The use of composite conical shell structures across the globe in various applications concentrates on nose cones and incredibly complex vehicle components in the aerospace and automotive industries. This work investigates the free vibration analysis of laminated conical shell (LCS) and sandwich conical shell (SCS) structures with various foam cores. The sandwich construction is fabricated using the hand layup technique. The vibration characteristics of the fabricated LCS and SCS specimens are validated with the Finite element software (ANSYS). Very minimal variation was observed between the experimental and numerical simulations. The various parametric analyses were also studied for the LCS and SCS with length, ply orientation, and boundary conditions. Further, for SCS, vibration behaviors across multiple core materials and core thickness were also investigated.
Driving simulators have been used in the automotive industry for many years because of their ability to perform tests in a safe, reproducible and controlled immersive virtual environment. The improved performance of the simulator and its ability to recreate in-vehicle experience for the user is established through motion cueing algorithms (MCA). Such algorithms have constantly been developed with model predictive control (MPC) acting as the main control technique. Currently, available MPCbased methods either compute the optimal controller online or derive an explicit control law offline. These approaches limit the applicability of the MCA for real-time applications due to online computational costs and/or offline memory storage issues. This research presents a solution to deal with issues of offline and online solving through a hybrid approach. For this, an explicit MPC is used to generate a look-up table to provide an initial guess as a warm-start for the implicit MPC-based MCA. From the simulations, it is observed that the presented hybrid approach is able to reduce online computation load by shifting it offline using the explicit controller. Further, the algorithm demonstrates a good tracking performance with a significant reduction of computation time in a complex driving scenario using an emulator environment of a driving simulator.
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