9 results
Filters
Settings
Now showing1 - 9 of 9
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , A Gradient Descent Method for Finite Horizon Distributed Control of Discrete Time Systems(2021-12); In this paper we consider the problem of designing distributed static state, static output and dynamic output feedback controllers for discrete time systems. The proposed approach is based on iteratively minimizing a finite horizon quadratic cost function, where the descent directions are determined using simulated system trajectories. When the system matrices are sparse, the computational complexity of the proposed algorithm scales linearly with the size of the system.Publicationtype: Conference Paper21 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Koopman-based Kalman Filter for Fault Detection for the Superconducting Radio Frequency Cavities of the European XFELThe Koopman operator is a novel approach to embed nonlinear dynamics into linear models. This work shows its successful application for fault detection to a large-scale facility with challenging real-time requirements: the European XFEL, which is the worldwide largest linear particle accelerator. We concentrate on the superconducting radio-frequency cavities, from which 808 exist and whose effective operation directly influences the performance of the whole facility. Thus, a proper fault detection scheme is desired. While a nonlinear state-space description of the cavity dynamics is well-known, its usage along with an unscented Kalman filter is not able to cope with the challenging online implementation requirements. Therefore, in this paper, we apply the Koopman operator technique to identify a finite-dimensional linear approximation of the nonlinear system. For the data-driven identification, the model knowledge is exploited by choosing physically motivated basis functions. With the linear approximation at hand, a linear Kalman filter can be applied. Results are presented for real experimental data. Compared to the unscented Kalman filter, the same detection capability but a speed-up of three orders of magnitude in calculation time can be achieved with the Koopman-based Kalman filter, which enables its implementation to the real facility.Publicationtype: Conference Paper78 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Inferring the adjoint turnpike property from the primal turnpike propertyThis paper investigates an interval turnpike result for the adjoints/costates of finite-and infinite-dimensional nonlinear optimal control problems under the assumption of an interval turnpike on states and controls. We consider stabilizable dynamics governed by a generator of a semigroup with finite-dimensional unstable part satisfying a spectral decomposition condition and show the desired turnpike property under continuity assumptions on the first-order optimality conditions. We further provide a numerical example with a semilinear heat equation to illustrate the results.Publicationtype: Conference Paper12 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Decentralized Output Feedback Control using Sparsity Invariance with Application to Synchronization at European XFEL(IEEE, 2021-12); ; ; ; In this work, we apply the recent framework of sparsity invariance to a large-scale facility, the European XFEL. With the sparsity invariance framework, the most general known class of convex approximations of distributed controller design problems is characterized. It is shown in previous work that this convex restriction can perform at least as well as all existing approximations for the possibly NP-hard problem of distributed controller design. The optical synchronization system at the European XFEL can be modeled as a distributed system with a tree or chain topology. Whereas in previous work based on the spatial invariance framework either static state feedback or high-order dynamic output feedback controller design is presented, we extend it in this work to static or fixed-order output feedback control and apply it to the system at hand. We show simulation results motivated by the real application and demonstrate that the convex approximation can outperform a local baseline tuning.Publicationtype: Conference Paper54 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Convergence Properties of Fast quasi-LPV Model Predictive Control(2021-12); In this paper, we study the convergence properties of an iterative algorithm for fast nonlinear model predictive control (MPC) of quasi-linear parameter-varying systems without inequality constraints. Compared to previous works considering this algorithm, we contribute conditions under which the iterations are guaranteed to converge. Furthermore, we show that the algorithm converges to suboptimal solutions and propose an optimality-preserving variant with moderately increased computational complexity. Finally, we compare both variants in terms of quality of solution and computational performance with a state-of-the-art solver for nonlinear MPC in two simulation benchmarks.Publicationtype: Conference Paper43 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Event-Triggered ℓ₂-Optimal Formation Control for Agents Modeled as LPV Systems(2021-12); This paper proposes a novel approach to event-triggered formation control for homogeneous, non-holonomic multi-agent systems with undirected interaction topology, where the non-holonomic vehicle dynamics are represented by poly-topic linear-parameter-varying (LPV) models. The proposed event-triggered strategy is able to reduce the communication cost by transmitting information only when needed. To maintain a formation, each agent is equipped with an inner state-feedback loop that is time-triggered, while an outer position loop is closed by each agent individually through the communication network whenever a local trigger condition is satisfied. The control strategy can be implemented in a distributed manner; the trigger condition is based only on locally available information. The proposed method allows to simultaneously design a controller and a trigger level that guarantee stability and a bound on the overall ℓ2 performance of the network. The synthesis problem is formulated as an LMI problem. Under the additional assumption that the agents are homogeneously scheduled, the synthesis problem can be decomposed to reduce its complexity to the size of a single agent, regardless of the number of agents, without degrading the performance. The effectiveness of the results is illustrated in a simulation scenario with non-holonomic agents modeled as dynamic unicycles.Publicationtype: Conference Paper22 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , An essentially decentralized interior point method for controlDistributed and decentralized optimization are key for the control of networked systems. Application examples include distributed model predictive control and distributed sensing or estimation. Non-linear systems, however, lead to problems with non-convex constraints for which classical decentralized optimization algorithms lack convergence guarantees. Moreover, classical decentralized algorithms usually exhibit only linear convergence. This paper presents an essentially de-centralized primal-dual interior point method with convergence guarantees for non-convex problems at a superlinear rate. We show that the proposed method works reliably on a numerical example from power systems. Our results indicate that the proposed method outperforms ADMM in terms of computation time and computational complexity of the subproblems.Publicationtype: Conference Paper9 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , An Information Flow Filter Approach to Formation Control for Heterogeneously Scheduled LPV Agents(2021-12); This paper studies the formation control problem for a group of non-holonomic agents modeled as LPV systems with heterogeneous scheduling. Here we use an approach based on an information flow filter that makes it possible to design the dynamics of the information flow in the network. We propose a synthesis condition in the form of a linear matrix inequality for a distributed control scheme that guarantees stability and a level of mathcalL₂ performance. Moreover, by using the framework of decomposable systems, the complexity of the synthesis problem can be made independent of the network size; it corresponds to the size of the synthesis problem for a single agent. The work presented here extends earlier work where a similar scheme was proposed, however restricted by the assumption that the LPV agents are scheduled homogeneously. Whereas in that work the agents were modeled as polytopic LPV models, here the extension to heterogeneously scheduled agents is achieved by using LFT representations of the agents and employing the full-block S-procedure for synthesis. The proposed method is illustrated in simulation by applying it to a formation control problem for a group of dynamic unicycles.Publicationtype: Conference Paper23 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Distributed Control of Heterogeneous Networks of Vehicles with Positive Systems Theory and Generalized H₂Norm(2021-12); For interconnections of vehicles with complex dynamics, it is commonly suggested to design controllers locally at agents so that agents track the trajectories generated by simplified dynamics. We analyze the performance of such a decoupled control architecture by considering the l∞ norm of the local tracking error, which allows to quantify the deviation of the actual system from the simplified system and either provide a justification for the decoupled architecture or show a need to consider instead a coupled architecture. Specifically, we consider first-order protocols as the interaction mechanism among agents and general discrete-time Linear Time-Invariant (LTI) dynamics as agent models which track the trajectories generated by the first-order protocols. For the analysis and synthesis of first-order protocols, we do not assume a priori knowledge of the connectivity of the graph or the spectrum of the Laplacian matrix, but use results on positive systems to obtain conditions that can scale linearly with network size. We provide bounds on the loss in performance due to imperfect tracking of first order dynamics by higher order agent dynamics and due to disturbances acting locally on agents. Numerical simulations involving generic second order vehicle models with damping illustrate the applicability of the results.Publicationtype: Conference Paper36