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Multivariable MPC system performance assessment, monitoring, and diagnosis
Title
Multivariable MPC system performance assessment, monitoring, and diagnosis
Creator
Schaffer, J., Cinar, A.
Date
2004-03
Publisher
ELSEVIER SCI LTD
Description
This study focuses on performance assessment and monitoring of model predictive control systems. A methodology is proposed to determine a...
Show moreThis study focuses on performance assessment and monitoring of model predictive control systems. A methodology is proposed to determine a benchmark and monitor model predictive control performance on-line. A performance measure based on the ratio of historical and achieved performance is used for monitoring and a ratio of design and achieved performance is used for diagnosis. Performance monitoring and diagnosis of causes for poor performance are integrated. A real-time knowledge-based system is developed to supervise monitoring and diagnosis activities. Case studies with linear and nonlinear models of an evaporator illustrate the methodology and limitations of linearity assumptions.
Endnote format citation for DOI:10.1016/j.jprocont.2003.07.003
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PROCESS DESIGN FOR SMART GRID COORDINATED IGCC POWER PLANT
Title
PROCESS DESIGN FOR SMART GRID COORDINATED IGCC POWER PLANT
Creator
Garcia Fracaro, Sofia Belen
Date
2019
Description
The current scientific consensus is that changes in greenhouse gas emissions will have wide-ranging effects on the natural environment as well...
Show moreThe current scientific consensus is that changes in greenhouse gas emissions will have wide-ranging effects on the natural environment as well as on human society and world economies. Cutting green house gas emissions could be achieved by switching the majority of power production to renewable sources, like wind and solar. However, the intermittent nature of renewable sources will require special attention when integrating into the electric power system. The notion of a smart grid is to introduce new dispatch capable sources as well as provide mechanisms for consumers to be responsive to power availability. One way a smart grid communicates its objectives is through the price of electricity. Economic Model Predictive Control (EMPC) can utilize forecasts of electricity prices to determine operating policies for dispatch capable generators and flexible consumers. While EMPC in the context of variable electricity prices can reduce costs (or increase the revenue), operational flexibility will usually require equipment upgrade, and add to the capital cost of the system. In this thesis an Integrated Gasification Combined Cycle (IGCC) will be used to illustrate potential dispatch capabilities, the benefits of EMPC based operation, and the challenges associated with process design in the context of smart grid coordinated operation. First it is assumed that dispatch enabling equipment is available. While EMPC can provide an increase in the revenue during plant operation, it is not amenable to the equipment design problem. While the method of ELOC can be used for integrated process design and control, we must first show that ELOC performs similar to EMPC and that it can serve as a surrogate. Finally, the ELOC based equipment design problem is formulated, which optimizes with respect to operating as well as capital costs.
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MODELING AND CONTROL OF A GASOLINE-FUELED COMPRESSION IGNITION ENGINE
Title
MODELING AND CONTROL OF A GASOLINE-FUELED COMPRESSION IGNITION ENGINE
Creator
Pamminger, Michael
Date
2021
Description
This work investigates a novel combustion concept, Gasoline Compression Ignition, that derives its superiority from the high compression ratio...
Show moreThis work investigates a novel combustion concept, Gasoline Compression Ignition, that derives its superiority from the high compression ratio of a compression ignition engine as well as the properties of gasoline fuel, such as longer ignition delay and higher volatility compared to diesel fuel. Gasoline Compression Ignition was experimentally tested on a 12.4L truck engine and the acquired data were leveraged to develop a physics-based 0-dimensional combustion model for an engine operating with a low-reactivity fuel. The proposed 0-dimensional combustion model was developed to account for the different stages in combustion caused by the fuel stratification of various injection events and fuel mass fractions. As the ignition delay model is an integral part of the entire combustion process and significantly affects the predictionaccuracy, special attention was paid to local phenomena influencing ignition delay. A 1-dimensional spray model by Musculus and Kattke was employed in conjunction with a Lagrangian tracking approach in order to estimate the local fuel-air ratio within the spray tip, as a proxy for reactivity. The local fuel-air ratio, in-cylinder temperature and pressure were used in an integral fashion to estimate the ignition delay. Heat release rates were modeled by using first-order non-linear differential equations. Model prediction errors in combustion phasing of less than 1 crank angle degree across most conditions were achieved. Modeling results of other combustion metrics such as combustion duration and indicated mean effective pressure are also suitably accurate. Also, the model has been shown to be capable of estimating the ringing intensity for most conditions. While the performance of the proposed model was very satisfactory, the high computational time made it unsuitable for simulations. The high computational cost was mostly caused by the 1-dimensional spray model which described the fuelstratifcation in the spray tip as a function of crank angle for multiple injection events. Insights obtained from the 1-dimensional spray model were leveraged and applied to a 0-dimensional model to reduce the computation time. With the reduced order model, the simulation time decreased by three orders of magnitude for an entire engine cycle over the combustion model with the 1-dimensional spray model. Capturing only the basic features of the spray propagation did not show a substantial increase in prediction error compared to the initially proposed model. In order for this model to reflect a virtual engine, the influence of changes in actuator settings on intake manifold dynamics was modeled with first-order transfer functions. The intake manifold dynamics in turn influence intake valve closure conditions and further the entire combustion process. The proposed model provides information about in-cylinder metrics such as combustion phasing and indicated mean effective pressure. By taking into account the losses due to gas-exchange and friction, the brake mean effective pressure was modeled. The model was also augmented to capture cycle-to-cycle variations, thereby ensuring a faithful representation of real engine behavior. The Gasoline Compression Ignition combustion model, the intake dynamics and gas-exchange and friction model as well as the cycle-to-cycle variations model were combined to create a full engine model. This Gasoline Compression Ignition engine model was used as the plant in a control system and implemented in Matlab/Simulink.The Gasoline Compression Ignition engine model was then leveraged to investigate control actions and engine behavior with and without limiting in-cylinder peak pressure as well as combustion noise. Controlling combustion noise is of particular interest for injection strategies where fuel introduction happens early in the cycle. State estimation was performed by means of a Kalman filter which feeds into a model predictive controller. The model predictive controller chooses control actions based on a predefined cost function under consideration of bounds reflecting physical constraints. The Gasoline Compression Ignition engine model was also utilized to establish a state-space model that serves the Kalman filter and model predictive controller for estimation and prediction. In addition, the proposed control architecture was investigated at two different levels of cycle-to-cycle variations. Disturbance rejection was implemented to reduce state fluctuations and control efforts when high cycle-to-cycle variations are present. The control algorithm is able to maintain the desired references for brake mean effective pressure and combustion phasing while controlling peak in-cylinder pressure and combustion noise.
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Computationally Efficient Predictive Control Strategies for Autonomous Vehicles
Title
Computationally Efficient Predictive Control Strategies for Autonomous Vehicles
Creator
Bhattacharyya, Viranjan
Date
2021
Description
This thesis aims at developing computationally efficient (hence real-time applicable) control strategies for autonomous vehicles in the...
Show moreThis thesis aims at developing computationally efficient (hence real-time applicable) control strategies for autonomous vehicles in the presence of uncertainty, while incorporating high fidelity vehicle dynamics. The motivation for the control strategies is to ensure safety and improve energy efficiency of the vehicles. In this research, an effort has been made to develop control strategies to strike a balance between these competing factors. The specific contributions are: development of a new hierarchical control framework that can guarantee avoidance of red-light idling in the presence of uncertainty in preceding vehicle information/prediction in connected environment (hence improves system mobility); exploitation of a data-driven modeling approach for identifying a linear predictor for the nonlinear vehicle dynamics, which facilitates formulation of a convex equivalent problem of the original non-convex problem (hence facilitates computational tractability); introduction of a novel vehicle dynamics-aware fast game-theoretic planner for behavior and motion planning of vehicles in uncertain and unconnected environments. This thesis explores both the possible directions of future autonomous vehicles: connected and unconnected autonomous vehicles. In particular, the first problem relates to longitudinal fuel efficient driving (eco-driving) in a connected urban environment, where the connected and automated vehicles (CAVs) aim at the improvement of fuel efficiency and reduction of red-light idling (stop and go motion). The CAVs also focus on ensuring collision avoidance with the preceding vehicles despite the prediction uncertainty in future trajectory of preceding vehicles. This problem assumes vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, and is a longitudinal control problem. The next problem considers the uncertainty in prediction of future states of neighbouring vehicles in an unconnected environment and involves both lateral and longitudinal control. Following previous research, the interactive nature of driving is modeled using game-theory and a computationally efficient game-theoretic planner is introduced. Simulation results show the efficacy of the proposed methods in terms of computational tractability and fuel-efficiency.
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Prediction and Control of In-Cylinder Processes in Heavy-Duty Engines Using Alternative Fuels
Title
Prediction and Control of In-Cylinder Processes in Heavy-Duty Engines Using Alternative Fuels
Creator
Pulpeiro Gonzalez, Jorge
Date
2024
Description
This Ph.D. thesis focuses on advancing diagnostic techniques and control-oriented models to enhance the efficiency and performance of internal...
Show moreThis Ph.D. thesis focuses on advancing diagnostic techniques and control-oriented models to enhance the efficiency and performance of internal combustion (IC) engines, particularly heavy-duty engines utilizing alternative fuels. The research endeavors to contribute to the field of model-based control of engines through the development and implementation of innovative methodologies. The primary emphasis is on the development of diagnostic methods, control-oriented models and advanced control strategies for compression ignition engines using alternative fuels. The first key topic explores the determination of the Most Representative Cycle for Combustion Phasing Estimation based on cylinder pressure measurements. The method developed extracts crucial information from experimental data obtained from four distinct engines: the heavy-duty single-cylinder GCI engine, the light-duty multi-cylinder diesel engine, a CFR engine, and a single-cylinder light-duty Spark Ignition (SI) engine. This work lays the foundation for precise combustion phasing estimation, a critical parameter for engine control. The second major contribution involves the development of control-oriented models for Variable Geometry Turbochargers (VGT) and inter-coolers. Two models are established: a data-driven turbocharger model and an empirical inter-cooler model. These models are meticulously calibrated and validated using experimental data from a multi-cylinder light-duty diesel engine, providing valuable insights into the behavior of these components under varying conditions. The outcomes contribute to facilitate predictive control of engine air systems. The third core aspect of the thesis revolves around Model Predictive Control of Combustion Phasing in heavy-duty compression-ignition engines utilizing alternative fuels. A combustion phasing and engine load model is derived from experimental data and incorporated into an MPC framework. The MPC strategy is subsequently tested in the heavy-duty GCI test cell and compared against a conventional Proportional-Integral-Derivative (PID) control strategy. The results showcase the effectiveness of the MPC approach in achieving precise control of combustion phasing, demonstrating its potential for optimizing engine performance. In summary, this Ph.D. thesis contributes significantly to the field of engine controls by advancing diagnostic techniques, control-oriented models, and implementing a cutting-edge MPC-based control strategy for compression ignition engines using alternative fuels. The research findings not only enhance the understanding of in-cylinder processes but also pave the way for more efficient and sustainable heavy-duty engines using alternative fuels.
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