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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
381

Robust predictive control by zonotopic set-membership estimation. / Commande prédictive robuste par des techniques d'observateurs à base d'ensembles zonotopiques

Le, Vu tuan hieu 22 October 2012 (has links)
L’objectif de cette thèse est d’apporter des réponses à deux problèmes importants dans le domaine de l’automatique : l'estimation d'état et la commande prédictive robuste sous contraintes pour des systèmes incertains, en se basant sur des méthodes ensemblistes, plus précisément liées aux ensembles zonotopiques. Les incertitudes agissant sur le système sont modélisées de façon déterministe, elles sont donc inconnues mais bornées par des ensembles connus.Dans ce contexte, la première partie de la thèse développe une méthode d’estimation afin d’élaborer à chaque instant un ensemble zonotopique contenant l’état du système malgré la présence de perturbations, de bruits de mesure et d’incertitudes paramétriques définies par intervalle. Cette méthode est fondée sur la minimisation du P-rayon d’un zonotope, critère original permettant de caractériser la taille de l’ensemble zonotopique et réalisant un bon compromis entre la complexité et la précision de l’estimation. Cette approche est tout d’abord développée pour les systèmes mono-sortie, puis étendue au cas des systèmes multi-sorties, dans un premier temps par des extensions directes de la solution mono-sortie (le système multi-sorties est considéré comme plusieurs systèmes mono-sortie). Une autre solution est ensuite proposée, qui conduit à résoudre un problème d’optimisation de type Inégalités Matricielles Polynomiales en utilisant une méthode de relaxation. Les approches précédentes n’étant que des extensions de la solution à une seule sortie, et malgré leurs bons résultats obtenus en simulation, une démarche originale, dédiée aux systèmes multi-sorties, fondée sur l’intersection entre un polytope et un zonotope, est finalement développée et validée.La deuxième partie de la thèse aborde la problématique de la commande robuste par retour de sortie pour des systèmes incertains. La commande prédictive est retenue du fait de son utilisation dans de nombreux domaines, de sa facilité de mise en œuvre et de sa capacité à traiter des contraintes. Parmi les démarches issues de la littérature, l’implantation de techniques robustes fondées sur des tubes de trajectoire est développée plus spécifiquement. Le recours à un observateur ensembliste à base de zonotopes permet d’améliorer la qualité de l’estimation, ainsi que la performance de la commande, dans le cas de systèmes soumis à des perturbations et des bruits de mesure inconnus, mais bornés.Dans une dernière partie, cette combinaison de l’estimation ensembliste et de la commande prédictive robuste est testée en simulation sur un système de suspension magnétique. Les résultats de simulation traduisent un comportement tout à fait satisfaisant validant les structures théoriques élaborées. / The aim of this thesis is answering to two significant problems in the field of automatic control: the state estimation and the robust model predictive control for uncertain systems in the presence of input and state constraints, based on the set-membership approach, more precisely related to zonotopic sets. Uncertainties acting on the system are modeled via the deterministic approach, and thus they are unknown but bounded by a known set.In this context, the first part of the thesis proposes an estimation method to compute a zonotope containing the real states of the system, which are consistent with the disturbances, the measurement noise and the interval parametric uncertainties. This method is based on the minimization of the P-radius of a zonotope, which is an original criterion to characterize the size of the zonotope, in order to obtain a good trade-off between the complexity and the precision of the estimation. This approach is first developed for single-output systems, and then extended to the case of multi-output systems. The first solution for multi-output systems is a direct extension of the solution for single-output systems (the multi-output system being considered as several single-output systems). Another solution is then proposed, leading to solve a Polynomial Matrix Inequality optimization problem using a relaxation technique. Due to the fact that the previous approaches are just extensions of the solution for a single-output system, and despite their good performance results obtained in simulation, a novel approach dedicated to multi-output systems based on the intersection of a polytope and a zonotope is finally developed and validated.The second part of the thesis deals with the problem of robust output feedback control for uncertain systems. Model predictive control is chosen due to its use in many areas, its ability to deal with constraints and uncertainties. Among the approaches from the literature, the implementation of robust predictive techniques based on tubes of trajectories is developed. The use of a zonotopic set-membership estimation improves the quality of the estimation, as well as the performance of the control, for systems subject to unknown, but bounded disturbances and measurement noise.In the last part, the combination of zonotopic set-membership estimation and robust model predictive control is tested in simulation on a magnetic levitation system. The simulation results reflect a satisfactory behavior validating the developed theoretical techniques.
382

MPC adaptativo - multimodelos para controle de sistemas não-lineares. / MPC adaptive - multimodels for control of nonlinear systems.

Paula, Neander Alessandro da Silva 14 April 2009 (has links)
Durante a operação de um controlador MPC, a planta pode ir para outro ponto de operação principalmente pela decisão operacional ou pela presença de perturbações medidas/não-medidas. Assim, o modelo do controlador deve ser adaptado para a nova condição de operação favorecendo o controle sob as novas condições. Desta forma, as condições ótimas de controle podem ser alcançadas com a maior quantidade de modelos identificados e com um controlador adaptativo que seja capaz de selecionar o melhor modelo. Neste trabalho é apresentada uma metodologia de controle adaptativo com identificação on-line do melhor modelo o qual pertence a um conjunto previamente levantado. A metodologia proposta considera um controlador em duas camadas e a excitação do processo através de um sinal GBN na camada de otimização com o controlador em malha fechada. Está sendo considerada a validação deste controlador adaptativo através da comparação dos resultados com duas diferentes técnicas Controlador MMPC e Identificação ARX, para a comprovação dos bons resultados desta metodologia. / During the operation of a MPC, the plant can change the operation point mainly due to management decision or due to the presence of measured or unmeasured disturbances. Thus, the model of the controller must be adapted to improve the control in the new operation conditions. In such a way, a better control policy can be achieved if a large number of models are identified at the possible operation points and it is available an adaptive controller that is capable of selecting the best model. In this work is presented a methodology of adaptive control with on-line identification of the most adequate model which belongs to a set of models previously obtained. The proposed methodology considers a two-layer controller and process excitation by a GBN signal in the LP optimization layer with the controller in closed loop mode. It is also presented the adaptive controller validation by comparing the proposed approach with two different techniques - MMPC and ARX Identification, to confirm the good results with this new methodology to the adaptive controller.
383

Controle preditivo de horizonte infinito para sistemas integradores e com tempo morto. / Model predictive control of integrating systems with dead time.

Santoro, Bruno Faccini 11 March 2011 (has links)
Controle preditivo baseado em modelo (MPC) recebeu ampla aceitação na indústria química nos últimos 30 anos. O funcionamento básico dessa técnica é a utilização de um modelo para calcular o comportamento de uma planta em função das entradas que ela receberia nos próximos instantes. Define-se um objetivo, cuja principal contribuição é dada por uma medida da distância entre a condição predita da planta e um valor desejado previamente estipulado. Esse objetivo pode incluir ainda, por exemplo, penalizações sobre o esforço de controle necessário para levar a planta a uma condição mais próxima do desejável. São incorporadas restrições como limites físicos da planta e dos atuadores e formula-se um problema de otimização, buscando o ponto ótimo dessa função objetivo e respeitando as restrições. Neste trabalho é abordado o problema de controle preditivo baseado em modelo para sistemas que apresentem integradores e/ou tempos mortos. Estes elementos tornam mais difícil o controle de processos baseado apenas em técnicas clássicas. Apresenta-se aqui um modelo em espaço de estados que permite a representação dessas dinâmicas de modo suficientemente preciso. A formulação de modelo apresentada permite ainda a incorporação de informações sobre distúrbios medidos. É feita uma demonstração da estabilidade desse controlador quando o modelo por ele utilizado é idêntico ao comportamento real da planta. Numa aplicação real do controlador proposto, seria necessário estimar os estados da planta a partir das medidas das saídas. Em geral, utiliza-se um Filtro de Kalman para realizar esta tarefa. São estudados aqui os efeitos que a presença desse filtro teria sobre o desempenho do sistema em malha fechada. É proposto um observador baseado numa mudança heurística feita sobre o Filtro de Kalman e que permite, em certos casos, uma melhoria de desempenho. São apresentados os resultados de simulações de uma planta de óxido de etileno com o intuito de ilustrar a atuação do controlador estável desenvolvido e do observador proposto. / Model Predictive Control (MPC) has gained wide acceptance in chemical industry in the last 30 years. The basic principle of this technique is to use a model to calculate plants future behavior based on the inputs it would receive in the next sampling periods. It must be set an objective, mainly composed of some measure of the distance between plants predicted state and a previously specified condition. Objective value may also include, for example, penalty on control effort necessary to drive the plant closer to the desired state. It is possible to include constraints, such as physical limits of the plant or of the actuators and therefore to pose an optimization problem, searching the best value of the objective function that satisfies all constraints. This work addresses the problem of MPC applied to integrating systems and/or processes with dead-time. These kinds of plants are often difficult to control using only classical techniques. It is presented here a state space model to represent both cases accurately. Measured disturbances may also be incorporated to the model. Finally, it is shown that the proposed controller is stable when its internal model represents exactly plants dynamics. In any real application of this controller, it would be necessary to estimate plants states from outputs measures. In general, Kalman Filter solves this problem. It is studied in this work the effects caused by filters inclusion on closed loop performance. A new observer is proposed, based on a heuristic improvement over Kalman Filter which induces, for some systems, improved performance. Numerical simulation has been performed over a model of an ethylene oxide plant, illustrating the use of this stable controller and the proposed observer.
384

Coordinated, Multi-Arm Manipulation with Soft Robots

Kraus, Dustan Paul 01 October 2018 (has links)
Soft lightweight robots provide an inherently safe solution to using robots in unmodeled environments by maintaining safety without increasing cost through expensive sensors. Unfortunately, many practical problems still need to be addressed before soft robots can become useful in real world tasks. Unlike traditional robots, soft robot geometry is not constant but can change with deflation and reinflation. Small errors in a robot's kinematic model can result in large errors in pose estimation of the end effector. This error, coupled with the inherent compliance of soft robots and the difficulty of soft robot joint angle sensing, makes it very challenging to accurately control the end effector of a soft robot in task space. However, this inherent compliance means that soft robots lend themselves nicely to coordinated multi-arm manipulation tasks, as deviations in end effector pose do not result in large force buildup in the arms or in the object being manipulated. Coordinated, multi-arm manipulation with soft robots is the focus of this thesis. We first developed two tools enabling multi-arm manipulation with soft robots: (1) a hybrid servoing control scheme for task space control of soft robot arms, and (2) a general base placement optimization for the robot arms in a multi-arm manipulation task. Using these tools, we then developed and implemented a simple multi-arm control scheme. The hybrid servoing control scheme combines inverse kinematics, joint angle control, and task space servoing in order to reduce end effector pose error. We implemented this control scheme on two soft robots and demonstrated its effectiveness in task space control. Having developed a task space controller for soft robots, we then approached the problem of multi-arm manipulation. The placement of each arm for a multi-arm task is non-trivial. We developed an evolutionary optimization that finds the optimal arm base location for any number of user-defined arms in a user-defined task or workspace. We demonstrated the utility of this optimization in simulation, and then used it to determine the arm base locations for two arms in two real world coordinated multi-arm manipulation tasks. Finally, we developed a simple multi-arm control scheme for soft robots and demonstrated its effectiveness using one soft robot arm, and one rigid robot with low-impedance torque control. We placed each arm base in the pose determined by the base placement optimization, and then used the hybrid servoing controller in our multi-arm control scheme to manipulate an object through two desired trajectories.
385

Temperature Control in Friction Stir Welding Using Model Predictive Control

Taysom, Brandon Scott 01 June 2015 (has links)
Temperature is a very important process parameter in Friction Stir Welding (FSW), but until lately active control of temperature has not been practiced. Recently, temperature control via a PID controller has proven to be effective. Model Predictive Control (MPC) is a control method that holds promise, but has not been attempted in FSW before. Two different model forms are developed for MPC and are evaluated. The first is a simple first-order plus dead time (FOPDT) model. The second is the Hybrid Heat Source model and is more complex; it combines the heat source method and a 1D discretized thermal model of the FSW tool. Model parameters were determined by fitting model predictions to actual weld data. The models were evaluated for their performance in modeled and unmodeled disturbances once the process was already at a quasi steady state condition and also were evaluated for control immediately after plunge. The FOPDT based MPC controller has very good performance and was comparable in performance to previously proven and well-tuned PID controllers. For small modeled disturbances the FOPDT controller settled within 1°C of the setpoint in 10s with almost no oscillations and only 2°C of overshoot. For large unmodeled disturbances, the FOPDT controller settled within 1°C of the setpoint in 30s with no oscillations and 16°C of overshoot. For the same disturbances, the PID servo controller settled in 30s with no oscillations and 9°C of overshoot, and the PID regulator controller settled in 15s but had almost a full oscillation and 13°C of overshoot.The Hybrid Heat Source MPC controller and the PID regulator controller were also able to control temperature within 5°C of the setpoint immediately after the plunge during the highly transient portion of the weld, which previously had been assumed to be too difficult to control. The PID regulator controller had a high degree of variability between the two runs (a settling time of 10s and 30s, and .5 and 4.5 oscillations before settling), but settled quickly and once settled was able to hold the temperature within 2°C of the setpoint. The HHS MPC controller on the other hand had far fewer oscillations (0 and 1 oscillation) before settling, but could only hold the temperature within 5°C of the setpoint. Both of these controllers performed far better than the FOPDT MPC and PID servo controllers.
386

Genetic algorithm tuning of artificial pancreas MPC with individualized models

Sehlin, Olov January 2019 (has links)
Diabetes is a growing chronic disease and a worldwide problem. Without any available cure in sight for the public other methods needs to be applied to increase the life quality of diabetic patients. Artificial Pancreas (AP), a concept of having a closed loop system to control the glucose level on Type 1 Diabetes (T1D) patients has been introduced and is under development. In this thesis, Model Predictive Control (MPC) has been re implemented from scratch in MATLAB/SIMULINK with associated Kalman filter and prediction function. It was implemented in the latest version of the UVA/Padova Simulator which is a tool approved by FDA for simulating diabetes treatment in order to speed up the AP development. Different MPC cost functions where tested together with integral action on a simplified system using a linear approximation of a population model. It was implemented and tuned with a new simulation tuning method using Genetic Algorithm (GA). It showed that the quadratic cost function without integral action was the best with respect to performance and time efficiency. 3 hours was the best prediction horizon and was used for the individualized tuning using the University of Virginia (UVA)/Padova simulator. For the individualized MPC, models identified by the University of Padova were used. These simulations showed that an individualized model could be used for improved T1D treatment compared to an average population model even though the results were mixed. Almost all of the patients got improved treatment with the closed treatment and non hypoglycemic event occurred. The identification of better models is a great challenge for the future development of the AP MPC due to the excitation problems.
387

Smart Manufacturing Using Control and Optimization

Harsha Naga Teja Nimmala (6849257) 16 October 2019 (has links)
<p>Energy management has become a major concern in the past two decades with the increasing energy prices, overutilization of natural resources and increased carbon emissions. According to the department of Energy the industrial sector solely consumes 22.4% of the energy produced in the country [1]. This calls for an urgent need for the industries to design and implement energy efficient practices by analyzing the energy consumption, electricity data and making use of energy efficient equipment. Although, utility companies are providing incentives to consumer participating in Demand Response programs, there isn’t an active implementation of energy management principles from the consumer’s side. Technological advancements in controls, automation, optimization and big data can be harnessed to achieve this which in other words is referred to as “Smart Manufacturing”. In this research energy management techniques have been designed for two SEU (Significant Energy Use) equipment HVAC systems, Compressors and load shifting in manufacturing environments using control and optimization.</p> <p>The addressed energy management techniques associated with each of the SEUs are very generic in nature which make them applicable for most of the industries. Firstly, the loads or the energy consuming equipment has been categorized into flexible and non-flexible loads based on their priority level and flexibility in running schedule. For the flexible loads, an optimal load scheduler has been modelled using Mixed Integer Linear Programming (MILP) method that find carries out load shifting by using the predicted demand of the rest of the plant and scheduling the loads during the low demand periods. The cases of interruptible loads and non-interruptible have been solved to demonstrate load shifting. This essentially resulted in lowering the peak demand and hence cost savings for both “Time-of-Use” and Demand based price schemes. </p> <p>The compressor load sharing problem was next considered for optimal distribution of loads among VFD equipped compressors running in parallel to meet the demand. The model is based on MILP problem and case studies was carried out for heavy duty (>10HP) and light duty compressors (<=10HP). Using the compressor scheduler, there was about 16% energy and cost saving for the light duty compressors and 14.6% for the heavy duty compressors</p> <p>HVAC systems being one of the major energy consumer in manufacturing industries was modelled using the generic lumped parameter method. An Electroplating facility named Electro-Spec was modelled in Simulink and was validated using the real data that was collected from the facility. The Mean Absolute Error (MAE) was about 0.39 for the model which is suitable for implementing controllers for the purpose of energy management. MATLAB and Simulink were used to design and implement the state-of-the-art Model Predictive Control for the purpose of energy efficient control. The MPC was chosen due to its ability to easily handle Multi Input Multi Output Systems, system constraints and its optimal nature. The MPC resulted in a temperature response with a rise time of 10 minutes and a steady state error of less than 0.001. Also from the input response, it was observed that the MPC provided just enough input for the temperature to stay at the set point and as a result led to about 27.6% energy and cost savings. Thus this research has a potential of energy and cost savings and can be readily applied to most of the manufacturing industries that use HVAC, Compressors and machines as their primary energy consumer.</p><br>
388

Optimal dispatch of uncertain energy resources

Amini, Mahraz 01 January 2019 (has links)
The future of the electric grid requires advanced control technologies to reliably integrate high level of renewable generation and residential and small commercial distributed energy resources (DERs). Flexible loads are known as a vital component of future power systems with the potential to boost the overall system efficiency. Recent work has expanded the role of flexible and controllable energy resources, such as energy storage and dispatchable demand, to regulate power imbalances and stabilize grid frequency. This leads to the DER aggregators to develop concepts such as the virtual energy storage system (VESS). VESSs aggregate the flexible loads and energy resources and dispatch them akin to a grid-scale battery to provide flexibility to the system operator. Since the level of flexibility from aggregated DERs is uncertain and time varying, the VESSs’ dispatch can be challenging. To optimally dispatch uncertain, energy-constrained reserves, model predictive control offers a viable tool to develop an appropriate trade-off between closed-loop performance and robustness of the dispatch. To improve the system operation, flexible VESSs can be formulated probabilistically and can be realized with chance-constrained model predictive control. The large-scale deployment of flexible loads needs to carefully consider the existing regulation schemes in power systems, i.e., generator droop control. In this work first, we investigate the complex nature of system-wide frequency stability from time-delays in actuation of dispatchable loads. Then, we studied the robustness and performance trade-offs in receding horizon control with uncertain energy resources. The uncertainty studied herein is associated with estimating the capacity of and the estimated state of charge from an aggregation of DERs. The concept of uncertain flexible resources in markets leads to maximizing capacity bids or control authority which leads to dynamic capacity saturation (DCS) of flexible resources. We show there exists a sensitive trade-off between robustness of the optimized dispatch and closed-loop system performance and sacrificing some robustness in the dispatch of the uncertain energy capacity can significantly improve system performance. We proposed and formulated a risk-based chance constrained MPC (RB-CC-MPC) to co-optimize the operational risk of prematurely saturating the virtual energy storage system against deviating generators from their scheduled set-point. On a fast minutely timescale, the RB-CC-MPC coordinates energy-constrained virtual resources to minimize unscheduled participation of ramp-rate limited generators for balancing variability from renewable generation, while taking into account grid conditions. We show under the proposed method it is possible to improve the performance of the controller over conventional distributionally robust methods by more than 20%. Moreover, a hardware-in-the-loop (HIL) simulation of a cyber-physical system consisting of packetized energy management (PEM) enabled DERs, flexible VESSs and transmission grid is developed in this work. A predictive, energy-constrained dispatch of aggregated PEM-enabled DERs is formulated, implemented, and validated on the HIL cyber-physical platform. The experimental results demonstrate that the existing control schemes, such as AGC, dispatch VESSs without regard to their energy state, which leads to unexpected capacity saturation. By accounting for the energy states of VESSs, model-predictive control (MPC) can optimally dispatch conventional generators and VESSs to overcome disturbances while avoiding undesired capacity saturation. The results show the improvement in dynamics by using MPC over conventional AGC and droop for a system with energy-constrained resources.
389

Model predictive control for adaptive digital human modeling

Sheth, Katha Janak 01 December 2010 (has links)
We consider a new approach to digital human simulation, using Model Predictive Control (MPC). This approach permits a virtual human to react online to unanticipated disturbances that occur in the course of performing a task. In particular, we predict the motion of a virtual human in response to two different types of real world disturbances: impulsive and sustained. This stands in contrast to prior approaches where all such disturbances need to be known a priori and the optimal reactions must be computed off line. We validate this approach using a planar 3 degrees of freedom serial chain mechanism to imitate the human upper limb. The response of the virtual human upper limb to various inputs and external disturbances is determined by solving the Equations of Motion (EOM). The control input is determined by the MPC Controller using only the current and the desired states of the system. MPC replaces the closed loop optimization problem with an open loop optimization allowing the ease of implementation of control law. Results presented in this thesis show that the proposed controller can produce physically realistic adaptive simulations of a planar upper limb of digital human in presence of impulsive and sustained disturbances.
390

Kappa Control with Online Analyzer Using Samples from the Digester's Mid-phase

Gäärd, Peter January 2004 (has links)
<p>In the pulp industry, digesters are used to disolve lignin in wood chips. The concentration of lignin is measured and is called the Kappa number. In this thesis, the question of whether an online Kappa sensor, taking samples from the mid-phase of the digester, is useful or not is analyzed. For the samples to be useful, there has to be a relationship between the measured Kappa at the mid- phase and the measured Kappa in the blowpipe at the bottom of the digester. An ARX model of the lower part of the digester has been estimated. Despite a lot of noise, it seems that it might be possible to use the mid-phase samples and for this model predict the blowpipe flow Kappa signal. It is concluded that the mid-phase samples should be further improved to be more useful. The mid-phase samples have also been used in another ARX model, this time to LP-filter these values without time loss. </p><p>Another important issue has been to examine if the existing controller is good or not. In order to be able to compare it with other controllers, a simulator has been created in MATLAB - Simulink. Test results from this simulator show that the existing controller's use of the mid-phase Kappa samples improves its performance. For a simplified digester model, the existing controller has also been compared with an MPC controller. This test shows that the MPC controller is significantly better. Hence, the conclusion in this thesis is that it might be interesting to study MPC further using a more advanced model.</p>

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