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Nonparametric representations of dynamical systems based on the image of a Hankel matrix of data are extensively used for data-driven control. However, if samples of data are missing, obtaining such representations becomes a difficult task. By exploiting the kernel structure of Hankel matrices of irregularly measured data generated by a linear time-invariant system, we provide computational methods for which any complete finite-length behavior of the system can be obtained. For the special case of periodically missing outputs, we provide conditions on the input such that the former result is guaranteed. In the presence of noise in the data, our method returns an approximate finite-length behavior of the system. We illustrate our result with several examples, including its use for approximate data completion in real-world applications and compare it to alternative methods.

Many industrial production processes use sensors to generate information about the manufacturing process. In this way, monitoring machine tools ensures the proper functionality of the system and detects unexpected behavior due to, for example, material inhomogeneity, incorrect data input, temperature influence, or tool wear. In large production machines, such as portal milling machines, the sensor equipment of guide carriages can be an important possibility since they are a standardized component and can be easily integrated into existing machines. Here, conventional polymer foil-based strain gauges show several disadvantages due to reproducibility and reliability of the sensor connection via adhesive. Thus, this article addresses the manufacturing of directly deposited chromium thin-film strain gauges on a guide carriage with integrated programmable data preamplification. Tests with different sensor materials on steel substrates showed that chromium was the most suitable sensor material with a high k-factor. Then, one end face of the carriage was polished before sputtering an Al2O3 insulation layer and a chromium sensor layer that was laser-structured afterward to produce two Wheatstone full bridges at previously simulated sensor positions. In a tensile test stand, the calibration of the sensors took place in the two spatial directions perpendicular to the guide rail direction. With an additional sensor data fusion for the final interpretation of measured forces, it is shown that this sensor technology is suitable for force measurement at guide carriages.

In this article, we present a sequential distributed model predictive control (MPC) scheme for cooperative control of multiagent systems with dynamically decoupled heterogeneous nonlinear agents subject to individual constraints. In the scheme, we explore the idea of using tracking MPC with artificial references to let agents coordinate their cooperation without external guidance. Each agent combines a tracking MPC with artificial references, the latter penalized by a suitable coupling cost. They solve an individual optimization problem for this artificial reference and an input that tracks it, only communicating the former to its neighbors in a communication graph. This puts the cooperative problem on a different layer than the handling of the dynamics and constraints, loosening the connection between the two. We provide sufficient conditions on the formulation of the cooperative problem and the coupling cost for the closed-loop system to asymptotically achieve it. Since the dynamics and the cooperative problem are only loosely connected, classical results from distributed optimization can be used to this end. We illustrate the scheme's application to consensus and formation control.

In this article, a robust data-driven moving horizon estimation (MHE) scheme for linear time-invariant discrete-time systems is introduced. The scheme solely relies on offline collected data without employing any system identification step. We prove practical robust exponential stability for the setting where both the online measurements and the offline collected data are corrupted by nonvanishing and bounded noise. The behavior of the novel robust data-driven MHE scheme is illustrated by means of simulation examples and compared with a standard model-based MHE scheme, where the model is identified using the same offline data as for the data-driven MHE scheme.

A new approach for the texturing of monocrystalline silicon solar wafers was developed. By using a spray etching process with aqueous HF-HCl-Cl2 mixtures, textured wafers were generated and processed to PERC solar cells and compared to cells textured by a conventional alkaline texturing process. Also, a novel saw damage removal (SDR) process using aqueous HF-Cl2 mixtures was tested against the typical NaOH SDR. A possible dependency of cell parameters on the wafer reflectivity is discussed. The etch rates of the spray etching process are compared to other chlorine-containing hydrofluoric acid-based processes and alkaline potassium hydroxide-based mixtures. Computational fluid dynamic analyses were performed to identify possible reasons for marks occurring on the spray textured wafers. The influence of the marks on the electrical properties were examined with electroluminescence measurements. As cell performance data show promising results, we discuss whether the spray etching process with HF-HCl-Cl2 mixtures is a viable way for the implementation of an inline texturing process for solar cell production.

Recently, many reactive trajectory planning approaches were suggested in the literature because of their inherent immediate adaption in the ever more demanding cluttered and unpredictable environments of robotic systems. However, typically those approaches are only locally reactive without considering global path planning and no guarantees for simultaneous collision avoidance and goal convergence can be given. In this article, we study a recently developed circular field (CF)-based motion planner that combines local reactive control with global trajectory generation by adapting an artificial magnetic field such that multiple trajectories around obstacles can be evaluated (cf., Becker et al., 2021). In particular, we provide a mathematically rigorous analysis of this planner for static environments in the horizontal plane to ensure safe motion of the controlled robot. Contrary to existing results, the derived collision avoidance analysis covers the entire CF motion planning algorithm including attractive forces for goal convergence and is not limited to a specific choice of the rotation field, i.e., our guarantees are not limited to a specific potentially suboptimal trajectory. Our Lyapunov-type collision avoidance analysis is based on the definition of an (equivalent) 2-D auxiliary system, which enables us to provide tight, if and only if conditions for the case of a collision with point obstacles. Furthermore, we show how this analysis naturally extends to multiple obstacles and we specify sufficient conditions for goal convergence. Finally, we provide challenging simulation scenarios with multiple nonconvex point cloud obstacles and demonstrate collision avoidance and goal convergence.

Future robots operating in fast-changing anthropomorphic environments need to be reactive, safe, flexible, and intuitively use both arms (comparable to humans) to handle task-space constrained manipulation scenarios. Furthermore, dynamic environments pose additional challenges for motion planning due to a continual requirement for validation and refinement of plans. This work addresses the issues with vector-field-based motion generation strategies, which are often prone to local-minima problems. We aim to bridge the gap between reactive solutions, global planning, and constrained cooperative (two-arm) manipulation in partially known surroundings. To this end, we introduce novel planning and real-time control strategies leveraging the geometry of the task space that are inherently coupled for seamless operation in dynamic scenarios. Our integrated multiagent global planning and control scheme explores controllable sets in the previously introduced cooperative dual task space and flexibly controls them by exploiting the redundancy of the high degree-of-freedom (DOF) system. The planning and control framework is extensively validated in complex, cluttered, and nonstationary simulation scenarios where our framework is able to complete constrained tasks in a reliable manner, whereas existing solutions fail. We also perform additional real-world experiments with a two-armed 14 DOF torque-controlled KoBo robot. Our rigorous simulation studies and real-world experiments reinforce the claim that the framework is able to run robustly within the inner loop of modern collaborative robots with vision feedback.

We provide a novel robust stability analysis for moving horizon estimation (MHE) using a Lyapunov function. In addition, we introduce linear matrix inequalities (LMIs) to verify the necessary incremental input/output-to-state stability ($\boldsymbol{\delta }$-IOSS) detectability condition. We consider an MHE formulation with time-discounted quadratic objective for nonlinear systems admitting an exponential $\boldsymbol{\delta }$-IOSS Lyapunov function. We show that with a suitable parameterization of the MHE objective, the $\boldsymbol{\delta }$-IOSS Lyapunov function serves as an $\boldsymbol{M}$-step Lyapunov function for MHE. Provided that the estimation horizon is chosen large enough, this directly implies exponential stability of MHE. The stability analysis is also applicable to full information estimation, where the restriction to exponential $\boldsymbol{\delta }$-IOSS can be relaxed. Moreover, we provide simple LMI conditions to systematically derive $\boldsymbol{\delta }$-IOSS Lyapunov functions, which allows us to easily verify $\boldsymbol{\delta }$-IOSS for a large class of nonlinear detectable systems. This is useful in the context of MHE in general, since most of the existing nonlinear (robust) stability results for MHE depend on the system being $\boldsymbol{\delta }$-IOSS (detectable). In combination, we thus provide a framework for designing MHE schemes with guaranteed robust exponential stability. The applicability of the proposed methods is demonstrated with a nonlinear chemical reactor process and a 12-state quadrotor model.

In this article, we present an extension of Willems' Fundamental Lemma to the class of multi-input–multi-output discrete-time feedback linearizable nonlinear systems, thus providing a data-based representation of their input–output trajectories. Two sources of uncertainty are considered. First, the unknown linearizing input is inexactly approximated by a set of basis functions. Second, the measured output data is contaminated by additive noise. Furthermore, we propose an approach to approximate the solution of the data-based simulation and output matching problems, and show that the difference from the true solution is bounded. Finally, the results are illustrated on an example of a fully actuated double inverted pendulum.