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Design of real time active controls for structural dynamics problems requires a very precise mathematical model, to closely determine the system dynamic behavior, under virtual simulation. The finite element models can somehow be used as a mathematical model but due to complex shape/structure of the component, the size of discrete models resulting from finite element analysis is usually very large, causing the virtual simulation to be extremely computationally intensive and time consuming, also the boundary conditions applied are not very scalable, making the system deviate from its real dynamic behavior. Thus, this paper deals with the design of a Model Order Reduction technique, using orthogonal decomposition of system matrices, which can be used for creating accurate low-order dynamic model with scalable boundary conditions.

The design and development of complete vehicle, understanding of chassis system development process is an important task. Chassis frame of a vehicle is supporting member, both structurally and functionally, to all other chassis aggregate systems viz. suspension, steering, braking system etc. In this paper, a methodology for chassis frame model construction and validation is explained. In present work, chassis frame model is validated in terms of modal parameters and also against static loading conditions. Existing chassis 3D Computer Aided Design (CAD) data was generated using scanning and cloud point data conversion technique. FE model was generated and validated through experimental measurements viz. modal testing, vertical bending, lateral bending, and torsional bending test. Loading and boundary conditions were replicated on the complete FE model in CAE domain and test validation was carried out using appropriate mesh biasing and weld modeling techniques.

Active control mainly comprises of three parts; sensor-detects the input disturbance, actuator -provide counter measures and control logic -processing of input disturbances and converting it into logical output. Lot of methods for active vibration control are available but this paper deals with active control of steering wheel vibrations of an LCV. A steering wheel is, one such component that directly transfers vibration to the driver. Active technique described here is implemented using accelerometer sensor, IMA (Inertial Mass Actuator) and feed forward Fx-LMS (Filtered reference Least Mean Square) control algorithm. IMA is a single-degree-of-freedom oscillator. To enable a control, IMA needs to be coupled to the structure at a single point, acting as an add-on to the passive system. Fx-LMS is a type of adaptive algorithm which is computationally simple and it also includes compensation for secondary path effects by using an estimate of the secondary path.

Autonomous Emergency Braking (AEB) systems play a critical role in ensuring vehicle safety by detecting potential rear-end collisions and automatically applying brakes to mitigate or prevent accidents. This paper focuses on establishing a framework for the Verification & Validation (V&V) of Advanced Driver Assistance Systems (ADAS) by testing & verifying the functionality of a RADAR-based AEB ECU. A comprehensive V&V approach was adopted, incorporating both virtual and physical testing. For virtual testing, closed-loop Hardware-in-Loop (HIL) simulation technique was employed. The AEB ECU was interfaced with the real-time hardware via CAN. Data for the relevant target such as the target position, velocity etc. was calculated using an ideal RADAR sensor model running on the real-time hardware. The methodology involved conducting a series of test scenarios, including various driving speeds, obstacle types, and braking distances.