The present research work has been carried out to develop a unified knowledge-based hybrid methodology combining regression-based, lumped parameter and finite element analyses that can be implemented in the initial phase of vehicle design resulting in a superior side crash performance. As a first step, a regression-based model (RBM) is developed between the injury parameter Thoracic Trauma Index (TTI) of the rear SID and characteristic side impact dynamic response variables such as rear door velocity (final) and intrusion supplementing an existing RBM for front TTI prediction. In order to derive the rear TTI RBM, existing public domain vehicle crash test data provided by NHTSA has been used. A computer-based tool with a Graphical User Interface (GUI) has been developed for obtaining possible solution sets of response variables satisfying the regression relations for both front and rear TTI. As a next step in the formulation of the present hybrid methodology for vehicle side impact safety design, a new Lumped Parameter Model (LPM) representing NHTSA side impact is developed. The LPM developed consists of body sub-systems like B-pillar, front door, rear door and rocker (i.e. sill) on the struck side of the vehicle, MDB, and “rest of the vehicle” as lumped masses along with representative nonlinear springs between them. It has been envisaged that for the initial conceptual design to progress, the targets of dynamic response variables obtained from RBM should yield a set of spring characteristics broadly defining the required vehicle side structure. However, this is an inverse problem of dynamics which would require an inordinate amount of time to be solved iteratively. Hence a knowledge-based approach is adopted here to link the two sets of variables i.e., the dynamic response parameters (such as average door and B-pillar velocities, door intrusion, etc.) and the stiffness and strength characteristics of the springs present in LPM. In effect, this mapping is accomplished with the help of an artificial neural network (ANN) algorithm (referred to as ANN_RBM_LPM in the current work). To generate the required knowledge database for ANN_RBM_LPM, one thousand cases of LPM chosen with the help of the Latin Hypercube technique are run with varying spring characteristics. The goal of finding the desired design solutions describing vehicle geometry in an efficient manner is accomplished with the help of a second ANN algorithm which links sets of dynamic spring characteristics with sets of sectional properties of doors, B-pillar and rocker (referred as ANN_LPM_FEM in the current work). The implementation of this approach requires creation of a knowledge database containing paired sets of spring characteristics and sectional details just mentioned. The effectiveness of the hybrid methodology comprising both ANN_RBM_LPM and ANN_LPM_FEM is finally illustrated by improving the side impact performance of a Honda Accord finite element model. Thus, the unique knowledge-based hybrid approach developed here can be deployed in real world vehicle safety design for both new and existing vehicles leading to enormous saving of time and costly design iterations.

Hat sections, single and double, made of steel are frequently encountered in automotive body structural components such as front rails, B-Pillar, and rockers of unitized-body cars. These thin-walled components can play a significant role in terms of crashworthiness and impact energy absorption, through a nonlinear phenomenon called as progressive dynamic buckling. As modern vehicle safety design relies heavily on computer-aided engineering, there is a great need for analysis-based predictions to yield close correlation with test results. Although hat sections subjected to axial loading have been studied widely in the past, there is scanty information in published literature on modeling procedures that can lead to robust prediction of test responses. In the current study, both single-hat and double-hat components made of mild steel are studied extensively experimentally and numerically to quantify statistical variations in test responses such as peak load, mean load and energy absorption, and formulate modeling conditions for capturing elasto-plastic material behavior, strain rate sensitivity, spot-welds, etc. that can lead to robust predictions of force-time and force-displacement histories as well as failure modes. In addition, keeping initial stages of vehicle design in mind, the effectiveness of soft computing techniques based on polynomial regression analysis, radial basis functions and artificial neural networks for quick assessment of the behaviors of steel hat sections has been demonstrated. The study is extended to double-hat sections subjected to eccentric impact loading which has not been previously reported. A lightweight enhancement of load carrying capacity of steel hat section components has been investigated with PU (polyurethane) foam-filled single and double hat sections, and subjecting the same to quasi-static and axial impact loading. Good predictions of load-displacement responses are again obtained and shortening of fold lengths vis-à-vis hollow sections is observed. Finally, the performance of hat sections made of glass fiber-reinforced composites is studied as a potential lightweight substitute to steel hat section components. The challenging task of numerical prediction of the behaviors of the composite hat sections has been undertaken using a consistent modeling and analysis procedure described earlier and by choosing an appropriate constitutive behavior available in the popular explicit contact-impact analysis solver, LS-DYNA.

Existing reinforced concrete structures feature, as a rule, a relatively low resistance to various sorts of impact loading, such as shock, collision, or explosion. To this aim, the primary goal of the Research Training Group (in German: Graduiertenkolleg, GRK) 2250, funded by the Deutsche Forschungsgemeinschaft (DFG), is to bring substantial improvements in the impact resistance of existing buildings by applying thin layers of strengthening material. By using innovative mineralbonded composites, public safety and reliability of vitally important existing structures and infrastructure should be significantly enhanced. The scientific basis to be developed will additionally enable to build new, impact-resistant structures economically and ecologically. The framework of the GRK 2250 as well as some achievements are herein briefly presented.

Robot arms require actuators that are powerful, precise and safe. The safety concern is amplified when these robots work closely with people in collaborative applications. This thesis investigates the design and implementation of hybrid pneumatic-electric actuators (HPEA) for use in robot arms, particularly those intended for collaborative applications. The initial focus was on improving the control of an existing single HPEA-driven rotary joint. The torque is produced by four pneumatic cylinders connected in parallel with a small DC motor. The DC motor is directly connected to the output shaft. A cascaded control system is designed that consists of an outer position control loop and an inner pressure control loop. The pressure controller is based on a novel inverse valve model. High precision position tracking control is achieved due to the combination of the model-based pressure controller, model-based position controller, adaptive friction compensator and offline payload estimator. Experiments are performed with the actuator prototype rotating a link and payload with a rotational inertia equivalent to a linear actuator moving a 573 kg mass. Averaged over five tests, a root-mean-square error of 0.024° and a steady-state error (SSE) of 0.0045° are achieved for a fast multi-cycloidal trajectory. This SSE is almost ten times smaller than the best value reported for previous HPEAs. An offline payload estimation algorithm is used to improve the control system’s robustness. The superior safety of the HPEA is shown by modeling and simulating a constrained robot-head impact, and comparing the result with equivalent electric and pneumatic actuators. This research produced two journal papers. Since HPEAs are redundant actuators that combine the large force, low bandwidth characteristics of pneumatic actuators with the large bandwidth, small force characteristics of electric actuators, the effect of using optimization-based input allocation for HPEAs was studied. The goal was to improve the HPEA’s performance by distributing the required input (force or torque) between the redundant actuators in accordance with each actuator’s advantages and limitations. Three novel model-predictive control (MPC) approaches are designed to solve the position tracking and input allocation problems using convex optimization. The approaches are simulated on a HPEA-driven system and compared to a conventional linear controller without active input allocation. The first MPC approach uses a model that includes the dynamics of the payload and pneumatics; and performs the motion control using a single loop. The latter methods simplify the MPC law by separating the position and pressure controllers. Although the linear controller is the most computationally efficient, it is inferior to the MPC-based controllers in position tracking and force allocation performance. The third MPC-based controller design demonstrated the best position tracking with root mean square errors of 46%, 20%, and 55% smaller than the other three approaches. It also demonstrated sufficient speed for real-time operation. This research produced one journal paper. The research continued with the design and implementation of a two degree-of-freedom HPEA-driven arm. A HPEA-driven “elbow” joint is designed and added to the existing “shoulder” joint. The force from a single pneumatic cylinder is converted into torque using a 4-bar linkage. To eliminate backlash and keep the weight of the arm low, a 2nd smaller DC motor is directly connected to the joint. The kinematic and kinetic models of the new arm, as well as the geometry of the new elbow joint are studied. The resulting joint design is implemented, tested and controlled. This joint could achieve a SSE of 0.0045° in spite of its nonlinear joint geometry. The arm is experimentally tested for simultaneous tracking control of the two joints, and for end-effector position tracking in Cartesian space. The end-effector is able to follow a circular trajectory in pneumatic mode with position errors below 0.005 m. / Thesis / Doctor of Philosophy (PhD) / Robots that work with, or near, humans require greater safety considerations than other robots. A significant concern is collisions between the robot and humans that may happen when sensors or software fails. An actuator for robots that combines the inherent safety of pneumatic actuators with the accuracy of electric actuators, termed a “hybrid pneumatic electric actuator” (HPEA), is investigated. The design, instrumentation, modelling, and control of HPEAs are studied theoretically and experimentally. The proposed actuator could achieve high position control accuracy in a variety of experiments, with steady state error of less than 0.0045 degrees. Simulated impacts with a human head also showed that a HPEA-driven robot arm can achieve a 52% lower impact force, compared to an arm driven by conventional electric actuators. The HPEA design and control experiments are performed on a single HPEA-driven joint and extended to an arm consisting of two HPEA-driven revolute joints.