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Item type:Publication, Access status: with files , Data-driven model predictive control with matrix forgetting factorIn this paper, a model predictive control (MPC) based on the Koopman operator framework is presented. This framework, allows to obtain a model solely from data. Moreover, it can be easily updated using a recursive least squares (RLS) approach. This aids the controller to deal with inaccuracies in the model parameters. The performance of the update mechanism can be enhanced by the inclusion of forgetting techniques. One of these is the addition of a constant forgetting factor (CFF) to the update equations. However, this technique can be very sensitive to noise and if the measured signals are not persistently exciting, numerical issues can occur. To avoid the latter, the matrix forgetting factor (MFF) presented in (Bruce et al., 2020) is used. This approach combines two forgetting techniques: variable-rate and variable-direction forgetting, which leads to a higher closed loop performance of the proposed controller.Publicationtype: Conference Paper20 155 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Nonlinear distributed model predictive flocking with obstacle avoidanceIn this paper, we present a framework for nonlinear distributed model predictive flocking with obstacle avoidance, the pursuit of group objectives, and input constraints. While most existing predictive flocking frameworks are only applicable to agents with double-integrator dynamics, we propose a general framework for nonlinear agents that furthermore allows for the independent tuning of cohesive and repulsive inter-agent forces. To reduce the computational complexity, the resulting nonlinear program is solved as a sequential quadratic program with a limited number of iterations. The performance of the proposed algorithms is demonstrated in simulation and compared to a non-predictive flocking algorithm.Publicationtype: Conference Paper36 111 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Gradient-based cooperative control of quasi-linear parameter varying vehicles with noisy gradientsThis paper extends recent results on the exponential performance analysis of gradient based cooperative control dynamics using the framework of exponential integral quadratic constraints. A cooperative source-seeking problem is considered as a specific example where one or more vehicles are embedded in a strongly convex scalar field and are required to converge to a formation located at the minimum of a field. A subset of the agents are assumed to have the knowledge of the gradient of the field evaluated at their respective locations and the interaction graph is assumed to be uncertain. As a first contribution, we extend earlier results on linear systems to non-linear systems by using quasi-linear parameter varying representations. Secondly, we remove the assumption on perfect gradient measurements and consider multiplicative noise in the analysis. Performance-robustness trade off curves are presented to illustrate the use of presented methods for tuning controller gains. The results are demonstrated on a group of non-linear second order vehicles with a velocity-dependent non-linear damping and a local gain-scheduled tracking controller.Publicationtype: Conference Paper32 37 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Towards data-driven predictive control using wavelets(2023-07-01)15 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Koopman model predictive control for wind farm yield optimization with combined thrust and yaw controlTwo novel approaches to data-driven wind farm control via Koopman model predictive control are presented, both combining thrust and yaw control for yield optimization and power reference tracking. The Koopman framework is used to build prediction models to predict wake effects of upwind on downwind turbines. This paper extends previous work by using yaw in addition to thrust control. The test case is a wind farm consisting of two turbines and wind with constant speed and direction parallel to the main axis of the farm. In closed-loop simulation, the two Koopman model predictive control designs reduce the tracking error considerably with regards to a previously published baseline controller, which used solely axial induction control. It is also demonstrated that this can be achieved with relatively small yaw angles, avoiding mechanical loads acting on turbines operating misaligned to the wind, making this a promising approach for further investigations in 3D medium and high fidelity simulation environments.Publicationtype: Conference Paper19 127 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Robust performance analysis for time-varying multi-agent systems with stochastic packet lossRecently, a scalable approach to system analysis and controller synthesis for homogeneous multi-agent systems with Bernoulli distributed packet loss has been proposed. As a key result of that line of work, it was shown how to obtain upper bounds on the H2-norm that are robust with respect to uncertain interconnection topologies. The main contribution of the current paper is to show that the same upper bounds hold not only for uncertain but also time-varying topologies that are superimposed on the stochastic packet loss. Because the results are formulated in terms of linear matrix inequalities that are independent of the number of agents, multi-agent systems of any size can be analysed efficiently. The applicability of the approach is demonstrated on a numerical first-order consensus example, on which the obtained upper bounds are compared to estimates from Monte-Carlo simulations.Publicationtype: Conference Paper22 67 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , A Safe Bayesian Optimization Algorithm for Tuning the Optical Synchronization System at European XFELThe European XFEL is one of the largest linear particle accelerator in the world used to generate extremely intense and ultra short X-Ray flashes to study ultra fast, time resolved chemical processes. In order to improve the quality of the observations, the laser-based optical synchronization system is optimized by tuning PI controllers using a safe Bayesian optimization approach. Since machine time on the European XFEL is very expensive, the algorithm needs to find the optimal parameters as fast as possible. In this contribution, we present a safe Bayesian optimization algorithm which, while guaranteeing safety, shows significantly improved convergence speed and noise robustness. Application and comparison results are presented in simulation for the optical synchronization system of the European XFEL and an experimental demonstration is performed for a laboratory synchronization system.Publicationtype: Conference Paper66 133