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Item type:Publication, Access status: without files , Finding Stability Bounds for Delayed Systems with Uncertain ParametersThis work addresses the problem of determining the stability picture of an uncertain system with a time delay in the feedback line. Using the Cluster Treatment of Characteristic Roots (CTCR) and the preparatory step, the Spectral Delay Space (SDS), this work proposes an iterative approach to draw the stability picture of the system by effectively running the CTCR procedure for different points on a defined grid of values of the uncertainty. The technique presented in this work focuses on the appearance of purely imaginary roots to draw an exact stability picture of a system. Moreover, a case study of a portal crane with uncertainty in the lifted mass is presented to exemplify the results of this approach.Publicationtype: Conference Paper27 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Nonlinear Model Predictive Control of a PEM Fuel Cell With an Integrated Li-ion Battery Using qLPVThis paper is concerned with the power control of a PEM Fuel Cell System with integrated Li-ion battery using Nonlinear Model Predictive Control. For the controller design a quasi-Linear Parameter Varying representation of the plant was developed using electrical circuit diagrams for both fuel cell and battery. The nonlinearities of the system were expressed by scheduled parameters by which the model of the plant becomes linear in its states and inputs. The battery model was linearized at certain operating points and the derived estimation parameters were integrated as 2-dimensional lookup table into the system representation. The main objective of the controller is to obtain optimal power provision during load transients to an electronic load while keeping the battery's state of charge at a certain level.Publicationtype: Conference Paper136 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Using particle swarm optimization for source seeking in multi-agent systemsThis paper presents a novel approach to the source seeking problem, where a group of mobile agents tries to locate the maximum of a scalar field defined on the space in which they are moving. The agents know their position and the local value of the field, and by communicating with their neighbors estimate the gradient direction of the field. A distributed cooperative control scheme is then designed that drives the group towards the maximum while maintaining a specified formation. Previously proposed control schemes that are based on a combination of H∞-optimal formation control and local gradient estimation suffer from premature convergence to local maxima. To overcome this problem, here the use of particle swarm optimization for locating the global maximum is proposed. Agents take the role of particles and an information flow filter approach is employed to separate the consensus dynamics from the local feedback loops governing the agent dynamics. Stability of the overall scheme is established based on the small gain theorem, and by decomposing the synthesis problem for the distributed information flow filter the problem size is reduced to that of a single agent. Simulation results with multiple maxima and quadrocopter models as agents illustrate the practicality of the approach.Publicationtype: Conference PaperScopus© Citations 5 167 244 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Comparison of consensus loop designs with a mission error signalA multi-agent system trying to complete a mission has to fulfill a number of objectives. In case of a combination of formation control and other mission-related goals such as source seeking, both objectives have to be integrated. This paper presents different designs which incorporate the mission error signal as well as the corresponding generalized plants needed for the synthesis of the corresponding-optimal information flow filter. The comparison of the designs is then performed by evaluation of the simulation results for a sample scenario. The results show different behaviors dependent on the choice of design and weighting filters. The variety in behavior implies to choose the design according to the performance requirements of the particular task.Publicationtype: Conference Paper95