El control predictivo basado en modelos o Model Predictive Control (MPC), no hace referencia al diseño concreto de un controlador sino más bien a un conjunto de ideas o características para el desarrollo de estrategias de control que, aplicadas en un mayor o menor grado, dan lugar a diferentes tipos de controladores con estructuras similares. El MPC es una de las técnicas de control que más se ha desarrollado en los ámbitos académico e industrial en las últimas décadas debido sobre todo a su simplicidad y eficiencia. Sin embargo, no es fácil relacionar los parámetros de ajuste del controlador y las prestaciones del bucle cerrado. En este sentido, es importante diseñar algoritmos de control predictivo que garanticen la estabilidad nominal del bucle cerrado, con tiempos de cálculo pequeños y con un significado claro de sus parámetros sobre las prestaciones del sistema o sobre el esfuerzo de control. La aportación fundamental de esta tesis está relacionada con la definición de un nuevo tipo de controlador predictivo, el PC-GPC, versión modificada de un GPC estándar. En este controlador se ha sustituido el factor de ponderación de la acción de control por un nuevo parámetro denominado número de componentes principales (NPC). La relación entre el nuevo parámetro (NPC) y algunos indicadores numéricos, como la norma del vector de acciones de control o el número de condición de la matriz dinámica G, hacen que su elección esté basada en criterios menos subjetivos que la ponderación de las acciones de control. Además, se ha analizado este tipo de controlador tanto en el ámbito de procesos SISO como MIMO, así como sus características de robustez y estabilidad. Por otro lado, se ha deducido un método de cálculo de un controlador PC-GPC para garantizar la estabilidad nominal de bucle cerrado, cuando el modelo conocido es exacto. / Sanchís Saez, J. (2002). GPC mediante descomposición en valores singulares (SVD). Análisis de componentes principales (PCA) y criterios de selección [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/4924 / Palancia

A run-of-mine (ROM) ore milling circuit is primarily used to grind incoming ore containing precious metals to a powder fine enough to liberate the valuable minerals contained therein. The ground ore has a product particle size specification that is set by the downstream separation unit. A ROM ore milling circuit typically consists of a mill, sump and classifier (most commonly a hydrocyclone). These circuits are difficult to control because of unmeasurable process outputs, non-linearities, time delays, large unmeasured disturbances and complex models with modelling uncertainties. The ROM ore milling circuit should be controlled to meet the final product quality specification, but throughput should also be maximised. This further complicates ROM ore grinding mill circuit control, since an inverse non-linear relationship exists between the quality and throughput. ROM ore grinding mill circuit control is constantly evolving to find the best control method with peripheral tools to control the plant. Although many studies have been conducted, more are continually undertaken, since the controller designs are usually based on various assumptions and the required measurements in the grinding mill circuits are often unavailable. / To improve controller performance, many studies investigated the inclusion of additional manipulated variables (MVs) in the controller formulation to help control process disturbances, or to provide some form of functional control. Model predictive control (MPC) is considered one of the best advanced process control (APC) techniques and linear MPC controllers have been implemented on grinding mill circuits, while various other advanced controllers have been investigated and tested in simulation. Because of the complexity of grinding mill circuits non-linear MPC (NMPC) controllers have achieved better results in simulations where a wider operating region is required. In the search for additional MVs some researchers have considered including the discrete dynamics as part of the controller formulation instead of segregating them from the APC or base-layer controllers. The discrete dynamics are typically controlled using a layered approach. Discrete dynamics are on/off elements and in the case of a closed-loop grinding mill circuit the discrete elements can be on/off activation variables for feed conveyor belts to select which stockpile is used, selecting whether a secondary grinding stage should be active or not, and switching hydrocyclones in a hydrocyclone cluster. Discrete dynamics are added directly to the APC controllers by using hybrid model predictive control (HMPC). HMPC controllers have been designed for grinding mill circuits, but none of them has considered the switching of hydrocyclones as an additional MV and they only include linear dynamics for the continuous elements. This study addresses this gap by implementing a hybrid NMPC (HNMPC) controller that can switch the hydrocyclones in a cluster. / A commonly used continuous-time grinding mill circuit model with one hydrocyclone is adapted to contain a cluster of hydrocyclones, resulting in a hybrid model. The model parameters are refitted to ensure that the initial design steady-state conditions for the model are still valid with the cluster. The novel contribution of this research is the design of a HNMPC controller using a cluster of hydrocyclones as an additional MV. The HNMPC controller is formulated using the complete nonlinear hybrid model and a genetic algorithm (GA) as the solver. An NMPC controller is also designed and implemented as the base case controller in order to evaluate the HNMPC controller’s performance. To further illustrate the functional control benefits of including the hydrocyclone cluster as an MV, a linear optimisation objective was added to the HNMPC to increase the grinding circuit throughput, while maintaining the quality specification. The results show that the HNMPC controller outperforms the NMPC one in terms of setpoint tracking, disturbance rejection, and process optimisation objectives. The GA is shown to be a good solver for HNMPC, resulting in a robust controller that can still control the plant even when state noise is added to the simulation. / Dissertation (MEng)--University of Pretoria, 2018. / National Research Foundation (DAAD-NRF) / Electrical, Electronic and Computer Engineering / MEng / Unrestricted

abstract: Interpersonal communications during civil infrastructure systems operation and maintenance (CIS O&M) are processes for CIS O&M participants to exchange critical information. Poor communications that provide misleading information can jeopardize CIS O&M safety and efficiency. Previous studies suggest that communication contexts and features could be indicators of communication errors and relevant CIS O&M risks. However, challenges remain for reliable prediction of communication errors to ensure CIS O&M safety and efficiency. For example, existing studies lack a systematic summarization of risky contexts and features of communication processes for predicting communication errors. Limited studies examined quantitative methods for incorporating expert opinions as constraints for reliable communication error prediction. How to examine mitigation strategies (e.g., adjustments of communication protocols) for reducing communication-related CIS O&M risks is also challenging. The main reason is the lack of causal analysis about how various factors influence the occurrences and impacts of communication errors so that engineers lack the basis for intervention. This dissertation presents a method that integrates Bayesian Network (BN) modeling and simulation for communication-related risk prediction and mitigation. The proposed method aims at tackling the three challenges mentioned above for ensuring CIS O&M safety and efficiency. The proposed method contains three parts: 1) Communication Data Collection and Error Detection – designing lab experiments for collecting communication data in CIS O&M workflows and using the collected data for identifying risky communication contexts and features; 2) Communication Error Classification and Prediction – encoding expert knowledge as constraints through BN model updating to improve the accuracy of communication error prediction based on given communication contexts and features, and 3) Communication Risk Mitigation – carrying out simulations to adjust communication protocols for reducing communication-related CIS O&M risks. This dissertation uses two CIS O&M case studies (air traffic control and NPP outages) to validate the proposed method. The results indicate that the proposed method can 1) identify risky communication contexts and features, 2) predict communication errors and CIS O&M risks, and 3) reduce CIS O&M risks triggered by communication errors. The author envisions that the proposed method will shed light on achieving predictive control of interpersonal communications in dynamic and complex CIS O&M. / Dissertation/Thesis / Doctoral Dissertation Civil, Environmental and Sustainable Engineering 2020

Lack of clean fresh water is one of the most pervasive problems afflicting people throughout the world. Efficient desalination of sea and brackish water and safe reuse of wastewater become an insistent need. However, such techniques are energy intensive, and thus, a good control design is needed to increase the process efficiency and maintain water production costs at an acceptable level. This thesis proposes solutions to the above challenges and in particular will be focused on two membranebased water systems: Membrane Distillation (MD) and Membrane Bioreactor (MBR) for wastewater treatment plant (WWPT). The first part of this thesis, Direct Contact Membrane Distillation (DCMD) will study as an example an MD process. MD is an emerging sustainable desalination technique which can be powered by renewable energy. Its main drawback is the low water production rate. However, it can be improved by utilizing advanced control strategies. DCMD is modeled by a set of Differential Algebraic Equations (DAEs). In order to improve its water production, an optimization-based control scheme termed Model Predictive Control (MPC) provides a natural framework to optimally operate DCMD processes due to its unique control advantages. Among these advantages are the flexibility provided in formulating the objective function, the capability to directly handle process constraints, and the ability to work with various classes of nonlinear systems. Motivated by the above considerations, two MPC schemes that can maximize the water production rate of DCMD systems have been developed. The first MPC scheme is formulated to track an optimal set-point while taking input and stability constraints into account. The second MPC scheme, Economic MPC (EMPC), is formulated to maximize the distilled water flux while meeting input, stability and other process operational constraints. The total water production under both control designs is compared to illustrate the effectiveness of the two proposed control paradigms. Simulation results show that the DCMD process produces more distilled water when it is operated by EMPC than when it is operated by MPC. The above control techniques assume the full access to the system states. However, this is not the case for the DCMD plant. To effectively control the closed-loop system, an observer design that can estimate the values of the unmeasurable states is required. Motivated by that, a nonlinear observer design for DCMD is proposed. In addition, the effect of the estimation gain matrix on the differentiation index of the DAE system is investigated. Numerical simulations are presented to illustrate the effectiveness of the proposed observer design. The observer-based MPC and EMPC are also studied in this work. Mathematical modeling of a wastewater treatment system is critical because it enhances the process understanding and can be used for process design and process optimization. Motivated by the above considerations, modeling and optimal control strategies have been developed and applied to the MBR-based wastewater treatment process. The model is an extension of the well-known Benchmark simulation models for wastewater treatment. In addition, model predictive control has been applied to maintain the dissolved oxygen concentration level at the desired value. In addition, a conventional PID controller has also been developed. The simulation results show that the both of controllers can be used for dissolved oxygen concentration control. However, MPC has better performance compared to PID scenario.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第22779号 / 工博第4778号 / 新制||工||1747(附属図書館) / 京都大学大学院工学研究科電気工学専攻 / (主査)教授 土居 伸二, 教授 萩原 朋道, 教授 小林 哲生, 教授 古谷 栄光 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第22808号 / 情博第738号 / 新制||情||126(附属図書館) / 京都大学大学院情報学研究科システム科学専攻 / (主査)教授 大塚 敏之, 教授 加納 学, 教授 太田 快人 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

The process to induct air into engine cylinders, via the air inlet system and cylin-der port valves, is referred to as the "gas-exchange". Control is achieved by theturbo-charger, the intake throttle plate and the variable valve timing (VVT) sys-tem. These actuation systems traditionally use separate control with indepen-dent SISO feedback. There are however physical couplings that affect the con-trol performance. This thesis work looks at MPC control methods for a robustcontrol strategy. MPC methods are typically used for systems with slow dynam-ics, due to computational limits. But new advances in CPU performance shouldallow for real-time implementations for engine control. / <p>Redan framlagt exjobbet</p>

Statistics show that unstable articulated vehicles pose a serious threat to the occupants driving them as well as the occupants of the vehicles around them. An articulated vehicle typically experiences three types of instability: snaking, jack-knifing and rollover. An articulated vehicle subjected to any of these instabilities can result in major accidents. It is also known that many individuals are unaware of how to properly tow or pack a loaded articulated vehicle. These individuals are, therefore, at a high risk of causing the vehicle system to become unstable. It can hence be confidently said that a method in which an articulated vehicle can stabilise itself is a worthy research question. The method that is implemented in this study is to create a control system, using Nonlinear Model Predictive Control (NMPC), that has the capability of stabilising an articulated vehicle by applying torque vectoring to the trailer. In order for this control system to be applied, a nonlinear articulated vehicle MSC ADAMS model was constructed. The NMPC controller works by using a nonlinear explicit model to predict the future states of the vehicle and then finding the optimal left and right braking forces of the trailer by minimising the cost function using least squares minimisation. The cost function includes the towing vehicle yaw rate, trailer yaw rate and hitch angle and is minimised by minimising the error between the desired vehicle states and the actual states. It was found that the NMPC is capable of not only preventing instability but also causes the vehicle system to behave as if the trailer is unloaded. This conclusion means that this type of control system can be used on all types of articulated vehicles and shall ensure the safety of not only the vehicle occupants but other road users as well. Unfortunately, due to the impact of the 2020 COVID-19 pandemic, the experimental validation of the model had to be delayed significantly. It is for this reason that the experimental validation for the controller could not be done. / Dissertation (MEng (Mechanical Engineering))--University of Pretoria, 2021. / SATC VDG UP / Mechanical and Aeronautical Engineering / MEng (Mechanical Engineering) / Unrestricted

This thesis focuses on the possibilities of application of nonlinear model predictive control for electric drives. Specifically, for drives with a permanent magnet synchronous motor. The thesis briefly describes the properties of this type of drive and presents its mathematical model. After that, a nonlinear model of predictive control and methods of nonlinear optimization, which form the basis for the controller output calculation, are described. As it is used in the proposed algorithm, the Active set method is described in more detail. The thesis also includes simulation experiments focusing on the choice of the objective function on the ability to control the drive. The same effect is examined for the different choices of the length of the prediction horizon. The end of the thesis is dedicated to the comparison between the proposed algorithm and commonly used field oriented control. The computational demands of the proposed algorithm are also measured and compared to the used sampling time.

This work deals with the predictive control algorithms of the AC drives. The introductory section contains summary of current state of theory and further description and classification of most significant predictive algorithms. A separate chapter is dedicated to linear model predictive control (linear MPC). The main contribution of this work is the introduction of two new predictive control algorithm for PMSM motor, both of which are based on linear MPC. The first of these algorithms has been created with the aim of minimizing its computational demands, while the second algorithm introduces the ability of field weakening. Both new algorithms and linear MPC were simulated in MATLAB-Simulink.