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Item type:Publication, Access status: without files , Distributed Model Predictive Flocking with Obstacle Avoidance and Asymmetric Interaction Forces(2023-05); In most of the existing literature on predictive flocking, the characteristic swarm behavior is formulated in terms of cost functions of quadratic optimization problems with attractive and repulsive interaction forces of equal strength. In this paper, we propose a distributed model predictive flocking framework in which attractive and repulsive interaction forces can be tuned independently by implementing the rules of flocking as softened inequality constraints. The presented framework is able to handle input constraints, obstacle avoidance, and the pursuit of group objectives. The performance of the proposed algorithms is validated in simulation.Publicationtype: Conference Paper12 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Robust Performance Analysis of Source-Seeking Dynamics with Integral Quadratic Constraints(2022-06); We analyze the performance of source-seeking dynamics involving vehicles embedded in an underlying scalar field with gradient based forcing terms. We leverage the recently developed framework of α-integral quadratic constraints (IQCs) to obtain convergence rate estimates. We first present the hard Zames-Falb (ZF) α-IQCs involving general non-causal multipliers and show that a parameterization of the ZF multiplier, suggested in the literature for the standard version of the ZF IQCs, can be adapted to the α-IQCs setting. Owing to the time-domain arguments, we can seamlessly extend these results to linear parameter varying (LPV) vehicles possibly opening the doors to non-linear vehicle models with quasi-LPV representations. We illustrate the theoretical results on a linear time invariant (LTI) model of a quadrotor, a non-minimum phase LTI example and an LPV example of a quadrotor with two modes which show a clear benefit of using general non-causal dynamic multipliers to drastically reduce conservatism.Publicationtype: Conference Paper38 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Online power system parameter estimation and optimal operation(American Automatic Control Council (AACC), 2021-05); Houska, BorisThe integration of renewables into electrical grids calls for novel control schemes, which usually are model based. Classically, for power systems parameter estimation and optimization-based control are often decoupled, which may lead to increased cost of system operation during the estimation procedures. The present work proposes a method for simultaneously minimizing grid operation cost and estimating line parameters. To this end, we rely on methods from optimal design of experiments. This approach leads to a substantial reduction in cost for optimal estimation and in higher accuracy in the parameters compared with standard combination of optimal power flow and maximum-likelihood estimation. We illustrate the performance of the proposed method on simple benchmark system.Publicationtype: Conference Paper17 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Distributed Controller Design for Systems Interconnected over Chordal Graphs(2020-07); ; In this paper we consider the problem of designing distributed H2 suboptimal controllers for continuoustime networked systems in a distributed fashion, when both the plant and the controller are interconnected over the same chordal network. Combining results for networked systems and sparse semidefinite optimization, it is possible to reduce the conservatism introduced by the decomposition of the synthesis problem compared to existing methods, while at the same time also reducing the computational complexity.Publicationtype: Conference Paper113 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , A Dynamic Quasi-Taylor Approach for Distributed Consensus Problems with Packet Loss(2020-07); This paper presents a novel approach for handling packet loss in first-order consensus protocols based on a Taylor series expansion. We propose a dynamic memory approach which depends on a locally measured loss rate at each agent. The quasi-Taylor method assumes that each agent is storing not only the past received value of its neighbours in a memory, but the last ν received states of all neighbours in order to predict the future trajectory with the quasi-Taylor estimation. In addition, we use the so-called importance measure to label the most important information received at each time step. Then, depending on the measured loss rate the past data points of a neighbour are used to predict the future trajectory. Hereby, the trajectory is determined as a convex combination of different orders of the quasi-Taylor estimation. In order to minimise the distance of the consensus value to the actual average, we propose to use an adaptive step size for predicting the future trajectory of the neighbours. We give an upper bound on the convergence rate when uniform packet loss is assumed and show that the proposed approach outperforms existing methods from the literature with the help of simulation studies.Publicationtype: Conference Paper144 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Dynamic mechanism design for human-in-the-loop control of building energy consumptionIntegrating the preferences of building occupants for efficient building energy management has the potential for significant energy savings. In this paper, we present a new dynamic mechanism that achieves ex-post incentive compatibility, i.e., the occupants reveal their privately-held preferences truthfully in every time period. These preferences are then incorporated in a receding horizon control scheme to jointly minimize the cost of energy purchase and the discomfort experienced by the occupants. We evaluate the performance of our scheme with a baseline heating policy based on standardized thermal comfort bounds, and the classical dynamic pivot mechanism via extensive numerical simulations of a sample building. We illustrate that due to the integration of individual preferences, both occupant discomfort and energy consumption can be greatly reduced compared to the baseline heating policy. Furthermore, while the dynamic pivot mechanism requires occupants to pay for their comfort, our payment scheme rewards the occupants whose participation leads to energy savings, making their participation in the scheme more palatable.Publicationtype: Conference Paper30 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Multiple Source Seeking using Glowworm Swarm Optimization and Distributed Gradient Estimation(2018-08-09); In this paper we propose a novel mission control strategy for a group of agents to locate unknown multiple extrema of a time-invariant scalar field. The goal is to provide a complete solution for realistic scenarios such as malfunction of agents or time-varying topology. Here we present MESA - Multiple Extrema Seeking Algorithm, where a group of autonomous agents are able, from any arbitrary initial location (in a bounded area), to locate multiple, unknown extrema. The proposed algorithm includes a combination of bio-inspired attraction with estimated gradient and formation control, where the task switching methodology is performed under the specification of Linear Temporal Logic formula. At first, agents find neighbors and construct groups for completing the search procedure. Once an extremum is 'located', a virtual repulsive force is produced by the pioneer group which as a result cause others to search for other unexplored extrema. We propose a new measure-source density - evaluating the algorithm efficiency which is tested under different simulations for a unicycle agent's model.Publicationtype: Conference Paper140 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Identification of Linear Parameter-Varying Models with Unknown Parameter Dependence Using ϵ-Support Vector Regression(2018-08-09); ; The bias-variance trade-off is very sensitive to the prior selection of functional dependencies when identifying linear parameter-varying (LPV) systems. To counteract this difficulty, various non-parametric methods have been recently proposed. These methods avoid the manual selection of the functional dependency but rather learn it during the identification itself. In this paper, we propose an algorithm to implement the ϵ-tube support vector regression (SVR) approach to such an LPV identification problem. We use some results from the machine learning literature to tune the parameters and provide different directions one could take to optimize these parameters. We demonstrate the effectiveness of our method on an example and compare the results with other methods recently proposed. We observe that because of the insensitive ϵ-tube, the number of parameters was greatly reduced still maintaining the same accuracy in terms of the best fit ratio.Publicationtype: Conference Paper180 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , A Decomposition Approach for a Class of Spatially Interconnected Systems of Finite Spatial Extent(2018-08-09); ; This paper presents a decomposition approach for a class of spatially interconnected systems of finite extent obtained by discretizing partial differential equations including boundary conditions. Such finite extent systems can be modeled as an interconnection of identical subsystems communicating with their nearest neighbors resulting in a lattice-interconnection-topology. Recently the question has been raised whether this class of systems is decomposable. It is shown that these systems are decomposable with respect to a pattern matrix for which a construction law is presented, using the coefficients of the finite difference equation and taking the boundary conditions into account. By bringing the system into a decomposed form, several existing distributed analysis and synthesis techniques can be applied. This approached also helps to bridge the gap between spatially interconnected and multiagent systems. Finally, the approach is demonstrated with stability analysis on two numerical examples.Publicationtype: Conference Paper152 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Distributed stochastic AC optimal power flow based on polynomial chaos expansion(American Automatic Control Council (AACC), 2018-08-09)11