Browsing by Subject "MPC"
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Item Addressing uncertainty and modeling error in the design and control of process systems : methods and applications(2016-08) Wang, Siyun, Ph.D.; Baldea, Michael; Edgar, Thomas F.; Rochelle, Gary T.; Truskett, Thomas M.; Biros, GeorgeA process system faces the challenge of uncertainty throughout its lifetime. At the design stage, uncertainty originates from inaccurate knowledge of design parameters and unmeasured or unmeasurable ambient disturbances. Oftentimes, designers choose to increase system size to account for uncertainty and fluctuations; however, this approach has an economic limit, past which the capital expenditure outweighs the potential operational benefits. In the operational stage, uncertainty is manifest, amongst others, in fluctuations in operating conditions, market demand and raw material availability. Another type of uncertainty in (modern) process operations is related to the quality of process models that are used for making control and operational decisions. Of particular importance is the quality of the dynamic models that are used in real-time optimal control computations. The chemical industry has been the pioneer (and is currently the leader) of model predictive control (MPC) implementations, whereby the control moves are computed, over a receding time horizon, by solving an optimal control problem at each time step. While uniquely able to deal with large-scale, non-square constrained systems, MPC is vitally dependent on the predictive abilities of the built-in model. Changes in plant conditions are a a source of uncertainty in this case as-well, leading to a discrepancy (mismatch) between the model predictions and the true plant behavior. In this dissertation, I address the problems of design under uncertainty and plant-model mismatch. For the former, identification-based optimization (IBO) framework is proposed as a new, computationally efficient framework for optimizing the design of dynamic systems under uncertainty problem. The framework uses properly designed pseudo-random multilevel signals (PRMS) to represent time-varying uncertain variables. This allows us to formulate the design under uncertainty problem as a dynamic optimization problem. A solution algorithm is proposed using a sequential approach. Several application examples are discussed, demonstrating the superior computational performance of the IBO approach. Furthermore, an extension of the method that explicitly considers the tradeoff between conservativeness and dynamic performance is introduced. The latter, plant-model mismatch problem, is addressed using a novel autocovariance-based approach. Under appropriate assumptions, an explicit relation is established between the autocovariance of the process output and the plant-model mismatch terms, represented either in a step response model or a transfer function model. It is demonstrated that an asymptotically correct set of estimates of the values of plant-model mismatch for each model parameters is the global minimizer of the discrepancy between the autocovariance predicted using the relation and the autocovariance calculated from a data set collected from closed-loop operating data. Extensions of this approach handle cases where the active set of the MPC is changing over time and there are setpoint change and measurable disturbances occur in the control loop.Item Decentralized Model Predictive Control of a Multiple Evaporator HVAC System(2009-05-15) Elliott, Matthew StuartVapor compression cooling systems are the primary method used for refrigeration and air conditioning, and as such are a major component of household and commercial building energy consumption. Application of advanced control techniques to these systems is still a relatively unexplored area, and has the potential to significantly improve the energy efficiency of these systems, thereby decreasing their operating costs. This thesis explores a new method of decentralizing the capacity control of a multiple evaporator system in order to meet the separate temperature requirements of two cooling zones. The experimental system used for controller evaluation is a custom built small-scale water chiller with two evaporators; each evaporator services a separate body of water, referred to as a cooling zone. The two evaporators are connected to a single condenser and variable speed compressor, and feature variable water flow and electronic expansion valves. The control problem lies in development of a control architecture that will chill the water in the two tanks (referred to as cooling zones) to a desired temperature setpoint while minimizing the energy consumption of the system. A novel control architecture is developed that relies upon time scale separation of the various dynamics of the system; each evaporator is controlled independently with a model predictive control (MPC) based controller package, while the compressor reacts to system conditions to supply the total cooling required by the system as a whole. MPC?s inherent constraint-handling capability allows the local controllers to directly track an evaporator cooling setpoint while keeping superheat within a tight band, rather than the industrially standard approach of regulating superheat directly. The compressor responds to system conditions to track a pressure setpoint; in this configuration, pressure serves as the signal that informs the compressor of cooling demand changes. Finally, a global controller is developed that has knowledge of the energy consumption characteristics of the system. This global controller calculates the setpoints for the local controllers in pursuit of a global objective; namely, regulating the temperature of a cooling zone to a desired setpoint while minimizing energy usage.Item Flow Control of Real Time Multimedia Applications Using Model Predictive Control with a Feed Forward Term(2011-02-22) Duong, Thien ChiMultimedia applications over the Internet are getting more and more popular. While non-real-time streaming services, such as YouTube and Megavideo, are attracting millions of visiting per day, real-time conferencing applications, of which some instances are Skype and Yahoo Voice Chat, provide an interesting experience of communication. Together, they make the fancy Internet world become more and more amusing. Undoubtedly, multimedia flows will eventually dominate the computer network in the future. As the population of multimedia flows increases gradually on the Internet, quality of their service (QoS) is more of a concern. At the moment, the Internet does not have any guarantee on the quality of multimedia services. To completely surpass this limitation, modifications to the network structure is a must. However, it will take years and billions of dollars in investment to achieve this goal. Meanwhile, it is essential to find alternative ways to improve the quality of multimedia services over the Internet. In the past few years, many endeavors have been carried on to solve the problem. One interesting approach focuses on the development of end-to-end congestion control strategies for UDP multimedia flows. Traditionally, packet losses and delays have been commonly used to develop many known control schemes. Each of them only characterizes some different aspects of network congestion; hence, they are not ideal as feedback signals alone. In this research, the flow accumulation is the signal used in feedback for flow control. It has the advantage of reflecting both packet losses and delays; therefore, it is a better choice. Using network simulations, the accumulations of real-time audio applications are collected to construct adaptive flow controllers. The reason for choosing these applications is that they introduce more control challenges than non-real-time services. One promising flow control strategy was proposed by Bhattacharya and it was based on Model Predictive Control (MPC). The controller was constructed from an ARX predictor. It was demonstrated that this control scheme delivers a good QoS while reducing bandwidth use in the controlled flows by 31 percent to 44 percent. However, the controller sometime shows erratic response and bandwidth usage jumps frequently between lowest and highest values. This is not desirable. For an ideal controller, the controlled bandwidth should vary near its mean. To eliminate the deficiency in the strategy proposed by Bhattacharya, it is proposed to introduce a feed forward term into the MPC formulation, in addition to the feedback terms. Simulations show that the modified MPC strategy maintains the benefits of the Bhattacharya strategy. Furthermore, it increases the probability of bandwidth savings from 58 percent for the case of Bhattacharya model to about 99 percent for this work.Item Novel methods that improve feedback performance of model predictive control with model mismatch(2009-05) Thiele, Dirk; Flake, Robert H.; Edgar, Thomas F.Model predictive control (MPC) has gained great acceptance in the industry since it was developed and first applied about 25 years ago [1]. It has established its place mainly in the advanced control community. Traditionally, MPC configurations are developed and commissioned by control experts. MPC implementations have usually been only worthwhile to apply on processes that promise large profit increase in return for the large cost of implementation. Thus the scale of MPC applications in terms of number of inputs and outputs has usually been large. This is the main reason why MPC has not made its way into low-level loop control. In recent years, academia and control system vendors have made efforts to broaden the range of MPC applications. Single loop MPC and multiple PID strategy replacements for processes that are difficult to control with PID controllers have become available and easier to implement. Such processes include deadtime-dominant processes, override strategies, decoupling networks, and more. MPC controllers generally have more "knobs" that can be adjusted to gain optimum performance than PID. To solve this problem, general PID replacement MPC controllers have been suggested. Such controllers include forward modeling controller (FMC)[2], constraint LQ control[3] and adaptive controllers like ADCO[4]. These controllers are meant to combine the benefits of predictive control performance and the convenience of only few (more or less intuitive) tuning parameters. However, up until today, MPC controllers generally have only succeeded in industrial environments where PID control was performing poorly or was too difficult to implement or maintain. Many papers and field reports [5] from control experts show that PID control still performs better for a significant number of processes. This is on top of the fact that PID controllers are cheaper and faster to deploy than MPC controllers. Consequently, MPC controllers have actually replaced only a small fraction of PID controllers. This research shows that deficiencies in the feedback control capabilities of MPC controllers are one reason for the performance gap between PID and MPC. By adopting knowledge from PID and other proven feedback control algorithms, such as statistical process control (SPC) and Fuzzy logic, this research aims to find algorithms that demonstrate better feedback control performance than methods commonly used today in model predictive controllers. Initially, the research focused on single input single output (SISO) processes. It is important to ensure that the new feedback control strategy is implemented in a way that does not degrade the control functionality that makes MPC superior to PID in multiple input multiple output (MIMO) processes.Item Subsurface Flow Management and Real-Time Production Optimization using Model Predictive Control(2012-02-14) Lopez, Thomas JaiOne of the key challenges in the Oil & Gas industry is to best manage reservoirs under different conditions, constrained by production rates based on various economic scenarios, in order to meet energy demands and maximize profit. To address the energy demand challenges, a transformation in the paradigm of the utilization of "real-time" data has to be brought to bear, as one changes from a static decision making to a dynamical and data-driven management of production in conjunction with real-time risk assessment. The use of modern methods of computational modeling and simulation may be the only means to account for the two major tasks involved in this paradigm shift: (1) large-scale computations; and (2) efficient utilization of the deluge of data streams. Recently, history matching and optimization were brought together in the oil industry into an integrated and more structured approach called optimal closed-loop reservoir management. Closed-loop control algorithms have already been applied extensively in other engineering fields, including aerospace, mechanical, electrical and chemical engineering. However, their applications to porous media flow, such as - in the current practices and improvements in oil and gas recovery, in aquifer management, in bio-landfill optimization, and in CO2 sequestration have been minimal due to the large-scale nature of existing problems that generate complex models for controller design and real-time implementation. Their applicability to a realistic field is also an open topic because of the large-scale nature of existing problems that generate complex models for controller design and real-time implementation, hindering its applicability. Basically, three sources of high-dimensionality can be identified from the underlying reservoir models: size of parameter space, size of state space, and the number of scenarios or realizations necessary to account for uncertainty. In this paper we will address type problem of high dimensionality by focusing on the mitigation of the size of the state-space models by means of model-order reduction techniques in a systems framework. We will show how one can obtain accurate reduced order models which are amenable to fast implementations in the closed-loop framework .The research will focus on System Identification (System-ID) (Jansen, 2009) and Model Predictive Control (MPC) (Gildin, 2008) to serve this purpose. A mathematical treatment of System-ID and MPC as applied to reservoir simulation will be presented. Linear MPC would be studied on two specific reservoir models after generating low-order reservoir models using System-ID methods. All the comparisons are provided from a set of realistic simulations using the commercial reservoir simulator called Eclipse. With the improvements in oil recovery and reductions in water production effectively for both the cases that were considered, we could reinforce our stance in proposing the implementation of MPC and System-ID towards the ultimate goal of "real-time" production optimization.