Oliver Sawodny

Bio: Oliver Sawodny is an academic researcher from University of Stuttgart. The author has contributed to research in topics: Feed forward & Control theory. The author has an hindex of 34, co-authored 594 publications receiving 5895 citations. Previous affiliations of Oliver Sawodny include Audi & Technische Universität Ilmenau.

A Variable Curvature Continuum Kinematics for Kinematic Control of the Bionic Handling Assistant

Abstract: We present a new variable curvature continuum kinematics for multisection continuum robots with arbitrarily shaped backbone curves assembled from sections with three degrees of freedom (DoFs) (spatial bending and extension, no torsion). For these robots, the forward kinematics and the differential forward kinematics are derived. The proposed model approach is capable of reproducing both the constant and variable backbone curvature in a closed form. It describes the deformation of a single section with a finite number of serially connected circular arcs. This yields a section model with piecewise constant and, thus, a variable section curvature. Model accuracy and its suitability for kinematic real-time control applications are demonstrated with simulations and experimental data. To solve the redundant inverse kinematics problem, a local resolution of redundancy at the velocity level through the use of the robot's Jacobian matrix is presented. The Jacobian is derived analytically, including a concept for regularization in singular configurations. Experimental data are recorded with Festo's Bionic Handling Assistant. This continuum robot is chosen for experimental validation, as it consists of a variable backbone curvature because of its conically tapering shape.

ON/OFF and Beyond - A Boolean Model of Apoptosis

Abstract: Apoptosis is regulated by several signaling pathways which are extensively linked by crosstalks. Boolean or logical modeling has become a promising approach to capture the qualitative behavior of such complex networks. Here we built a largescale literature-based Boolean model of the central intrinsic and extrinsic apoptosis pathways as well as pathways connected with them. The model responds to several external stimuli such as Fas ligand, TNF-a, UV-B irradiation, interleukin1b and insulin. Timescales and multi-value node logic were used and turned out to be indispensable to reproduce the behavior of the apoptotic network. The coherence of the model was experimentally validated. Thereby an UV-B dose-effect is shown for the first time in mouse hepatocytes. Analysis of the model revealed a tight regulation emerging from high connectivity and spanning crosstalks and a particular importance of feedback loops. An unexpected feedback from Smac release to RIP could further increase complex II formation. The introduced Boolean model provides a comprehensive and coherent description of the apoptosis network behavior. It gives new insights into the complex interplay of pro- and antiapoptotic factors and can be easily expanded to other signaling pathways.

A Two-Stage Model Predictive Control Strategy for Economic Diesel-PV-Battery Island Microgrid Operation in Rural Areas

Abstract: Microgrids consisting of diesel generators, storage devices, and renewable sources present an effective approach for an economic energy supply to rural areas. Advanced control methods are needed to improve the energy dispatch, enable a cost-efficient operation and guarantee an uninterrupted power supply. In particular, sudden variations in load demand or additional power supply from renewable sources are often unpredictable and underline the need for enhanced control. This paper presents an advanced control strategy for the optimal microgrid operation using a two-layer model predictive method. The first optimization layer presents an optimal control problem, based on real-time predictions of future power profiles, for the calculation of the optimal energy dispatch. To improve the robustness of the control strategy toward prediction errors, a boundary value problem is solved to adjust the diesel generator power in the second stage. The model predictive control framework is further used to adapt the weights of the forecast algorithm. Simulation studies are carried out by using real-world data to illustrate the performance and economic benefits of the proposed method. Results show the effectiveness of the control strategy in terms of computational feasibility, accuracy, increased robustness, and reduced cost.

Active Control for an Offshore Crane Using Prediction of the Vessel’s Motion

Abstract: During offshore installations in harsh sea conditions, the involved crane system must satisfy rigorous requirements in terms of safety and efficiency. The forces resulting from the vertical motion of the vessel have an extensive effect on the overall crane structure and its lifetime. Moreover, vessel motion handicaps the operator during fine positioning of the payload. Hence, an active compensation system for the vertical vessel motion is proposed. An important point to consider for such systems is the time delay between the sensors and actuators, which diminishes performance. To compensate the dead times in the system, a prediction algorithm for the vertical motion of the vessel is proposed in the first part. In the second part, an inversion-based control strategy for the hydraulic-driven winch is formulated that considers the dynamic behavior of the drive system. A feedforward controller compensates the vertical-motion disturbance using the predicted motion. The proposed controller together with the prediction algorithm decouple the motion of the rope-suspended payload from the vessel's motion. The active compensation approach is evaluated with simulation and measurement results.

Nonlinear Model Predictive Control of a Point Absorber Wave Energy Converter

Abstract: This paper presents the application of nonlinear model predictive control (NMPC) to a point absorber wave energy converter (WEC). Model predictive control (MPC) is generally a promising approach for WECs, since system constraints and actuator limits can be taken into account. Moreover, it provides a framework for defining optimal energy capture and it can benefit from predictions. Due to possible nonlinear effects, such as the mooring forces, an NMPC is proposed in this paper, whose performance is compared to that of a linear MPC. Both controllers are supposed to control a nonlinear point absorber model. Computer simulations show that the proposed NMPC is able to optimize the energy capture while satisfying system limits.

Coverage control for mobile sensing networks

Abstract: This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Linear systems

Abstract: Most of the signal processing that we will study in this course involves local operations on a signal, namely transforming the signal by applying linear combinations of values in the neighborhood of each sample point. You are familiar with such operations from Calculus, namely, taking derivatives and you are also familiar with this from optics namely blurring a signal. We will be looking at sampled signals only. Let's start with a few basic examples. Local difference Suppose we have a 1D image and we take the local difference of intensities, DI(x) = 1 2 (I(x + 1) − I(x − 1)) which give a discrete approximation to a partial derivative. (We compute this for each x in the image.) What is the effect of such a transformation? One key idea is that such a derivative would be useful for marking positions where the intensity changes. Such a change is called an edge. It is important to detect edges in images because they often mark locations at which object properties change. These can include changes in illumination along a surface due to a shadow boundary, or a material (pigment) change, or a change in depth as when one object ends and another begins. The computational problem of finding intensity edges in images is called edge detection. We could look for positions at which DI(x) has a large negative or positive value. Large positive values indicate an edge that goes from low to high intensity, and large negative values indicate an edge that goes from high to low intensity. Example Suppose the image consists of a single (slightly sloped) edge:

Adaptive Control

Abstract: This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

Heat and Mass Transfer

Abstract: Thermophysical Properties Research Literature Retrieval Guide Edited by Y. S. Touloukian, J. K. Gerritsen and N. Y. Moore Second edition, revised and expanded. Book 1: Pp. xxi + 819. Book 2: Pp.621. Book 3: Pp. ix + 1315. (New York: Plenum Press, 1967.) n.p.