FG Regelungssysteme und Netzleittechnik
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Drones can be connected as a swarm to precisely monitor large agricultural areas and manage them more sustainable. They test sensor technology in real environments and solve complex tasks faster. Key aspects include accurate localization, reliable communication, and dynamic flight control. The localization accuracy of RTK-GNSS-enabled system, which includes GPS, GLONASS, GALILEO positioning systems is compared with Ultra-Wideband (UWB) one. The UPWARDS communication hub is presented as a processing and connection point across swarm agents. This controller also incorporates collision avoidance features and testing of evasion strategies.
Design of controls for ISS and Integral ISS Stabilization of Multistable State Periodic Systems
(2023)
Sufficient conditions for almost global synchronization in acyclic networks of Kuramoto oscillators with heterogeneous coupling strengths and natural frequencies are presented. The result is established by employing the recently developed Leonov function framework for systems whose dynamics are periodic for all state variables. The synchronization property is accompanied by necessary and sufficient conditions to guarantee the existence of equilibria. The implications of these conditions on the network topology, the oscillator's coupling strengths and natural frequencies are discussed. Finally, the results are illustrated via a numerical example.
We propose an optimal operation controller for an electro-thermal microgrid. Compared to existing work, our approach increases flexibility by operating the thermal network with variable flow temperatures and in that way explicitly exploits its inherent storage capacities. To this end, the microgrid is represented by a multi-layer network composed of an electrical and a thermal layer. We show that the system behavior can be represented by a discrete-time state model derived from DC power flow approximations and 1d Euler equations. Both layers are interconnected via heat pumps. By combining this model with desired operating objectives and constraints, we obtain a constrained convex optimization problem. This is used to derive a model predictive control scheme for the optimal operation of electro-thermal microgrids. The performance of the proposed operation control algorithm is demonstrated in a case study.
We present new results for the analysis of global boundedness of state periodic systems. Thereby, we address both the case of systems, whose dynamics is periodic with respect to a part of the state vector, and the case of systems, whose dynamics is periodic with respect to all state variables. To derive the results, the notion of strong Leonov functions is introduced. The main results are complemented by a number of relaxations based on the concept of weak Leonov functions.
Interior permanent magnet synchronous machines (IPMSMs) are gaining popularity in e-mobility applications due to their wide constant power speed range when compared with other electric motors. The high-performance of IPMSMs is due to the pronounced degree of magnetic saliency, which originates from embedding their magnets inside the rotor. Yet, this feature complicates their control since it increases nonlinear phenomena such as cross-magnetization and saturation. This results in pronounced variations of the machine inductances, which are highly dependent on the machine current. This behaviour is of particular relevance in e-mobility applications, where a highly dynamic operation of the machine is required. In contrast to most existing current control approaches, where precise inductance knowledge is required, we address this challenge by deriving a controller that exploits the machine dynamics to achieve exponential current tracking in the presence of unknown and varying inductances. By interpreting the flux linkage as a modelled disturbance, we propose a dynamic compensator based on the internal model principle. As a by-product, the method provides an estimate of the flux linkage. The gain in performance compared to the standard PI controller is illustrated in simulation.
Dynamic state and parameter estimation (DSE) plays a key role for reliably monitoring and operating future, power-electronics-dominated power systems. While DSE is a very active research field, experimental applications of proposed algorithms to real-world systems remain scarce. This motivates the present paper, in which we demonstrate the effectiveness of a DSE algorithm previously presented by parts of the authors with real-world data collected by a Phasor Measurement Unit (PMU) at a substation close to a power plant within the extra-high voltage grid of Germany. To this end, at first we derive a suitable mapping of the real-world PMU-measurements recorded at a substation close to the power plant to the terminal bus of the power plants’ synchronous generator. This mapping considers the high-voltage transmission line, the tap-changing transformer and the auxiliary system of the power plant. Next, we introduce several practically motivated extensions to the estimation algorithm, which significantly improve its practical performance with real-world measurements. Finally, we successfully validate the algorithm experimentally in an auto- as well as a cross-validation.
Small Unmanned Aerial Vehicles (UAVs) have vast application potential from industrial logistics and disaster monitoring to smart farming, for example, to create maps with Normalized Difference Vegetation Index (NDVI) cameras and AI-based image classification or the precision spot application of fertilizers. Operating multiple UAVs in parallel, including those with different specializations, enables efficient coverage of large areas. While existing research focused mainly on predefined flight formations for swarms or autonomous missions for single UAVs, the focus of this work is on multiple UAVs with individually adaptable missions. We derive communication system requirements from swarm coordination algorithms and present a respective design. The resulting modular architecture enables future research on autonomous adaptive UAV swarms and their use as flying sensor platforms.
A novel characterization of the integral Inputto-
State Stability (iISS) property is introduced for multistable
systems whose dynamics are periodic with respect to a part of
the state. First, the concepts of iISS-Leonov functions and output
smooth dissipativity are introduced, then their equivalence
to the properties of bounded-energy-bounded-state and global
attractiveness of solutions in the absence of disturbances are
proven. The proposed approach permits to relax the usual
requirements of positive definiteness and periodicity of the iISSLyapunov
functions. Moreover, the usefulness of the theoretical
results is illustrated by a robustness analysis of a nonlinear
pendulum with a constant bias input and an unbounded statedependent
input coefficient.
The worldwide transition to climate-friendly energy systems entails the substitution of conventional energy generation based on synchronous generators by renewable energy sources based on power electronics. As a consequence, the overall inertia of the grid decreases, resulting in high volatility of the frequency and posing new challenges for the estimation of the latter quantity. To address this problematic, in the present paper a phase-locked-loop (PLL) based on the super-twisting algorithm is considered for the estimation of the phase angle and time-varying frequency of a symmetric three-phase signal. A rigorous proof of the algorithm’s exact convergence in the presence of a fast-varying frequency together with tuning rules for its gains are derived by means of Lyapunov theory. Additionally, an estimate of the region of attraction is provided. The effectiveness of the proposed tuning method is illustrated in numerical simulations, while comparing its performance against a standard synchronous reference frame PLL.
We propose a switched control law for a DC-DC Buck converter that enables a grid-aware operation of a distributed generator (DG) in a low-voltage islanded DC microgrid (MG). The qualifier grid-aware means that the DG adjusts its operation mode in dependency of the MG status. The resulting closed-loop system is a switched system, in which the subsystems possess different equilibrium points. By means of a time- and state-dependent switching logic together with dwell-time stability analysis methods, we derive sufficient stability criteria that ensure the existence of a unique and globally exponentially stable equilibrium point of the resulting closed-loop switched system. The performance of the proposed control is illustrated via a numerical example.
Due to its simplicity and inexpensive computation, the gradient descent algorithm is one of the most used tools in adaptive control and system identification. Although it has been studied for decades, little has been achieved in terms of tuning methods in the presence of disturbances. One of the main difficulties in its analysis is the time-varying nature of the algorithm. In this work, we contribute in such direction by providing LMI tools for tuning the gradient descent algorithm gain such that a guaranteed upper bound on the L2 -gain with respect to parameter variations and measurement noise is achieved. Two academic examples are provided to illustrate the efficient application of the method.
We address the problems of modeling and controlling multi-energy microgrids (meMGs) composed of an electrical and a thermal system, which are connected via heat pumps (HPs). At first, we model the individual subsystems in a port-Hamiltonian (pH) framework. Then, by exploiting the structural properties of pH systems, we interconnect the subsystems in a passive manner and show that the overall meMG is shifted passive with respect to the control input-output mapping. We then use this property to propose a distributed passivity based-control (PBC) that addresses frequency and temperature regulation by utilizing the resources in the meMG in a proportional fashion and renders the closed-loop equilibrium asymptotically stable.
The increasing ease of obtaining and processing data together with the growth in system complexity has sparked the interest in moving from conventional model-based control design toward data-driven concepts. Since in many engineering applications time delays naturally arise and are often a source of instability, we contribute to the data-driven control field by introducing data-based formulas for state feedback control design in linear discrete-time time-delay systems with uncertain delays. With the proposed approach, the problems of system stabilization as well as of guaranteed cost and H∞ control design are treated in a unified manner. Extensions to determine the system delays and to ensure robustness in the event of noisy data are also provided