Model predictive control with a rigorous model of a Solid Oxide Fuel Cell

Jacobsen, Lee T.; Spivey, Benjamin J.; Hedengren, John D.

doi:10.1109/acc.2013.6580409

Cited by 17 publications

(12 citation statements)

References 11 publications

(9 reference statements)

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“…To solve the problem of maintaining the output voltage and fuel utilization of SOFCs, many advanced control strategies have been proposed, such as model predictive control (MPC) [11][12][13], fuzzy proportional-integral-derivative (PID) control [14], fuzzy logic control [15], and neural network control [16]. All of these control strategies have been shown to obtain excellent control performance in numerous simulation studies: However, due to their computational complexity, these control strategies are difficult to implement in practical applications.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Genetic Algorithm-Based Optimization of PID Controller Parameters for Fuel Cell Voltage and Fuel Utilization

et al. 2019

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Nowadays, given the great deal of fossil fuel consumption and associated environmental pollution, solid oxide fuel cells (SOFCs) have shown their great merits in terms of high energy conversion efficiency and low emissions as a stationary power source. To ensure power quality and efficiency, both the output voltage and fuel utilization of an SOFC should be tightly controlled. However, these two control objectives usually conflict with each other, making the controller design of an SOFC quite challenging and sophisticated. To this end, a multi-objective genetic algorithm (MOGA) was employed to tune the proportional–integral–derivative (PID) controller parameters through the following steps: (1) Identifying the SOFC system through a least squares method; (2) designing the control based on a relative gain array (RGA) analysis; and (3) applying the MOGA to a simulation to search for a set of optimal solutions. By comparing the control performance of the Pareto solutions, satisfactory control parameters were determined. The simulation results demonstrated that the proposed method could reduce the impact of disturbances and regulate output voltage and fuel utilization simultaneously (with strong robustness).

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Section: Introductionmentioning

confidence: 99%

Multiobjective Genetic Algorithm-Based Optimization of PID Controller Parameters for Fuel Cell Voltage and Fuel Utilization

et al. 2019

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“…In this study, methods developed in the APMonitor Optimization Suite [5] are applied to systems biology in MATLAB, Python, or Julia programming languages. APMonitor is a software package used for applications including advanced monitoring [6], advanced control [7], unmanned aircraft systems [8,9], smart grid energy integration systems [10][11][12][13], and other applications [14,15] that utilize the simultaneous approach to dynamic optimization. The examples shown in this work are computed from the SBML database of curated models in APMonitor and MATLAB.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Dynamic Optimization Methods for Systems Biology with Efficient Sensitivities

2015

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Abstract:In recent years, model optimization in the field of computational biology has become a prominent area for development of pharmaceutical drugs. The increased amount of experimental data leads to the increase in complexity of proposed models. With increased complexity comes a necessity for computational algorithms that are able to handle the large datasets that are used to fit model parameters. In this study the ability of simultaneous, hybrid simultaneous, and sequential algorithms are tested on two models representative of computational systems biology. The first case models the cells affected by a virus in a population and serves as a benchmark model for the proposed hybrid algorithm. The second model is the ErbB model and shows the ability of the hybrid sequential and simultaneous method to solve large-scale biological models. Post-processing analysis reveals insights into the model formulation that are important for understanding the specific parameter optimization. A parameter sensitivity analysis reveals shortcomings and difficulties in the ErbB model parameter optimization due to the model formulation rather than the solver capacity. Suggested methods are model reformulation to improve input-to-output model linearity, sensitivity ranking, and choice of solver.

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“…To achieve this trade-off, a quadratic performance index is used where the squared deviations at the end of each time interval are weighted differently (10 for setpoint deviations and 1 for manipulated variable changes) and summed to create a performance index to be minimized. This yields the dynamic optimization problem in Equation (42), which is subject to the system model in Equations 33-41 and inequality constraints on the inputs:…”

Section: Continuous Logic In An Nmpc Problemmentioning

confidence: 99%

“…The 10 minute time horizon is discretized into one minute intervals and solved using a simultaneous solution method. This is done using a DAE solution package known as APMonitor [40][41][42]. This software package allows a user to define a model using both differential and algebraic equations [43,44].…”

Section: Tank With Overflowmentioning

confidence: 99%

A Continuous Formulation for Logical Decisions in Differential Algebraic Systems using Mathematical Programs with Complementarity Constraints

et al. 2016

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This work presents a methodology to represent logical decisions in differential algebraic equation simulation and constrained optimization problems using a set of continuous algebraic equations. The formulations may be used when state variables trigger a change in process dynamics, and introduces a pseudo-binary decision variable, which is continuous, but should only have valid solutions at values of either zero or one within a finite time horizon. This formulation enables dynamic optimization problems with logical disjunctions to be solved by simultaneous solution methods without using methods such as mixed integer programming. Several case studies are given to illustrate the value of this methodology including nonlinear model predictive control of a chemical reactor using a surge tank with overflow to buffer disturbances in feed flow rate. Although this work contains novel methodologies for solving dynamic algebraic equation (DAE) constrained problems where the system may experience an abrupt change in dynamics that may otherwise require a conditional statement, there remain substantial limitations to this methodology, including a limited domain where problems may converge and the possibility for ill-conditioning. Although the problems presented use only continuous algebraic equations, the formulation has inherent non-smoothness. Hence, these problems must be solved with care and only in select circumstances, such as in simulation or situations when the solution is expected to be near the solver's initial point.

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Model predictive control with a rigorous model of a Solid Oxide Fuel Cell

Cited by 17 publications

References 11 publications

Multiobjective Genetic Algorithm-Based Optimization of PID Controller Parameters for Fuel Cell Voltage and Fuel Utilization

Multiobjective Genetic Algorithm-Based Optimization of PID Controller Parameters for Fuel Cell Voltage and Fuel Utilization

Hybrid Dynamic Optimization Methods for Systems Biology with Efficient Sensitivities

A Continuous Formulation for Logical Decisions in Differential Algebraic Systems using Mathematical Programs with Complementarity Constraints

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