Fuel cell systems that utilize anode recirculation generally require a purge process to remove accumulated gaseous impurities from the anode recirculation system. Especially the accumulation of nitrogen leads to a dec...
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ISBN:
(纸本)9781665436601
Fuel cell systems that utilize anode recirculation generally require a purge process to remove accumulated gaseous impurities from the anode recirculation system. Especially the accumulation of nitrogen leads to a decrease of the cell voltage and therefore a reduced stack efficiency. However, unconsumed hydrogen is lost during the purge process, resulting in a decrease of hydrogen utilization. Therefore, an optimal purge control can help to maximize the overall system *** order to determine and predict the influence of the purge valve opening on the system efficiency with respect to the hydrogen utilization and the stack efficiency we develop a control-oriented model of a PEMFC anode recirculation system. We then set up a model predictive purge controller and compare its performance to two standard purge strategies using the NEDC vehicle test cycle.
This work is concerned with the application of reinforcement learning (RL) techniques to adaptive dynamic programming (ADP) for systems with partly unknown models. In ADP, one seeks to approximate an optimal infinite ...
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This work is concerned with the application of reinforcement learning (RL) techniques to adaptive dynamic programming (ADP) for systems with partly unknown models. In ADP, one seeks to approximate an optimal infinite horizon cost function, the value function. Such an approximation, i.e., critic, does not in general yield a stabilizing control policies, i.e., stabilizing actors. Guaranteeing stability of nonlinear systems under RL/ADP is still an open issue. In this work, it is suggested to use a stability constraint directly in the actor-critic structure. The system model considered in this work is assumed to be only partially known, specifically, it contains an unknown parameter vector. A suitable stabilizability assumption for such systems is an adaptive Lyapunov function, which is commonly assumed in adaptive control. The current approach formulates a stability constraint based on an adaptive Lyapunov function to ensure closed-loop stability. Convergence of the actor and critic parameters in a suitable sense is shown. A case study demonstrates how the suggested algorithm preserves closed-loop stability, while at the same time improving an infinite-horizon performance.
We present an approach to design stabilizing controllers for a set of linear systems without restrictions regarding their modeling order. To this end, the systems are treated as abstract objects in the space of the ν...
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Larvae of Hermetia illucens, also commonly known as black soldier fly (BSF) have gained significant importance in the feed industry, primarily used as feed for aquaculture and other livestock farming. Mathematical mod...
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This work is concerned with practical stabilization of nonlinear systems by means of inf-convolution-based sample-and-hold control. It is a fairly general stabilization technique based on a generic non-smooth control ...
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Hierarchical control approaches have been one of the elective methods for the optimal control of large-scale systems in the last decades. In (Petzke et al., 2018) we presented a multirate hierarchical MPC scheme for l...
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Hierarchical control approaches have been one of the elective methods for the optimal control of large-scale systems in the last decades. In (Petzke et al., 2018) we presented a multirate hierarchical MPC scheme for linear systems, with remarkable flexibility and scalability properties. In this paper we extend the former approach to ensembles of Hammerstein systems and we complement the method by proposing a suitable high-level optimizer. The theoretical properties are discussed in the light of the theoretical properties of the former method. Lastly, an example case study is presented to show the effectiveness of the proposed method.
We introduce PoCET: a free and open-scource Polynomial Chaos Expansion Toolbox for Matlab, featuring the automatic generation of polynomial chaos expansion (PCE) for linear and nonlinear dynamic systems with time-inva...
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We introduce PoCET: a free and open-scource Polynomial Chaos Expansion Toolbox for Matlab, featuring the automatic generation of polynomial chaos expansion (PCE) for linear and nonlinear dynamic systems with time-invariant stochastic parameters or initial conditions, as well as several simulation tools. It offers a built-in handling of Gaussian, uniform, and beta probability density functions, projection and collocation-based calculation of PCE coefficients, and the calculation of stochastic moments from a PCE. Efficient algorithms for the calculation of the involved integrals have been designed in order to increase its applicability. PoCET comes with a variety of introductory and instructive examples. Throughout the paper we show how to perform a polynomial chaos expansion on a simple ordinary differential equation using PoCET, as well as how it can be used to solve the more complex task of optimal experimental design.
This work presents a robust MPC (Model Predictive control) approach for reserve balancing in DC microgrid systems under uncertainties like wind power and energy price variations and different types of fault events. Th...
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This work presents a robust MPC (Model Predictive control) approach for reserve balancing in DC microgrid systems under uncertainties like wind power and energy price variations and different types of fault events. The robust MPC algorithm considers a variable-length prediction horizon which accounts for forecasts in energy price and renewable power over one day. Furthermore, a storage system is used to increase the utility of the demands and minimize the energy costs. The algorithm is tested for multiple fault types which affect the system (line and loss of power faults).
Model predictive control (MPC) is the standard approach to infinite-horizon optimal control which usually optimizes a finite initial fragment of the cost function so as to make the problem computationally tractable. G...
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We provide a computer-assisted approach to ensure that a given discrete-time polynomial system is (asymptotically) stable. Our framework relies on constructive analysis together with formally certified sums of squares...
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