18 research outputs found

    Simulation and optimisation of the controls of the stock preparation area of a paper machine.

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    Thesis (M.Sc.Eng.)-University of KwaZulu-Natal, Durban, 2004.At Mondi Paper Ltd, Merebank, South of Durban, Paper Machine 2 has recently been transferred onto a Distributed Control System (DCS). This was seen as a good opportunity to enhance the control of the pulp feed to the machine. A prime concern in operating a paper machine is to ensure consistent set-point paper properties in the Cross-Direction (CD: ie. across the paper width) and in the Machine-Direction (MD: ie. along the paper length). Sophisticated adjustments are available to ensure an even feed of the stock (consistencies around 2% m/m wood fibres in water) from the head-box across the receiving width of the paper machine. The properties of prime interest as the pulp is pumped through the head-box distributor onto the receiving belt of the machine are the basis weight (fibre mass per unit area) and moisture content (per unit area). However, the distribution system is highly dependent on the properties of the stock as it arrives at the head-box. Variations in upstream chest levels, the supplied pressure, flow-rate and fibre/water ratio, all cause MD and even CD variations. The problems of maintaining steady conditions at the head-box are well known, and are understood to arise from sub-optimal control in the preceding section involving a blend chest and machine chest, amongst other items, where several pulp streams and dilution water are combined. A number of control loops are involved, but appear to require different tuning for different paper grades. Often individual loops are taken off-line. In this study, an understanding of the controller interactions in the stock preparation section has been developed by detailed dynamic modelling, including all of the existing control loops. The model is built up in a modular fashion using a basic element, having one input (which can collect multiple streams originating elsewhere) and four outputs, linked through a vessel of variable volume. Several basic elements are linked together to form the overall system. All of the necessary properties can be defined so that the model allows the simulation of all features of the network: vessels, pipes, junctions, valves, levels and consistencies. A set of first order differential equations is solved which includes total water balance, species mass balances, derivatives of flow controller action, and derivatives of supervisory controller action. Supervisory controllers for consistency or level cascade onto flow controllers. Flow controllers manipulate valves which give a first-order dynamic response of actual flow. Where valves are manipulated directly by the supervisory level, the flow controller is effectively bypassed. This study involves a constraint problem around the blend chest, resulting in a loss of specification at the paper machine. This was solved by the implementation of a static optimiser. Its objective function penalizes deviations from setpoint of five parameters (ratios, consistency and level) using respective weight factors. Both the model and its optimiser were included in a simulator designed with the graphical user interface (GUI) of Matlab. The simulator has then been used to explore control performance over the operating range, by means of a set of scenarios

    Multivariable Adaptive Control Design Under Internal Model Control Structure.

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    A new adaptive multivariate control scheme has been devised. The method combines the best characteristics of conventional adaptive systems and internal model control (IMC) structure. The control scheme builds by itself the required models and avoids the ambiguities in the definition of performance specifications. The problem of plant inversion associated with the IMC structure has been solved. The method introduced in this work is based on the properties of the Smith-McMillan form. However, the method does not require the explicit determination of the form. Furthermore, the computation of a stable plant inverse requires only matrix inversion and scalar polynomial factorization. The resulting algorithm is suitable for on-line operation. The control schemed is implemented through the following stages: (1) Identification. The parameters of a multivariable ARX model are estimated using a recursive least square algorithm with variable forgetting factor. The input and output orders can be used as additional degrees of freedom. The algorithm developed shows good numerical characteristics with fast convergence even for a large number of parameters. (2) Computation of the manipulated variables. The model is used to determine a controller following the IMC approach. The resulting equations are solved to compute the required manipulated variables. The algorithm for system inversion allows computations to be executed on-line. (3) Filtering. The usual filters of the IMC approach are also used in the adaptive scheme. The objective is to reduce the sensitivity of the controller. Only non-adaptive non-interactive filters have been considered. The results with first order low pass filters are satisfactory. The bandwidth of the filter is used as an additional tuning parameter. The adaptive control strategy has been extensively tested using computer simulation. The tests include extensions to non-linear plants. Comparisons with non-adaptive IMC control show the advantage of the new scheme developed in this work

    Directional Change Issues in Multivariable State-Feedback Control

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    Robust model predictive control for linear systems subject to norm-bounded model Uncertainties and Disturbances: An Implementation to industrial directional drilling system

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    Model Predictive Control (MPC) refers to a class of receding horizon algorithms in which the current control action is computed by solving online, at each sampling instant, a constrained optimization problem. MPC has been widely implemented within the industry, due to its ability to deal with multivariable processes and to explicitly consider any physical constraints within the optimal control problem in a straightforward manner. However, the presence of uncertainty, whether in the form of additive disturbances, state estimation error or plant-model mismatch, and the robust constraints satisfaction and stability, remain an active area of research. The family of predictive control algorithms, which explicitly take account of process uncertainties/disturbances whilst guaranteeing robust constraint satisfaction and performance is referred to as Robust MPC (RMPC) schemes. In this thesis, RMPC algorithms based on Linear Matrix Inequality (LMI) optimization are investigated, with the overall aim of improving robustness and control performance, while maintaining conservativeness and computation burden at low levels. Typically, the constrained RMPC problem with state-feedback parameterizations is nonlinear (and nonconvex) with a prohibitively high computational burden for online implementation. To remedy this issue, a novel approach is proposed to linearize the state-feedback RMPC problem, with minimal conservatism, through the use of semidefinite relaxation techniques and the Elimination Lemma. The proposed algorithm computes the state-feedback gain and perturbation online by solving an LMI optimization that, in comparison to other schemes in the literature is shown to have a substantially reduced computational burden without adversely affecting the tracking performance of the controller. In the case that only (noisy) output measurements are available, an output-feedback RMPC algorithm is also derived for norm-bounded uncertain systems. The novelty lies in the fact that, instead of using an offline estimation scheme or a fixed linear observer, the past input/output data is used within a Robust Moving Horizon Estimation (RMHE) scheme to compute (tight) bounds on the current state. These current state bounds are then used within the RMPC control algorithm. To reduce conservatism, the output-feedback control gain and control perturbation are both explicitly considered as decision variables in the online LMI optimization. Finally, the aforementioned robust control strategies are applied in an industrial directional drilling configuration and their performance is illustrated by simulations. A rotary steerable system (RSS) is a drilling technology that has been extensively studied over the last 20 years in hydrocarbon exploration and is used to drill complex curved borehole trajectories. RSSs are commonly treated as dynamic robotic actuator systems, driven by a reference signal and typically controlled by using a feedback loop control law. However, due to spatial delays, parametric uncertainties, and the presence of disturbances in such an unpredictable working environment, designing such control laws is not a straightforward process. Furthermore, due to their inherent delayed feedback, described by delay differential equations (DDE), directional drilling systems have the potential to become unstable given the requisite conditions. To address this problem, a simplified model described by ordinary differential equations (ODE) is first proposed, and then taking into account disturbances and system uncertainties that arise from design approximations, the proposed RMPC algorithm is used to automate the directional drilling system.Open Acces

    RELIABLE ROBUST CONTROLLER FOR HALF-CAR ACTIVE SUSPENSION SYSTEMS BASED ON HUMAN-BODY DYNAMICS

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    The paper investigates a non-fragile robust control strategy for a half-car active suspension system considering human-body dynamics. A 4-DoF uncertain vibration model of the driver’s body is combined with the car’s model in order to make the controller design procedure more accurate. The desired controller is obtained by solving a linear matrix inequality formulation. Then the performance of the active suspension system with the designed controller is compared to the passive one in both frequency and time domain simulations. Finally, the effect of the controller gain variations on the closed-loop system performance is investigated numerically

    Process Control 1971-1972 : Final Report Project

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    Controle de pH em máquina de produção de cartão multicamada

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    Orientador : Prof. Dr. Marcelo Kaminski LenziCo-Orientador: Prof. Dr. Ivo NeitzelDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Química. Defesa: Curitiba, 31/08/2010Bibliografia: fls. 45-48Resumo: O pH é uma das variáveis de maior importância para a fabricação de papel, devido à sua enorme influência na química da parte úmida e na formação da folha. O controle desta variável resulta em produção de papel com baixa variabilidade e em consequência mais facilidade para atender a qualidade requerida. Devido às dificuldades intrínsecas do processo, como não linearidade e à sua natureza estocástica, o desenvolvimento de modelos fundamentais para controle de processo torna-se uma tarefa complexa. Assim, usando os resultados da resposta a uma excitação em degrau do controlador PI em laço fechado foi possível obter o modelo do comportamento dinâmico do processo. Diversas abordagens foram usadas para a obtenção do modelo matemático, mas a que mais se destacou foi a abordagem em que foi considerada o equacionamento na variável tempo, utilizando o critério ISE (Integral do Quadrado do Erro) como critério de avaliação. É importante mencionar que foram consideradas duas alternativas para representar a mudança de setpoint, um degrau ideal e o caso real, uma sequência de degraus (staircase). A partir do modelo identificado para o processo, foram realizados estudos de simulação visando avaliar a sintonia do controlador e o impactoeconômico por ajuste dos parâmetros proporcional e integral. Desta forma, mostrouse que a escolha adequada dos parâmetros do controlador, afeta de maneira decisiva a variabilidade da resposta da malha de controle, bem como sua estabilidade e o tempo necessário para atingir o setpoint. Finalmente, é de suma importância ressaltar que os resultados indicam uma melhora no desempenho da malha de controle avaliado através critério ISE e que o ganho econômico pode chegar a 88%.Abstract: Due to its influence on wet end chemistry, pH plays a key role in paper manufacturing. Therefore, the control of this variable results in production with low variability and as consequence high paperboard quality. However, because of inherent manufacturing difficulties, such as process nonlinearities and its stochastic nature, the derivation of fundamental models for control purposes still remains a complex task. Consequently, by using closed-loop-based identification techniques, consisting on the dynamic behavior of a closed PI pH control loop a mathematical model was obtained. Different approaches were considered for modeling issues; however, the best fit was obtained by using a mathematical model based on a continuous time independent variable, using the ISE (Integral Square Error) as evaluation criterion. It is worth mentioning that the setpoint change was modeled in two ways, such as step and staircase. With the aid of the identified process model, simulations were carried out in order to evaluate the controller tuning parameters and also its economic impact on the control loop performance. This way, is could be successfully shown that the choice of controller parameters directly influences the control loop variance and the required time to reach a new setpoint. Finally, it should be stressed that the obtained results indicate an improvement of roughly 88% on the economic parameter evaluated

    Control Performance Monitor and a Model Performance Monitor for Process Controllers

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    Chemical Engineerin

    New Approaches in Automation and Robotics

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    The book New Approaches in Automation and Robotics offers in 22 chapters a collection of recent developments in automation, robotics as well as control theory. It is dedicated to researchers in science and industry, students, and practicing engineers, who wish to update and enhance their knowledge on modern methods and innovative applications. The authors and editor of this book wish to motivate people, especially under-graduate students, to get involved with the interesting field of robotics and mechatronics. We hope that the ideas and concepts presented in this book are useful for your own work and could contribute to problem solving in similar applications as well. It is clear, however, that the wide area of automation and robotics can only be highlighted at several spots but not completely covered by a single book
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