Network traffic assignment/equilibrium models have been widely used for transportation planning. For traffic management, one is interested on how traffic patterns change dynamically since equilibrium cannot be reached instantaneously. This dissertation focuses on modeling short-term traffic patterns in a transportation network, and addresses these topics: (1) comparison of loading route flows versus link flows, (2) development of a mesoscopic model for loading route flows in a network, (3) estimation of route flows based on the mesoscopic model, and (4) optimizing traffic signal timings based on the estimated route flows.With regard to the first topic, many models propagate flows through a network using link flows and independent turning probabilities (ITP) at nodes. Chapter 2 describes the effects of the ITP assumption on the traffic patterns that occur based on route flow loading; this provides the motivation for using route flows in this research.Route trajectories are the spatial-temporal realizations of vehicle route flow demands. This dissertation proposes a mesoscopic simulation platform where route flows are propagated through the network and dynamic trajectories are computed. Under interrupted and uninterrupted flow conditions, route trajectories from the mesoscopic model are compared with ones from a microscopic model, the latter model being used to provide realistic data since real data at this level of detail is not currently available. Results from both models match well; the corresponding traffic patterns are very similar, both graphically and statistically.In chapter 5, a model is presented for estimating temporal route flow demands when real-time data is available. The model is formulated as a bi-level optimization problem where a least-square model is constructed at the upper level and the mesoscopic model is utilized at the lower level to relate flow demands and route trajectories. Based on real-time measurements, this model estimates dynamic route flows that are consistent with observed traffic patterns. Computational results using a micro-simulator for "real" data show that the model estimates well the route flows loaded in the micro-simulation model.Finally, in chapter 6, a traffic signal control optimizer is developed based on the mesoscopic model and a gradient search to optimize any given performance index such as average delay. A numerical example shows that the optimizer significantly improves the performance index and the approach may be used for on-line traffic signal control.

The thesis deals with the design of a permanent magnet synchronous machine controller that isimplemented on an embedded platform to replace the off-the-shelf controller currently being used in theelectric race car of the KTH Formula Student team. Software implementation of the control algorithmwas tested in laboratory environment on the hardware prototype of a 2-level three-phase voltage sourceinverter.Field oriented control and finite control set model predictive control algorithms were implemented insimulation environment. The latter performed better in terms of reducing switching activity and torqueripple, but needs vastly more computational resources due to its nature of being an online optimizationproblem. Trade-off curve of phase current harmonic distortion and switching activity showed that themodel prediction control algorithm performs better in the low frequency range (1-20 kHz). Obtainedsimulation results were used for power electronics component selection.Field oriented control was implemented on a TMS320F28335 DSP. SPI communication was employedto configure gate driver circuits and perform error handling. The DSP program follows interrupt basedorganization and the main control loop runs on the variable frequency of the pulse width modulation.Low voltage test results on three-phase inductive-resistive load showed that the controller outputssinusoidal current. Efficiency measurement, high voltage and motor testing were hindered by interferencefrom the Silicon-Carbide MOSFETs that prohibited correct operation of hardware. / Den här uppsatsen handlar om designen och implementeringen av en motorstyrning för en permanen- magnetiserad synkronmotor, med syfte att ersätta standardmotorstyrningsenheten i KTH Formula Students tävlingsbil. Implementationen av styralgoritmen testades experimentellt tillsammans med en prototyptillverkad frekvensomriktare i labbmiljö. Regleralgoritmer för field oriented control och finite control set model predictive control implementerades och testades i simuleringsmiljö. Den senare algoritmen visade sig prestera bättre i form av lägre vridmomentsoscillationer trots lägre switch-frekvens men den kräver samtidigt mer beräkningskraft. Övertonsinnehållet (THD) i fasströmmarna som funktion av switchfrekvensen undersöktes för de båda regleralgoritmerna, algoritmen för model predictive control gav lägre THD vid lägre frekvenser (1-20 kHz). Simuleringsresultaten användes för att motivera valet av komponenter till frekvensomriktaren. Regleralgoritmen för field oriented control implementerades och testades experimentellt med hjälp av ett utvecklingskort (TMS320F28335) från Texas Instruments. SPI-kommunikation användes för att konfigurera drivkretsana samt för att utläsa felkoder. Experimentalla tester som utfördes på låg spänningsnivå visade att strömmen till lasten var sinusformad. Mätning av verkningsgrad och provning tillsammans med motorn på en högre spänningsnivå gick inte att geno av att de snabba switchförloppen i kiselkarbidtransistorerna störde ut motorstyrningen.

No description available.

Experience Mapping based Prediction Control (EMPC) is a new type of controller presented in literature, which is based on the concept of Human Motor Control (HMC). During the developmental phase, called the initial learning phase, the controller records the experience in a knowledge base, through online interactions with the system to be controlled. This knowledge base created using the experience maps is termed as Experience Mapped Knowledge Base (EMK). The controller envisages the development of EMK only through interaction with the system, without the need for knowledge of the detailed plant model. The EMPC controls the system through prediction of actions based on the mapped experiences of EMK. Depending on the nature of control required for the system chosen, various strategies can be used to achieve control using the EMK. The above controller has previously been utilized for motion control applications. In the present work an effort has been made to study the suitability of the EMPC for the voltage regulation of switching converters. The plant chosen for the control study is a discontinuous conduction mode (DCM) buck converter. The parameter to be monitored for the purpose of control is the load voltage. The control input from the EMPC to the converter is a duty ratio value based pulse-width modulated (PWM) signal. Two strategies of control have been proposed: steady state control and transient control. Steady state control action maintains the steady state output voltage at the required value for a given load. The transient control action is used to improve the transient performance of the system. Iterative predictive action and iterative transient actions are used to facilitate convergence of the output voltage to within the required range in presence of non-linearities and uncertainties in the system. Impulse action is introduced to further improve the transient performance of the system. The EMPC is compared a proportional-integral (PI) controller for the given DCM buck system.

<p>To apply Industry 4.0 technologies and accelerate the modernization of continuous pharmaceutical manufacturing, digital twin (DT) and advanced process control (APC) strategies are indispensable. The DT serves as a virtual representation that mirrors the behavior of the physical process system, enabling real-time monitoring and predictive capabilities. Consequently, this facilitates the feasibility of real-time release testing (RTRT) and enhances drug product development and manufacturing efficiency by reducing the need for extensive sampling and testing. Moreover, APC strategies are required to address variations in raw material properties and process uncertainties while ensuring that desired critical quality attributes (CQAs) of in-process materials and final products are maintained. When deviations from quality targets are detected, APC must provide optimal real-time corrective actions, offering better control performance than the traditional open loop-control method. The progress in DT and APC is beneficial in shifting from the paradigm of Quality-by-Test (QbT) to that of Quality-by-Design (QbD) and Quality-by-Control (QbC), which emphasize the importance of process knowledge and real-time information to ensure product quality.</p> <p><br></p> <p>This study focuses on four key elements and their applications in a continuous dry granulation tableting process, including feeding, blending, roll compaction, ribbon milling and tableting unit operations. Firstly, the necessity of a digital infrastructure for data collection and integration is emphasized. An ISA-95-based hierarchical automation framework is implemented for continuous pharmaceutical manufacturing, with each level serving specific purposes related to production, sensing, process control, manufacturing operations, and business planning. Secondly, investigation of process analytical technology (PAT) tools for real-time measurements is highlighted as a prerequisite for effective real-time process management. For instance, the measurement of mass flow rate, a critical process parameter (CPP) in continuous manufacturing, was previously limited to loss-in-weight (LIW) feeders. To overcome this limitation, a novel capacitance-based mass flow sensor, the ECVT sensor, has been integrated into the continuous direct compaction process to capture real-time powder flow rates downstream of the LIW feeders. Additionally, the use of near-infrared (NIR)-based sensor for real-time measurement of ribbon solid fraction in dry granulation processes is explored. Proper spectra selection and pre-processing techniques are employed to transform the spectra into useful real-time information. Thirdly, the development of quantitative models that establish a link between CPPs and CQAs is addressed, enabling effective product design and process control. Mechanistic models and hybrid models are employed to describe the continuous direct compaction (DC) and dry granulation (DG) processes. Finally, applying APC strategies becomes feasible with the aid of real-time measurements and model predictions. Real-time optimization techniques are used to combine measurements and model predictions to infer unmeasured states or mitigate the impact of measurement noise. In this work, the moving horizon estimation-based nonlinear model predictive control (MHE-NMPC) framework is utilized. It leverages the capabilities of MHE for parameter updates and state estimation to enable adaptive models using data from the past time window. Simultaneously, NMPC ensures satisfactory setpoint tracking and disturbance rejection by minimizing the error between the model predictions and setpoint in the future time window. The MHE-NMPC framework has been implemented in the tableting process and demonstrated satisfactory control performance even when plant model mismatch exists. In addition, the application of MHE enables the sensor fusion framework, where at-line measurements and online measurements can be integrated if the past time window length is sufficient. The sensor fusion framework proves to be beneficial in extending the at-line measurement application from just validation to real-time decision-making.</p>