Orientador: Francisco Maugeri Filho / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia de Alimentos / Made available in DSpace on 2018-08-04T02:36:57Z (GMT). No. of bitstreams: 1 Atarassi_MiltonMassahiro_M.pdf: 930809 bytes, checksum: 1ff313d204727a6756c4b1294579393f (MD5) Previous issue date: 2005 / Mestrado / Engenharia de Alimentos / Mestre em Engenharia de Alimentos

Dans une large variété d'applications industrielles, il existe une demande croissante pour améliorer la qualité de l'énergie fournie par les systèmes électriques. En plus de la fiabilité et de la disponibilité d'énergie électrique, la qualité de la puissance fournie devient maintenant une question importante. Parmi les causes de la pauvre qualité de puissance, les harmoniques sont considérés comme la raison qui contribue à la majorité de pannes de courant. Beaucoup d'efforts ont été développés pour résoudre le problème des perturbations harmoniques comme, par exemple, installer des dispositifs spéciaux tels que les filtres actifs. Ce travail de thèse traite le développement d’une commande directe de puissance utilisant l'approche prédictive hybride. La commande hybride considère chaque vecteur de tension du convertisseur comme une entité discrète qui sera appliquée pour commander un système linéaire continu. Un critère pour calculer le vecteur optimal de tension à appliquer sera établi à partir d’un modèle prédictif. Le vecteur optimal de tension à appliquer pour chaque période de commutation, et le correspondant temps d'application seront utilisés pour approcher la valeur réelle des variables d'état du système au point de référence désiré. Deux théories de puissance instantanées seront employées, p-q et p-q-r, pour une application de filtre active parallèle de puissance dans un système triphasé de 4 fils. Ces théories instantanées de puissance ont été développées pour être appliquées aux systèmes non équilibrés utilisant les variables de puissance pour obtenir les courants qui devraient être injectés par le filtre actif. Le filtre actif produira la puissance réactive demandée par la charge et compensera la composante d'ondulation de la puissance active de sorte que la source livre seulement la puissance active constante. / In a wide variety of industrial applications, an increasing demand exists to improve the quality of the energy provided by electrical systems. Besides the reliability and availability of electric power, the power quality is now becoming an important issue. Among the causes of the poor power quality, the harmonics are included as the reason which contributes the majority of power failures. Many efforts have been developed to solve the harmonics problem as, for instance, to install special devices such as active filters. This research work deals with the development of direct power control using the hybrid predictive control approach. The hybrid control considers each voltage vector of the converter as a discrete entity which will be applied to control a continuous linear system. One criterion to calculate the optimal voltage vector to apply will be established for the predictive control model. The optimal voltage vector to apply for each switching period, and the corresponding application time will be used to approach the actual value of the state variables of the system to the desired reference point. Two instantaneous power theories will be used, i.e. pq0 and pqr instantaneous power theory for a shunt active power filter application implemented in 3-phase 4-wire system. These instantaneous power theories have been developed to be applied to unbalanced systems using the power variables to obtain the currents that should be injected from active filters. The active filter will produce the required reactive power for the load and compensate the ripple component of active power so that the source only delivers constant active power.

In industrial fuel gas preparation, there are several compositional properties that must be controlled within specified limits. This allows client plants to use the fuel gas mixture safely without having to adjust and control the composition themselves. The variables to be controlled are the Higher Heating Value (HHV), Wobbe Index (WI), Flame Speed Index (FSI), and Pressure (P). These variables are controlled by adjusting the volumetric flow rates of several inlet gas streams of which some are makeup streams (always available) and some are wild streams that vary in composition and availability (by-products of plants). The inlet streams need to be adjusted in the correct ratios to keep all the controlled variables (CVs) within limits while minimising the cost of the gas blend. Furthermore, the controller needs to compensate for fluctuations in inlet stream compositions and total fuel gas demand (the total discharge from the header). This dissertation describes the modelling and model validation of an industrial fuel gas header as well as a simulation study of three different Model Predictive Control (MPC) strategies for controlling the system while minimising the overall operating cost. / Dissertation (MEng)--University of Pretoria, 2011. / Electrical, Electronic and Computer Engineering / unrestricted

This dissertation addresses the problem of optimally scheduling the hoists of a twin rock winder system in a demand side management context. The objective is to schedule the hoists at minimum energy cost taking into account various physical and operational constraints and production requirements as well as unplanned system delays. The problem is solved by first developing a static linear programming model of the rock winder system. The model is built on a discrete dynamic winder model and consists of physical and operational winder system constraints and an energy cost based objective function. Secondly a model predictive control based scheduling algorithm is applied to the model to provide closed-loop feedback control. The scheduling algorithm first solves the linear programming problem before applying an adapted branch and bound integer solution methodology to obtain a near optimal integer schedule solution. The scheduling algorithm also compensates for situations resulting in infeasible linear programming solutions. The simulation results show the model predictive control based scheduling algorithm to be able to successfully generate hoist schedules that result in steady state solutions in all scenarios studied, including where delays are enforced. The energy cost objective function is proven to be very effective in ensuring minimal hoisting during expensive peak periods and maximum hoisting during low energy cost off-peak periods. The algorithm also ensures that the hoist target is achieved while controlling all system states within or around their boundaries for a sustainable and continuous hoist schedule. Copyright / Dissertation (MEng)--University of Pretoria, 2010. / Electrical, Electronic and Computer Engineering / Unrestricted

This thesis presents a robust nonlinear model predictive controller (RNMPC), nominal nonlinear model predictive controller (NMPC) and single-loop proportional-integral-derivative (PID) controllers that are applied to a nonlinear model of a run-of-mine (ROM) ore milling circuit. The model consists of nonlinear modules for the individual process units of the milling circuit (such as the mill, sump and cyclone), which allow arbitrary milling circuit configurations to be modelled easily. This study aims to cast a complex problem of a ROM ore milling circuit into an RNMPC framework without losing the flexibility of the modularised nonlinear model and implement the RNMPC using open-source software modules. The three controllers are compared in a simulations study to determine the performance of the controllers subject to severe disturbances and model parameter variations. The disturbances include changes to the feed ore hardness, changes in the feed ore size distributions and spillage water being added to the sump. The simulations show that the RNMPC and NMPC perform better than the PID controllers with regard to the economic objectives, assuming full-state feedback is available, especially when actuator constraints become active. The execution time of the RNMPC, however, is much too long for real-time implementation and would require further research to improve the efficiency of the implementation. / Thesis (PhD)--University of Pretoria, 2009. / Electrical, Electronic and Computer Engineering / unrestricted

Starting a few decades ago, the unmanned wheeled vehicle research has drawn lately more attention, especially for off-road environment. As the demand to use electric vehicles increased, the need to conceptualize the use of electrically driven vehicles in autonomous operations became a target. That is because in addition to the fact that they are more environmentally friendly, they are also easier to control. This also gives another reason to enhance further the energy economy of those unmanned electric vehicles. Off-road vehicles research was always challenging, but in the present work the nature of the off-road land is utilized to benefit from in order to enhance the energy consumption of those vehicles. An algorithm for energy consumption optimization for electrically driven unmanned wheeled vehicles is presented. The algorithm idea is based on the fact that in off-road conditions, when the vehicle passes a ditch or a hole, the kinetic energy gained while moving downhill could be utilized to reduce the energy consumption for moving uphill if the dimensions of the ditch/hole were known a distance ahead. Two manipulated variables are evaluated: the wheels DC motors supply voltage and the DC armature current. The developed algorithm is analysed and compared to the PID speed iii controller and to the open-loop control of DC motors. The developed predictive controller achieved encouraging results compared to the PID speed control and also compared to the open-loop control. Also, the use of the DC armature current as a manipulated variable showed more noticeable improvement over using the DC input voltage. Experimental work was carried out to validate the predictive control algorithm. A mobile robot with two DC motor driven wheels was deployed to overcome a ditch-like hindrance. The experimental results verified the simulation results. A parametric study for the predictive control is conducted. The effect of changing the downhill angle and the uphill angle as well as the size of the prediction horizon on the consumed electric energy by the DC motors is addressed. The simulation results showed that, when using the proposed approach, the larger the prediction horizon, the lower the energy consumption is.

The need to save energy, cut costs, and increase profit margin in process manufactureincreases continually. There is also a global drive to reduce energy use and cut down co2 emission and combat climate change. These in turn have led to more stringent requirements on process control performance. Hence, the requirements for modern systems are often not achievable using classical control techniques. Therefore, advanced control strategies are often required to ensure optimal process performance. Despite these challenges, PID has continued to be the dominant industrial control scheme. However, for systems with complex dynamics and/or high performance requirements, PID control may not be sufficient. Therefore, a significant number of industrial control loops are not performing optimally and more advanced control than PID may be required in order to achieve optimal performance. MPC is one of the advanced control schemes that has had a significant impact in the industry. Despite the benefits associated with the implementation of MPC, the technology has remained a niche application in process manufacture. This thesis seeks to address these issues by developing ways that could lead to widespread application of MPC. In the first part of this thesis, a study was carried out to understand the characteristics of processes that would benefit from the application of MPC at the regulatory control level even in the single-input single-output (SISO) case. This is a departure from the common practice in which MPC is applied at the supervisory control layer delivering set points to PID controllers at the regulatory control layer. Both numerical simulation and industrial studies were used to show and quantify benefits of MPC for SISO applications at the regulatory control layer. Some issues that have led to the limited application of MPC include the cost and human efforts associated with modelling and controller design. And to achieve high process performance, accurate models are required. To address this issue, in the second part of this thesis, a novel technique for designing MPC from routine plant data – routine data MPC (RMPC) is proposed. The proposed technique was successfully implemented on process models. This technique would reduce the high human cost associated with MPC deployment, which could make it a widespread rather than niche application in the process manufacturing industry.

Actuator saturation is ubiquitous in physical plants. In closed-loop systems limits imposed on the actuators may result in degraded performance of the control law and, ultimately, instability of the system. When other non-linearities, such as deadzone, backlash or stiction, are also present in a system’s input, the analysis and design procedures become more involved. The core of this thesis is a new structure based on the right inverse approach for deadzone and backlash, which is extended to linear plants that exhibit a combination of saturation and either deadzone, backlash or stiction, in the actuator. It is shown that, for this type of system, the inclusion of the right inverse nonlinearity results in the linear plant being subject to a new input saturation. Then, one can design standard controllers such as anti-windup or input constrained MPC around this saturation. This simplifies the analysis and design processes, in spite of the presence of complex nonlinearities. The results for deadzone and backlash are extended to stiction by proposing an approximate stiction nonlinearity, and then introducing a right inverse to this approximation. It is demonstrated that the systems studied can be compensated by a standard input constrained MPC which can be solved by a convex quadratic program. Additionally, a simple anti-windup structure is used to demonstrate the applicability of the proposed structure using existing control strategies.

A run-of-mine ore milling circuit is primarily used to grind incoming ore containing precious metals to a particle size smaller than a specification size. A traditional run-of-mine (ROM) ore single-stage closed milling circuit comprises of the operational units: mill, sump and cyclone. These circuits are difficult to control because of significant nonlinearities, large time delays, large unmeasured disturbances, process variables that are difficult to measure and modelling uncertainties. A nonlinear model predictive controller with state estimation could yield good control of the ROM ore milling circuit despite these difficulties. Additionally, the ROM ore milling circuit is an energy intensive unit and a controller or power optimizer could bring significant cost savings. A nonlinear model predictive controller requires good state estimates and therefore a neural network for state estimation as an alternative to the particle filter has been addressed. The neural network approach requires fewer process variables that need to be measured compared to the particle filter. A neural network is trained with three disturbance parameters and used to estimate the internal states of the mill, and the results are compared with those of the particle filter implementation. The neural network approach performed better than the particle filter approach when estimating the volume of steel balls and rocks within the mill. A novel combined neural network and particle filter state estimator is presented to improve the estimation of the neural network approach for the estimation of volume of fines, solids and water within the mill. The estimation performance of the combined approach is promising when the disturbance magnitude used is smaller than that used to train the neural network. After state estimation was addressed, this work targets the implementation of a nonlinear controller combined with full state estimation for a grinding mill circuit. The nonlinear controller consists of a suboptimal nonlinear model predictive controller coupled with a dynamic inversion controller. This allows for fast control that is asymptotically stable. The nonlinear controller aims to reconcile the opposing objectives of high throughput and high product quality. The state estimator comprises of a particle filter for five mill states as well as an additional estimator for three sump states. Simulation results show that control objectives can be achieved despite the presence of noise and significant disturbances. The cost of energy has increased significantly in recent years. This increase in price greatly affects the mineral processing industry because of the large energy demands. A run-of-mine ore milling circuit provides a suitable case study where the power consumed by a mill is in the order of 2 MW. An attempt has been made to reduce the energy consumed by the mill in the two ways: firstly, within the nonlinear model predictive control in a single-stage circuit configuration and secondly, running multiple mills in parallel and attempting to save energy while still maintaining an overall high quality and good quantity. A formulation for power optimization of multiple ROM ore milling circuits has been developed. A first base case consisted not taking power into account in a single ROM ore milling circuit and a second base case split the load and throughput equally between two parallel milling circuits. In both cases, energy can be saved using the NMPC compared to the base cases presented without significant sacrifice in product quality or quantity. The work presented covers three topics that has yet to be addressed within the literature: a neural network for mill state estimation, a nonlinear controller with state estimation integrated for a ROM ore milling circuit and power optimization of a single and multiple ROM ore milling circuit configuration. / Dissertation (MEng)--University of Pretoria, 2015. / Electrical, Electronic and Computer Engineering / Unrestricted

Orientadores: João Mauricio Rosário, Didier Dumur / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica / Made available in DSpace on 2018-08-22T07:43:59Z (GMT). No. of bitstreams: 1 RinconArdila_LizKatherine_D.pdf: 34773968 bytes, checksum: a0a6769d12cd82525097b60b095e82b4 (MD5) Previous issue date: 2013 / Resumo: Esta tese de doutorado apresenta o estudo do comportamento dinâmico para máquinas-ferramenta CNC (Controle Numérico Computadorizado) com ênfase na implementação do sistema de controle, a fim de melhorar o desempenho desse dispositivo, possibilitando assim a obtenção de maiores velocidades de funcionamento com precisão de operação. O estudo do comportamento dinâmico do sistema foi baseado na modelagem dinâmica do dispositivo CNC, através de um modelo tipo MIMO (Múltiplas Entradas- Múltiplas Saídas), não linear, invariante no tempo, constituído pelo modelo do sistema mecânico, elétrico, eletrônico e controle. O modelo é fundamentado no estudo dos torques estático, dinâmico e de perturbações, incluindo a dinâmica não linear ocorrida pelos efeitos de atrito dos componentes, variação inercial e forças de perturbação. O modelo final é definido mediante a aplicação de uma estratégia proposta de identificação e estimação de parâmetros dinâmicos do dispositivo CNC real. A estratégia é baseada na estimação de parâmetros através de modelos de referência e método de otimização não linear. O sistema de controle proposto é constituído pelos níveis de geração de movimentos, controle e compensação, diagnóstico e otimização. O nível de controle utiliza estratégias de controle PID, controle preditivo generalizado (GPC) e controle preditivo robusto (GPCR). O nível de compensação é configurado pelos controladores em feedforward utilizando modelos de atrito e variação inercial. Finalmente, a validação e testes são realizados inicialmente através da implementação de um simulador virtual de dispositivo CNC, com posterior validação experimental em um dispositivo com arquitetura de supervisão e controle aberto disponível no Laboratório de Automação e Robótica da UNICAMP / Abstract: This doctoral thesis presents the dynamic behavior for CNC (Computer Numerical Control) Machine Tool with emphasis on the control system, for the purpose of improving the machine's performance, by obtaining high operation's velocity with precision. This study was based on the dynamic modeling by means of MIMO (multiple-input and multiple-output), nonlinear, and invariant time model, constituted by mechanical, electrical, electronics and control models. The model is based on the study of static, dynamic, and perturbation torques, and includes the nonlinear dynamic caused by the effects of friction in the components, inertial variation and perturbation's force. The final model is defined through the application of a strategy proposed by the identification and parameters estimation, in order to obtain the actual CNC machine's values. The strategy is formed by the parameters estimation by means of reference models and nonlinear optimization methods. The control architecture proposed is composed of the following levels: movement generator, control and compensation, diagnosis and optimization. The level of control applies strategies of PID control, generalized predictive control (GPC) and robust predictive control (GPCR). The level of compensation is configured by the feedforward controls, which utilize the friction and inertial variation models. Finally, the validation and tests are developed initially through the implementation of a virtual simulator of CNC machine tool, with the latter experimental validation in device with supervision and open architecture control, available in UNICAMP / Doutorado / Mecanica dos Sólidos e Projeto Mecanico / Doutora em Engenharia Mecânica