcome these limitations in model structure selection

Model structure selection using an integrated forward orthogonal search algorithm interfered with squared correlation and mutual information

Model structure selection plays a key role in nonlinear system identification. The first step in nonlinear system identification is to determine which model terms should be included in the model. Once significant model terms have been determined, a model selection criterion can then be applied to select a suitable model subset. The well known orthogonal least squares type algorithms are one of the most efficient and commonly used techniques for model structure selection. However, it has been observed that the orthogonal least squares type algorithms may occasionally select incorrect model terms or yield a redundant model subset in the presence of particular noise structures or input signals. A very efficient integrated forward orthogonal searching (IFOS) algorithm, which is interfered with squared correlation and mutual information, and which incorporates a general cross-validation (GCV) criterion and hypothesis tests, is introduced to overcome these limitations in model structure selection

Constrained Adaptive Inverse Control With Disturbances

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2007Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2007Kontrol Teorisinin amacı dinamik sistemin en doğru ve sağlam olarak istenilen şekile davranmasını sağlamaktır. Bu amaç, sistemin kararlı hale getirilmesi, kontrolü, ve sistemdeki gürültünün yok edilmesi olarak üç ana gruba ayrılabilir. Konvansiyonel kontrol sistemleri, lineer olmayan veya sistemin dinamiklerinin zamanla değiştiği durumlarda yetersiz kalmaktadırlar. Uyarlamalı ters kontrol metedolojisi bu tip sistemlerin kontrolünde kullanılabilir. Bu çalışmada lineer ve lineer olmayan sistemler kontrol edilmeye çalışılmıştır. Yapay Sinir Ağları ve Uyarlamalı FIR filtreler, Gradient-Descent tabanlı algoritmalarla eğitilmiş, sistemin modeli, kontrolorü ve gürültü yok edici olarak kullanılmıştır. Algoritma sistemin modelinin çıkarılmasına, kontrolörünün ve gürültü yok edicinin elde edilmesinde ayrı izin vermektedir. Kullanıcının belirlediği sınırlı kontrol de sağlanabilir.The aim of control theory is to force the dynamical system to behave in user specified manner as accurately, and as robust as possible. The aims may be separated into three parts; stabilizing the plant, controlling the plant and disturbance cancelling. Conventional control systems are not adequate in such as non linear or time varying dynamic in controlled system. Adaptive inverse control is a methodology, which achieves to control these kinds of systems. In this work both linear and nonlinear plants are tried to be controlled. Neural networks and FIR filters, which are trained by gradient-descent based algorithms, are used for modelling, control and disturbance cancelling. The algorithm allows separate implementation of the adaptive controller, plant model and disturbance canceller. General user specified constraints on the control effort may be satisfied.Yüksek LisansM.Sc

Estimation of biomass at innate errors metabolism institute (IEIM)

Este trabajo presenta el diseño y desarrollo de un estimador de biomasa para un Bioreactor con cepa de Pichia Pastoris. Se presentan alternativas No-lineales como lo son: Uno basado en modelos, donde se requirió la identificación del modelo y el respectivo diseño del filtro de Kalman. Y otro, un sensor virtual (Red Neuronal). Un problema resulto durante el desarrollo del trabajo fueron las dos frecuencias de muestreo diferentes presentes en el conjunto de datos para dos grupos de variables. Se muestran los diferentes resultados y análisis de simulación realizadosThis work presents the design and development of a Biomass estimator for a Pichia Pastoris Bioreactor. The nonlinear estimator alternatives presented in this article are: Model Based Estimator, which required the Model Identi?cation and its respective Kalman Filter design. And, Soft sensor (Neural Network). One issue solved during this work was the two different sample frequencies exhibited by the data training sets. Different simulation results and analysis are presented during the work.Magíster en Ingeniería ElectrónicaMaestrí

Identification of an Adaptative Model for an Articulated robot: A black-box approach

TCC(graduação) - Universidade Federal de Santa Catarina. Centro Tecnológico. Engenharia de Controle e Automação.O conceito emergente e cada vez mais consolidado da indústria 4.0 traz consigo em um de seus pilares a rápida adaptação aos anseios do mercado, sendo necessário prover cada vez mais produtos mais individualizados e customizados, fazendo da flexibilidade, uma das características chaves para prosperar nesse ambiente. Nesse contexto, a montagem final de produtos de larga escala, como por exemplo, na indústria automotiva, apresenta um ambiente bastante flexível devido ao baixo número de produtos e a imensa gama de variações dos mesmos. Essa flexibilidade, porém, é obtida ao custo de um baixo nível de automação no ambiente da montagem final. Manipuladores robóticos apresentam-se como elementos bastante flexíveis: apresentam alto grau de liberdade de movimentação e capazes de atuar na execução das mais diversas tarefas. Tradicionalmente, estes são empregados em um layout celular que permitem um alto grau de versatilidade. A fim de se diminuir o ciclo de tempo da montagem final nas linhas de produção, cada vez mais, opta-se por um layout de fluxo contínuo, ininterrupto, capaz de reduzir em mais de 63% os tempos da montagem. Assim, o Werkzeugmaschinenlabor -WZL (Laboratório de Máquinas-Ferramenta) da Universidade Técnica da Renânia do Norte-Vestefália em Aachen através do projeto FASIM - Final Assembly in Motion (Montagem Final em Movimento) busca solucionar toda a problemática envolvida na sincronização dos manipuladores robóticos, com o restante dos componentes da linha de produção em um ambiente de movimento contínuo. Através de diversos trabalhos envolvendo anos de pesquisa, o laboratório optou por uma sincronização realizada através de um controle preditivo (Model Predictive Control - MPC) capaz de: garantir a sincronização requerida ao passo que compensa interferências das vibrações do sistema de movimentação e; lidar com o tempo de zona morta proveniente da comunicação entre sistema de controle, manipuladores robóticos e o sistema de medição. Como qualquer abordagem de controle clássica, para um devido ajuste e um bom resultado do sistema de controle, é preciso antes de mais nada um bom modelo que represente o sistema. Durante as etapas mais recentes do projeto, o modelo do sistema foi obtido através de uma estrutura caixa-preta utilizando a captação de dados reais de entrada e saída do sistema. Esse trabalho se propõe, então, a identificar um modelo de um manipulador robótico, acoplado ao sistema de medição de larga escala, através de uma abordagem caixa-preta, que gere resultados mais próximos ao sistema real que o modelo até então obtido pelo WZL. O trabalho se centrou em pesquisar diversas técnicas de identificação e possíveis ferramentas de implementação que pudesse proporcionar uma integração rápida ao ambiente do laboratório. Indo desde identificação usando-se de técnicas de aprendizado de máquina, otimização a estimação online de parâmetros do sistema. Visou-se estudar a possibilidade de identificação de um modelo adaptativo capaz de aproximar a dinâmica do sistema real em pose do laboratório a fim de melhorar os resultados do controle projetado pelo mesmo.The growing desire for more individualized products requires from the industry a high degree of flexibility and shorter production times. In this context, in order to achieve the required quality and time standards in the final assembly in the automotive industry, the process is done through a high degree of manual work in continuous assembly line. Seeking to create a more automated production environment while maintaining the same levels of flexibility and quality, the Werkzeugmaschinenlabor -WZL through the FASIM (Final Assembly in Motion) project, studies the possibility of employing robotic manipulators synchronized with the movement of the product in the continuous production line. Synchronization is performed through a model predictive control (MPC) capable of compensating for deviations of the manipulator system and conveyor system while, rejecting system's disturbances and dealing with the dead-time delays from the robot and measurement system . In order for the control to have an adequate behavior, it needs a good model of the system. Thus, this work aims to study methods and tools capable of providing a more accurate model than the current one in the possession of the laboratory. Several methods and tools were researched, which could provide an adaptive model for the robotic system. It focused on evaluating the possibility of implementing a neural network model and the implementation of an online estimator of system parameters

An improved iterative real-time optimization scheme for slow processes

Iterative Real-Time Optimization (RTO) has gained increasing attention in the context of model-based optimization of the operating points of chemical plants in the presence of plant-model mismatch. In all these schemes, it is necessary to wait for the plant having reached a steady-state to obtain the required information on plant performance and constraint satisfaction, which leads to slow convergence in the case of processes with slow dynamics. This works addresses this issue by considering both parametric, and structural plant-model mismatch. First, a simple approach to determine the type of plant-model mismatch with the use of transient data is discussed. An approach for dealing with parametric mismatch based on a sensitivity analysis of the nominal dynamic model is presented, and its performance is evaluated with the case-study of a Continuously Stirred Tank Reactor (CSTR), where fast convergence to the optimum can be obtained, even with noisy measurements. For the case of structural mismatch, nonlinear system identification is integrated with iterative RTO. The identified models are used to predict the steady-state of the system, thus reducing the total optimization time. The performance of the strategy is illustrated by simulation studies of a CSTR and a hydroformylation process. It is shown that a mixed scheme, where both a linear and nonlinear model are used for steady-state prediction, results in fast convergence to a neighborhood of the true optimum, even in the presence of measurement noise. The use of taylored nonlinear models for dynamic system identification is shown to be a promising approach for reducing the time necessary to reach the optimum of a process

Polynomial Nonlinear State Space Identification of an Aero-Engine Structure

Most nonlinear identification problems often require prior knowledge or an initial assumption of the mathematical law (model structure) and data processing to estimate the nonlinear parameters present in a system, i.e. they require the functional form or depend on a proposition that the measured data obey a certain nonlinear function. However, obtaining prior knowledge or performing nonlinear characterisation can be difficult or impossible for certain identification problems due to the individualistic nature of practical nonlinearities. For example, joints between substructures of large aerospace design frequently feature complex physics at local regions of the structure, making a physically motivated identification in terms of nonlinear stiffness and damping impossible. As a result, black-box models which use no prior knowledge can be regarded as an effective method. This paper explores the pragmatism of a black-box approach based on Polynomial Nonlinear State Space (PNLSS) models to identify the nonlinear dynamics observed in a large aerospace component. As a first step, the Best Linear Approximation (BLA), noise and nonlinear distortion levels are estimated over different amplitudes of excitation using the Local Polynomial Method (LPM). Next, a linear state space model is estimated on the non-parametric BLA using the frequency domain subspace identification method. Nonlinear model terms are then constructed in the form of multivariate polynomials in the state variables while the parameters are estimated through a nonlinear optimisation routine. Further analyses were also conducted to determine the most suitable monomial degree and type required for the nonlinear identification procedure. Practical application is carried out on an Aero-Engine casing assembly with multiple joints, while model estimation and validation is achieved using measured sine-sweep and broadband data obtained from the experimental campaign