Automation of tower cranes and construction sites is an ongoing research field driven by high demands for productivity and safety. High-level automation necessitates basic functionalities such as sway control, which is realized through appropriate controllers. To achieve high control performance, accurate mathematical models of the machines are required. In tower cranes, hydraulic drives reduce cost and on site assembly time but introduce nonlinear behaviour compared to electric drives. Additionally, due to temperature-dependent viscosity, hydraulic system dynamics are sensitive to oil temperature. Disregarding this effect can lead to model errors and decreased controller performance. In this work, an orifice equation that considers the oil temperature is derived. The unknown parameters are identified with measurements from an industrial hydraulically actuated tower crane. It is shown that with the proposed model the robustness against varying temperature can be vastly improved.

In-hand manipulation tasks using hand-like robotic grippers offer a promising approach to accomplish various tasks in a human-centered environment. Due to the inherent safety of soft robots, in-hand manipulations performed by soft robots provide great opportunities for future human-robot collaboration, which is the scope of this paper. By modeling a new and innovative soft-robotic gripper known as the Anthropomorphic Soft Gripper and synthesis of an open-loop controller with deep reinforcement learning, it is demonstrated how the movement of objects by in-hand manipulations can be accomplished. Moreover, this work explores the application of deep reinforcement learning methods without the employment for domain randomization. As noted by Bhatt et al. the inherent soft properties of soft robotic grippers enable remarkably robust in-hand manipulation in open-loop control, giving the impetus for the approach that is being followed in this work. Motion sequences generated in simulation are successfully transferred to the real anthropomorphic soft gripper and validated in experiments.

Soft continuum robots enable new application areas in contrast to standard rigid robots, such as interaction with a varying environment. Due to their compliant continuous structure, they are inherently safe and adaptive to environmental conditions. In this paper, the interaction with the environment is performed at the tool-center-point of a soft continuum manipulator and is realized by a hybrid force-position control. For this, a force estimation model is derived to substitute the force sensor at the tool-center-point. The force estimation is probabilistic and relies on normal distributions considering model parameters and deviations from model identification of the soft continuum robot. It also provides a qualitative measure for the contact estimation. This paper first presents the probabilistic force estimation model and then shows the hybrid force-position control using the presented model. From the results, it is concluded that force sensing is replaceable for the environment interaction.

Age-structured population models are an intuitive way to model competing bacteria populations in bioreactors, and are of interest for biotechnology processes, wastewater treatment, or epidemics. Such multi-population models with competition terms result in coupled partial differential equations with integral terms and non-local boundary conditions. They represent the population density of each species at a specific time and age. In this work, a model to represent two intra-and interspecific competing populations in a bioreactor is introduced. It has two system inputs, namely the dilution rate with nutrient solution and a recycling rate which introduces biomass in a steady-state from a second bioreactor. Adding a recycling rate to the multi-population models allows for influencing not only the entire biomass in the bioreactor but also the age distribution of the bacteria. In order to use population models to develop improved control concepts for such a cascaded bioreactor experiment, an extensive steady-state analysis is carried out. There exists an infinite number of steady-states of the system in dependence of the initial condition. Each choice of that initial condition lead to uniquely determined steady-state profiles, inputs and outputs. In a next step, a stabilization around these steady-states is necessary.

Adaptive high-rise buildings use multiple sensor systems, actuators integrated into the structure, and a control unit in order to actively counteract external disturbances. In civil engineering, a distinction is made between static loads such as snow, and dynamic loads, e.g. wind and earthquakes. These result in two control objectives, each of which is achieved by a separate controller. A static load compensation method is employed to minimize static displacements, while a model predictive controller induces additional damping into the structure to suppress structural vibrations. Here, both controllers use the same set of actuators with limited forces. This paper presents an investigation of the control input requirement for static load compensation and vibration damping. An algorithm for optimal control input distribution over the different control objectives is implemented to achieve good performance of the overall system. The method is tested in simulations considering a wind disturbance. By applying the introduced control strategy, the closed loop achieves a performance improvement of 14% with regard to the building's displacements and velocities compared to the application of solely a model predictive controller.

Automation functionalities of mobile construction site vehicles continue to improve constantly with the growth of the industry. Advanced sensor technology and enhanced computational power allow for the development of innovative and robust assistance systems. By applying control algorithms to the machines, they are enabled to operate on a construction site autonomously. Whereas such use cases expand the utilization of these vehicles, existing features can be improved as well. For instance, load limit control avoids engine stalling of a mobile machine in critical scenarios by regulation of the engine dynamics. This work presents a feed-forward control approach for a telescopic handler's driving functionality, which is actuated by a hydro-mechanical powertrain and powered by a diesel engine. Application of the proposed method to the real system yields a quality of life improvement through a more reliable prevention of engine stalling due to a lower reaction time to external loads. Furthermore, the engine speed decrease is reduced by efficiently regulating the drive pump.

Correctly classifying tumorous tissue and the resection margins proves to be a difficult task in endoscopic bladder cancer surgeries. As a tumor shows altered electrical properties, intraoperative impedance measurements can support the surgeon to classify the tissue. An important step in this process is the decision on how to pass the impedance information to the classification algorithm. This can be either in the form of a raw measurement vector or based on parameters extracted from the measurement. These parameters arise from curve characteristics of the Nyquist diagram, electrical tissue models, or indices defined as ratios between specific measurement points. In this work, different features proposed in the literature are reviewed and extracted from impedance measurements taken on bladder tissue. The most influential measurement frequencies are determined via a principle component analysis, and the most promising parameters are selected based on an analysis of variance. The different feature extraction approaches are compared based on the classification accuracy on unseen test data. The accuracy is very high for all different ways to define features, and it increases for every approach when only considering the selected features. Especially for the Nyquist parameters and indices, as well as the reduced measurement vector, the number of necessary frequencies is significantly reduced. This shortens the measurement, which is favorable for an intraoperative application.

Modern vehicles are equipped with a growing amount of advanced driver assistance functions (ADAS), such as adaptive cruise control or collision avoidance systems. These functions heavily rely on the current state of the vehicle. Particularly for lateral vehicle dynamics functions, the state of the tires has a crucial influence. For moderate driving scenarios, the vehicle lateral tire forces are mainly described by the tire cornering stiffnesses, which give a linear relation between tire slip angles and the resulting lateral tire forces. However, the tire cornering stiffnesses heavily depend on the type of tire and on many parameters, most significantly the tire vertical load, the tire wear and the tire pressure. As such changes in tire cornering stiffness can lead to a completely different vehicle behavior, it is essential to obtain current cornering stiffness estimates for the application of ADAS. Thus, this paper proposes a combined beta-less approach for vehicle cornering stiffness and road-friction coefficient estimation based on a nonlinear tire model, which solely makes use of typical vehicle in-series sensors. Vehicle measurements on dry road as well as on ice show the effectiveness of the proposed method.

Quantization errors occur when a signal is measured by digital sensors that have naturally a limited accuracy. When applying position control to a large scale robot, these quantization errors cause steps in the control input that possibly excite structural oscillations. To prevent this phenomenon, a state observer is designed that provides smooth estimated states with high accuracy for systems with quantized output. It is realized by observing the error dynamics of the measurement signal. The resulting observer is updated at each time instance where the quantized value of the measured signal changes. By using a corrected value of the output as well as an orthogonal projection, the updated estimation error leads to smooth estimated states. The design of this triggered observer does not have more design parameter than a conventional Luenberger observer and is therefore readily to implement. However, the overall estimation error is smaller in a direct comparison to such a Luenberger observer which is shown in simulations using a model of a mechanical drive system.

Functionally graded concrete (FGC) is a novel technology in the building industry, allowing for savings of up to 50% mass in standard concrete elements like slabs and beams. This is achieved by cavities in the elements interior, which are generated by placing mineral hollow spheres inside the formwork before casting. The concrete is then casted with a conveying system (CS), consisting of a pump and an extrusion unit, mounted on a manipulator. An important challenge for automation is trajectory generation for the CS and the manipulator, which accounts for physical system restrictions and the fabrication process. In this paper, the problem is described for the case of rectangular elements with hollow spheres in a simple cubic pattern. As buoyancy forces would lead to floating hollow spheres in a one-shot production, suitable layer heights are calculated by an optimization problem in a first step. Then, a boustrophedon path is planned, which neatly covers the element while avoiding the spheres. Using the element configuration, the volume demand along the path can be calculated, which serves as a cost function. This allows the formulation of an optimal control problem, which is solved with multiple shooting, in order to obtain the input trajectorys.

In order to make the construction industry more sustainable, timber structures represent a good alternative to common concrete or steel buildings. The individual design of timber elements justify the investigation of new on-site assembly concepts. To combine the precision and rigidity of a manipulator with the load capacity of a tower crane, this work considers a novel crane-collaboration for the on-site assembly of timber structures, where a spider crane with low load capacity manipulates the payload and a tower crane compensates the large gravitational forces of the timber elements. This work presents the first dynamical model and leader-follower control concept of such a crane-collaboration. The two cranes are modeled as closed kinematic chain. The manipulator serves as leader and the tower crane serves as follower. Simulation results show that the presented leader-follower control structure achieves a compensation of the payload’s entire gravitational force by the tower crane which is promising for the application of the novel assembly concept in practice.

This paper describes a novel nonlinear flatness-based state observer for vehicle dynamics control. The differential flatness property of a nonlinear vehicle model is used to derive a state observer for the lateral dynamics of a vehicle. Furthermore, a second state observer for the combined lateral and longitudinal movement is presented. Additionally, the flat outputs and the state transformations to bring both models into observability normal form are shown and the observer equations are derived. For this purpose, it is shown that the description of the vehicle model in suitable coordinates is necessary. The observers are applied to measurement data of a passenger car. The results show very good estimation results in convergence and tracking. The approach enables linear state estimation for a nonlinear vehicle model with a camera based ground sensor.

A flexible multibody system model for a large-scale spider crane manipulator with five actuated joints is derived. The model is based on the Euler-Lagrange formalism and the assumption of the boom segments as Euler-Bernoulli beams. The swaying behavior of the manipulator model is verified for various robot poses that represent typical working configurations using precise absolute position measurements of the spider crane tool center point.

A decision tree algorithm for fault detection and isolation of additive sensor faults in an inertial measurement unit (IMU) is presented. The algorithm is trained and validated using real measurements with an extended Kalman filter (EKF) implemented on hardware in a production vehicle. The measurement data is cleaned from faulty measurements using different clustering algorithms , i.e., K-means, hierarchical clustering and density based clustering for applications with noise (DBSCAN). Then, decision tree models are trained based on the output estimates of an EKF using the original IMU data. Two different classification problems are considered. First, the binary classification problem distinguishing between the cases nominal and faulty is considered. Second, the multi-class classification problem distinguishing between errors in all three acceleration and angular velocity signals is considered.

In this paper, a graph-based planning algorithm is presented to extend the control concept from [1] to enable automatic positioning tasks with industrial tower cranes including prismatic obstacles in the workspace. The newly developed algorithm makes use of efficient calculation of s-curve trajectories. To avoid collisions with static obstacles, a heuristic approach is chosen to generate additional via-points, besides the start and target position. The transition times of the trajectories are used as weightings of the edges in the resulting directed graph. Using the transition times as weightings allows to determine the best trajectory sequence with state of the art graph-search algorithms. The algorithm is explained in detail and simulation results are shown.

The main disadvantage of vision-based control systems is the inherent time delay due to the processing of the image information, which leads to significant performance losses of the control system. We present a cascaded Kalman Filter structure to compensate for the time delay introduced by the 6d pose estimation in vision based control systems. In addition to delay compensation the structure allows all sensor data to be used asynchronously for state correction. The first Kalman Filter runs in the past and compensates for the time delay of the pose estimation. The second Kalman Filter is used exclusively for sensor data fusion. A special structure within the filter allows all sensor data to be used for state correction regardless of their sampling rate. The sampling rate, at which the second Kalman filter provides the state vector for a controller, can be set independently of all other variables.The entire structure has been tested by simulation and in experiments on a three degree of freedom robot. By applying the cascaded Kalman Filter structure, the position as well as angular errors in orientation can be reduced by more than 50%.

Tool-center-point (TCP) control increases the operating comfort of concrete pumps by shifting the control to the task space. The operation is accompanied by various requirements and constraints from the mechanical structure, the hydraulic system, and the operator. They must be taken into account for the efficient and error-free operation of the concrete pump. In this publication, a TCP control algorithm based on constrained quadratic optimization (cQP) is presented, which addresses the requirements and constraints of a mobile concrete pump. The features include boom deformation compensation, multiple operation modes, configuration control, compliant constraints on the joint movement, and hydraulic flow rate constraints. The methods are verified by simulation for a flexible link six degrees of freedom concrete pump. The results include deformation-compensated 2-D/3-D movements of the TCP with different operation modes and configuration control under joint and hydraulic constraints.

Driving cycles play an important role in analysis, design, and optimization of vehicles. The use of smartphones as data acquisition devices leads to easier access to a large test group, but results might be less accurate than with professional test devices. In this work, a systematic comparison of smartphone-based data acquisition and data recorded by a professional device is performed regarding the simplicity of data acquisition and the accuracy of energy estimation. Challenges and opportunities of smartphone-based driving cycle synthesis are described. A frequency analysis presents differences in sampling rates and energy characteristics. It is shown that a sampling rate of 1Hz is sufficient to cover relevant dynamics for low-power electric two-wheelers (E2Ws). The driving behavior of E2Ws in Shanghai is used as a case study and analyzed by means of a data collection campaign. Based on a localized E2W driving cycle in Shanghai, a data collection strategy for improving driving cycles is proposed. In particular, the inclusion of data collection by smartphone and an offline data fusion with IMU data to improve energy predictions are presented. The derived method can help manufacturers to estimate energy consumption and improve the design process of localized driving cycles.

In this paper an overall, simplified modeling approach describing the dependency between applied brake pressure and brake torque is presented. Therefore the internal amplification of a disc brake, the so-called brake-factor, is modeled in dependency of the vehicle velocity, the brake pressure and the brake temperature. As the application of this model requires the brake temperature, which is commonly not measured, a new lumped-mass-based brake temperature model, that describes the thermal behavior of a typical disc brake is stated and validated.

Air conditioning systems are available in all modern production cars, regardless of whether they have an internal combustion engine or an electric motor. To ensure thermal comfort of the passengers, safe and energy efficient operation, as well as an easy adaptability to different vehicle architectures, advanced control strategies have to be implemented. The basis for a controller design are dynamical models of the air conditioning system. For this purpose, in this paper an extended standard refrigerant circuit with additional components connected in parallel and serial is considered. The single components of the circuit are modeled, identified and validated in this paper. Additionally, a simulation model for the low pressure part of the refrigerant circuit is presented.

This paper describes a new sensor fault detection approach for a vehicle dynamics controller. The detection problem is divided into two parts. First, a model-based observer is used to incorporate the knowledge of the system into the fault detection. Next, a data driven classification algorithm based on kalman filter performance metrics is used. This machine learning algorithm is trained using real vehicle data and, therefore, able to handle model uncertainties and disturbances inherently. Due to the usage of a nonlinear observer, the fault detection is suitable up to the limits of handling. The presented structure offers the possibility to use the same classification algorithm for different vehicles as the vehicles' behavior is abstracted in the observer. Therefore, the need of extensive training data is reduced. This paper focuses on the development of features and gives a first proof of concept. The developed fault detection is validated with real car measurements.

This paper presents a novel reference trajectory generation for an oversteering vehicle behavior. This serves as an intuitive and safe driver interface in order to empower the inexperienced driver to achieve an enjoyable driving experience while preserving driving safety. The reference generation scheme consists of a finite-state machine and incorporates parameters to constrain the maximum sideslip angle and build-up rate. Normal driving is modeled using a linear single-track model. The transition to the oversteering mode is initiated when the driver is steering more than a model-based calculated threshold. A simulation study and a comparison to measurement data on a low-friction surface show that the proposed scheme matches a real vehicle's behavior.

Pneumatic cylinder drives are widely used in automation technology-mainly for two reasons: They are cheap to acquire and easy to handle. At the same time, it is well known that there is a significant potential of energy savings when changing the control pattern from a standard scheme toward a task-specific scheme. In order to maintain a reliable operation and low wear of pneumatic cylinders, it is essential not to exceed the manufacturer's specifications on kinetic impact energy at stroke end. Lowering the kinetic impact energy is typically done either by external shock absorbers (increasing the acquisition cost and requiring installation space) or internal solutions, i.e., pneumatic cushioning systems. In this article, we give a comprehensively identified and validated model extension to common modeling approaches for pneumatic cylinder drives regarding pneumatic end cushioning systems. Based on this model, we analyze, optimize, and evaluate the potential and applicability of energy-saving control strategies for pneumatic cylinder drives in the context of internal pneumatic cushioning systems. The results illustrate the large savings potential and point out which strategy is best to use for a specific application. Note to Practitioners-This article addresses the topic of energy efficiency of pneumatic drives, which are widely used in automation technology due to their low cost and high reliability. It is well known that pneumatic drives are often oversized and cause waste of energy. By changing the valve hardware and control pattern, energy savings of more than half of the compressed air consumption can be achieved as demonstrated in many scientific works. One main barrier to using such energy-saving strategies in practice is the concern about losing robustness. In this article, we analyze the influence of energy-saving strategies on impact velocity and the functionality of internal cushioning systems. By this, the robustness issue is addressed from two sides: the kinetic energy that needs to be absorbed at stroke end, and the kinetic energy that can be absorbed by internal damping systems. From a practical point of view, this contribution motivates and helps one to decide on the choice of energy-saving measures for pneumatic drives.

While battery electric vehicles (EVs) are on the advance, the broad customer basis is still concerned about battery electric range, a phenomenon commonly known as range anxiety. To tackle these concerns, the functionality of in-vehicle navigational systems must adapt to the new propulsion technology with a limited battery capacity. Central aim is to consider charging infrastructure in route planning. Furthermore, detailed powertrain models are required to accurately forecast an EV's energy consumption. On the other hand, such detailed models are hardly applicable to large scale road networks that are usually handled by routing services for vehicle navigation. This study proposes a two-staged approach to compute time optimal routes for EVs. To this end, a reduced road network is obtained from a leading routing service. Subsequently, a detailed consumption model is applied and the resulting multiobjective shortest path problem is solved using an adapted Moore-Bellman-Ford algorithm. Within an experimental study, the consumption forecast is validated against measurement data and query times of the proposed methodology are assessed for generic routing problems. The former shows significant improvement of consumption forecast accuracy compared to state-of-the-art models while the latter indicates potential for application in car manufacturers vehicle backend services.

Energy efficiency and thermal comfort are the most important objectives for building operation. While energy efficiency is at least partly implemented in operation concepts, individual thermal comfort is commonly not taken into account for commercial buildings due to missing sensors and actuators or suitable control approaches. A first step towards individualized comfort control needs a detailed room model to capture the various conditions within a large occupied space. In this paper a Gaussian process regression model is developed for the prediction of the stationary temperature profile in an exemplary open-plan office. Since sensor count and positions have a significant impact on the achievable prediction accuracy, optimal placement strategies are evaluated based on computational fluid dynamics simulations. Two objective functions - maximizing Mutual Information and minimizing the prediction error - are considered and compared. The differences between the approaches and their effect on the model quality are analyzed and discussed regarding their feasibility for real-world applications. Fewer requirements on the initial system knowledge suggest the usage of Mutual Information as an objective for optimizing the sensor setup, although the simpler application and higher robustness are accompanied by a slightly increased prediction error.

Electromechanical drives are widely used in industrial applications in order to fulfill given motion tasks. In this contribution, we consider the generation of energy-optimal trajectories for those. The corresponding optimal control problem is solved analytically up to a remaining root-finding problem using Pontryagin's Maximum Principle. The results are validated with experiments on a toothed belt axis driven by a servo motor. Incorporating the derivations in the design optimization of electromechanical drives enables to simultaneously optimize the design parameters and the dynamic behavior at only low computational effort.

This contribution focuses on designing a disturbance feedforward control for optically addressed deformable mirrors (OADMs). These mirrors can be used as intra-cavity actuators in high-power lasers to compensate for wavefront perturbations and for beam shaping within the framework of adaptive optics. For achieving the required deformations with high precision, the thermoelastic transfer behavior of the control intensity as well as disturbances due to the primary intensity have to be taken into account. The proposed control approach aims at minimizing the control effort in a spatial sense. Moreover, input constraints are considered explicitly, as the control intensity acts as a volumetric heat source which cannot cool the mirror. Furthermore, the 3D heat transfer in the cylindrical mirror is captured by a modal decomposition in the plate-direction in conjunction with a closed-form solution in the normal direction. By applying a similar approach to the mechanical equations, we are in the position to compute the equilibrium solution of the overall system in an efficient way. Based on the optimal solution, we subsequently derive an approximate solution by virtue of heuristics towards an online-capable algorithm. Finally, the performance is illustrated by means of simulations and a benchmark optimization.

By using opto-acoustic indoor localization systems based on ultrasound and infrared, the pose (position and orientation) of multiple objects can be tracked simultaneously when making use of the code-division multiple access (CDMA) technique. This allows the tracking of tools or hand movements in complex manual handling processes in an industrial environment in order to ensure the completeness of the assembly or the pick and place task. This contribution presents such a system with mobile transmitters including four ultrasound piezos and three to five room-fixed receivers. Hence, the 3D position can be determined by unilateral time-of-flight distance measurements between transmitters and receivers. Moreover, the system provides an orientation estimation by using the position signals of multiple transmitters attached to one object. Measurement results for five different poses show an object's center point position error below 2.2 cm across all poses and an orientation error below 17.1° when using five receivers. Those results are obtained using off-the-shelf low-cost narrowband piezoelectric ultrasound transducers without establishing any special laboratory conditions.

Opto-acoustic indoor localization systems based on ultrasound and infrared allow the tracking of tools and hand movements in complex manual industrial assembly and handling processes, e. g. the application of glue or adhesive sealing material. However, this requires a localization system that is able to track the position of multiple objects simultaneously during movement when the system is affected by Doppler effects and noise. The amplitude shift keying (ASK) modulation method within the code division multiple access (CDMA) environment using Gold codes is focused in previous publications for unilateral distance measurement of non-moving objects. The novelty of this contribution is the implementation of the binary phase shift keying (BPSK) modulation method and the consideration of moving objects with a Doppler effect mitigation algorithm. This algorithm can be performed in real time with an acquisition and a tracking step. Moreover, BPSK provides a permanently available phase information that can be used for exact positioning of multiple moving transmitters and ensures more robustness to modeling errors and noise. In this publication unilateral distance measurement results for up to four moving objects simultaneously with various speeds up to 0.54 m/s are presented showing the superiority of BPSK-CDMA with Doppler effect mitigation (mean errors below 6.9 mm) compared to ASK-CDMA and BPSK-CDMA without Doppler effect consideration.

This contribution refers to an opto-acoustic indoor localization system for the tracking of objects and hand movements in manual industrial assembly and handling processes as well as manual pick and place processes in logistics. The system is based on unilateral time-of-flight distance measurement between multiple transmitters attached to rigid bodies and fixed receivers. Previous publications have focused on the amplitude shift keying (ASK) modulation method within the code division multiple access (CDMA) environment using Gold codes for unilateral distance measurement. The novelty of this contribution lies in the implementation of the binary phase shift keying (BPSK) modulation method which provides a permanently available phase information that can be used for exact positioning of multiple moving transmitters. In addition, it ensures more robustness to modeling errors and noise. However, more complexity on the receivers' side is required. In this publication unilateral distance measurement results for various distances between 0.5 m and 3 m in an office environment are presented showing the superiority of BPSK-CDMA compared to ASK-CDMA when multiple transmitters are active. Furthermore, the better distance measurement performance of the cross-correlation algorithm compared to the phase correlation algorithm for ultrasound signals is outlined in measurement all distances, a maximum positioning error of 4.2 cm is achieved with BPSK-CDMA.

In this contribution, an opto-acoustic system for distance measurement between a transmitter and a receiver is presented. This distance measurement is part of an indoor localization system which uses multilateration to obtain the position and orientation of rigid bodies. The novelty of the presented distance measurement is the efficiency in the sense that high accuracy is obtained with the use of standard components, making the overall localization system affordable and facilitating its prevalence. This efficiency is achieved by the simultaneous use of spread spectrum time-of-flight (TOF) on the one hand, and carrier phase measurements on the other hand. Specifically, we use a two-channel link which is made up of an ultrasound and an infrared channel, the latter using the same carrier frequency as the ultrasound channel. In order to render the system extendible for multiple transmitters and receivers, which is necessary for its use in the multilateration setup, code-division-multiple-access amplitude modulation using Gold codes is employed, as all ultrasound and infrared signals share the same carrier frequency. This is also advantageous for multipath scattering suppression. The phase difference between the ultrasound and infrared carrier signals offers a highly accurate but ambiguous distance signal. On the other hand, correlation of the received code-modulated signals establishes a TOF measurement yielding a coarse but largely unique distance signal in the form of a probability density function. The combination of those signals results in a highly accurate and unique position estimation. Experimental results obtained on our test rig are presented to show the performance of the unilateral distance measurement.

During the virtual development and experimental testing of advanced construction machinery, automation approaches for automated task execution can prove very valuable. In this paper, modeling and automation approaches for a hydraulic mini excavator are developed. In particular, a physical model for detailed system analysis and a simplified Hammerstein model for controller tuning are developed and validated with measurement data from the mini excavator. For attitude estimation of the excavator, inertial measurement units and extended Kalman filters are used in a sensor fusion framework. The control concept for automation is based on a virtual driver consisting of a state machine for task coordination as well as offset-free model predictive controllers (MPCs) for decentralized and robust tracking control of all motion axes. The constrained MPC optimization problems are solved in real time by means of the accelerated proximal gradient method. Experimental results from the mini excavator prove the developed control approach to be valuable for virtual development and automated testing during the commissioning of hydraulic machinery.

Macrophages are cells with remarkable plasticity. They integrate signals from their microenvironment leading to context-dependent polarization into classically (M1) or alternatively (M2) activated macrophages, representing two extremes of a broad spectrum of divergent phenotypes. Thereby, macrophages deliver protective and pro-regenerative signals towards injured tissue but, depending on the eliciting damage, may also be responsible for the generation and aggravation of tissue injury. Although incompletely understood, there is emerging evidence that macrophage polarization is critical for these antagonistic roles. To identify activation-specific expression patterns of chemokines and cytokines that may confer these distinct effects a systems biology approach was applied. A comprehensive literature-based Boolean model was developed to describe the M1 (LPS-activated) and M2 (IL-4/13-activated) polarization types. The model was validated using high-throughput transcript expression data from murine bone marrow derived macrophages. By dynamic modeling of gene expression, the chronology of pathway activation and autocrine signaling was estimated. Our results provide a deepened understanding of the physiological balance leading to M1/M2 activation, indicating the relevance of co-regulatory signals at the level of Akt1 or Akt2 that may be important for directing macrophage polarization.

In bioindustrial large scale fermenters microorganisms are exposed to conditions of unsteady nutrient supply which occur only rarely on small lab-scale fermenters and lead to economic losses. In aerobic processes cells face different availabilities of oxygen, nitrogen and carbon sources along various directions inside a fermenter. The adaptation of the central metabolism in the facultative anaerobic bacterium Escherichia coli to changing oxygen concentrations will be investigated. Flux balance analysis (FBA) is an often used method to calculate reaction fluxes under given environmental conditions. FBA is based on a stoichiometric model with possible reaction fluxes which are limited by constraints. One sort of constraints are the lower and upper flux bounds. Existing methods of FBA describe metabolic adaptations to changing environments not in sufficient detail. This work develops a variant of FBA in order to close this gap. Balance equations for important gene transcripts and gene products are formulated and flux bounds are calculated continuously. The described variant of FBA is applied to a model of E. coli central metabolism. A transition between anaerobic and aerobic environment is simulated. The results are compared with a conventional FBA approach and regulatory FBA (rFBA). The FBA method described in this study shows possible targets for experimental validation.

Fast and exact motions of continuum robots are hardly seen so far. Partly this is caused by physical constraints, e.g. small available actuation forces. Another reason is the dynamic coupling between the actuators that cannot be neglected during fast motions. Therefore, a model-based MIMO controller in actuator space was developed, that is based on a spatial dynamic model with one mass point per section. Using feedback linearization, the actuators can be decoupled and feedforward control in combination with linear controllers can be applied. Measurements of an example manipulator show the good tracking result of pure feedforward action with feedback linearization. Adding a linear PD-controller increases the robustness against disturbances without reducing the possibility of fast motions.

In the previous decade, multiple useful approaches for kinematic models of continuum manipulators were successfully developed. However, dynamic modeling approaches needed for fast simulations and the development of model-based controller design are not powerful enough yet-especially for spatial manipulators with multiple sections. Therefore, a practicable lumped mass model similar to common dynamic models of rigid-link manipulators is needed, which can be used for simulations and model-based control design. The model incorporates mechanical interconnections of parallel and serially connected bellows and uses constant curvature kinematics and its analytical derivatives to balance forces and energies in a global reference frame. The parameters of the resulting model are identified with measurements before the simulation results are experimentally validated. The obtained dynamic model can be used to both simulate the manipulator dynamics and calculate the inverse dynamics needed for model-based controller design or path planning.

The article deals with the design of a feedforward controller for a transport system with spatially distributed control input considering input constraints. The feedforward controller is used to compensate transport delays in the input-/output (I/O) behavior of the transport system by realizing exact trajectory tracking of the nominal output along desired trajectories. The proposed method is based on the inversion of the linear I/O-model of the considered transport system. The input constraints are considered by optimizing parameters of the planned output trajectories. The presented method is compared to an early lumping approach discretizing the model equations and solving an optimization problem of the discretized system. Finally simulation results are presented and discussed.

Hydraulic excavators play an important role for various construction tasks. But due to increasing costs of fossil fuels and an increasing environmental awareness, there is high demand for more efficient machinery. During the development of the necessary technologies, simulations based on an excavator model and measured driving cycles allow for an early assessment of the expected machine performance. However, the outcome of such simulations is not only influenced by the cycle and the machine model, but also by the chosen driver model. In this work, a predictive driver model for the virtual excavator is presented. Taking typical real-world driver behavior into account, the developed driver model is based on an event-based task coordinator and a model predictive controller for each axis. Simulation results indicate that the developed driver model captures the actual driver behavior very well.

The automation and motion control aspects of a developed multi-sensor measuring system are described in this contribution. The measuring system is designed to automatically detect and survey surface defects of microscopic size on macroscopic mechanical components. It is based on the hierarchical combination of optical sensors with different working ranges and resolutions. This measurement approach requires a high level of automation to perform the inspection and measurement tasks. From a control engineering point of view, the path planning and trajectory generation as well as the high-accuracy motion control and friction compensation system are particularly challenging and compose the focus of this contribution.

A highly accurate 6 DOF real-time navigation system for minimally-invasive surgical interventions is presented. The system consists of short range acoustic transducers that combine time-of-flight and carrier-phase measurements and a standard inertial measurement unit. With Hybrid Extended Kalman Filtering, a precise pose estimation of the surgical instrument is achieved. In this contribution, simulation results focussing on different noise variances, which are experimentally obtained from a prototype setup, and initial errors are presented. In addition to previous work, the main advance lies in the estimate of the orientation of the laparoscopic instrument through the use of multiple acoustic transducers.

Dynamic models of continuum manipulators tend to become very complex, especially for spatial manipulators with multiple sections. Therefore a practicable model is needed that can be used for simulations and model-based control design. Neglecting rotational energies and assuming a continuum manipulator that consists of a single concentrated mass per section, dynamic equations for each actuator state are derived using the Euler-Lagrange formalism. Forces, positions and velocities are transformed to a global reference system using the homogeneous transformation based on constant curvature robot kinematics and its derivatives. Measurements of an example manipulator verify the resulting dynamic model that can be used to both simulate the dynamics and calculate the inverted robot dynamics needed for model-based controller design.

In this contribution a novel human-assistive robot for laparoscopic surgery is presented. The purpose of the proposed system is to improve the ergonomics of laparoscopic surgery by reducing the physical load on the surgeon. Selected examples of existing robotic systems for medical and manufacturing applications are compared to the suggested system. After a brief description of the system's main features, a velocity-based admittance controller for regulating the interaction force is introduced. The desired interaction force is the sum of the required supporting force and a force-feedback, signalizing workspace constraints. The supporting force varies in magnitude and direction depending on the surgeon's posture and allows for individual adjustments to the needs of the surgeon on duty.

A new concept for surgical navigation in minimally-invasive interventions is presented, which allows an enhanced orientation of the surgeon with reduced cost and system complexity. The navigation system takes advantage of recorded image data, including the preoperatively computed tomography (CT) and magnetic resonance tomography (MRT) data, which are registered and segmented in order to obtain relevant biological markers, as well as the intraoperative camera data. A device for absolute positioning, included in the laparoscope and consisting of an acoustic indoor localization with a supporting inertial measurement unit (IMU), fuses these different navigation data for reliable 6 degree-of-freedom (DOF) position and orientation estimation. With those navigation information of the laparoscope, the landmarks of the preoperative data are localized on the intraoperative camera images via augmented reality and thus the site of the relevant features (e. g., a carcinoma) is determined.

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.

The number of mathematical models for biological pathways is rapidly growing. In particular, Boolean modelling proved to be suited to describe large cellular signalling networks. Systems biology is at the threshold to holistic understanding of comprehensive networks. In order to reach this goal, connection and integration of existing models of parts of cellular networks into more comprehensive network models is necessary. We discuss model combination approaches for Boolean models. Boolean modelling is qualitative rather than quantitative and does not require detailed kinetic information. We show that these models are useful precursors for large-scale quantitative models and that they are comparatively easy to combine. We propose modelling standards for Boolean models as a prerequisite for smooth model integration. Using these standards, we demonstrate the coupling of two logical models on two different examples concerning cellular interactions in the liver. In the first example, we show the integration of two Boolean models of two cell types in order to describe their interaction. In the second example, we demonstrate the combination of two models describing different parts of the network of a single cell type. Combination of partial models into comprehensive network models will take systems biology to the next level of understanding. The combination of logical models facilitated by modelling standards is a valuable example for the next step towards this goal.

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 large-scale 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-alpha, UV-B irradiation, interleukin-1beta 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.

This paper handles the control of flexible link robot systems. Since in the considered case a manipulator for grabbing containers is suspended on two ropes, torsional oscillation occurs changing the position of the manipulator. A control strategy, which consists of feedforward and disturbance observer based feedback control implemented on boom cranes to assure an accurate tracking of the manipulator along a reference path is shown. The system parameters like rope length, moment of inertia of the load and its mass are changed frequently during the crane operation. Therefore the controller is fully adaptive due to the varying system parameters. Measurement results show the efficiency of the presented control strategy implemented on a harbor mobile crane LIEBHERR LHM 400.

Because of their high power/weight ratio pneumatic actuators, especially the so called pneumatic muscles, are very interesting for the use as actuators in robotics. But, in fact the physical model is highly nonlinear, in the following a flatness based position controller for the pneumatic artificial muscles is presented. The considered pneumatic muscle is produced by the manufacturer Festo and possesses a high pulling force to 4000N and a very long lifetime at least to 10 million switching cycles. The control objective is to track the payload along a specified reference path including an active attenuation. Since a model based control approach is pursued, a physical model is presented for an experimental setup. The model is very nonlinear making nonlinear flatness based control desirable. Experimental results are included and demonstrate the efficiency of the control.