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This paper presents a test rig developed for the experimental investigations on a vibration isolation system, based on the use of magnetorheological elastomers (MRE). These intelligent materials allow to adapt their stiffness to the needs of the isolation as the material stiffness depends on the intensity of the magnetic field in which it is immersed; it is possible to modify the material stiffness, quickly and reversibly, by means of coils. The paper describes an isolation system, based on MRE pads, and the test rig developed for the experimental tests. The rig allows to experimentally study the effectiveness of different control algorithms adopted to adjust the material stiffness, considering that the stiffness of the MRE pads can only be increased by activating the material.

In this paper, we suggest a novel Ecological Adaptive Cruise Control (Eco-ACC) system for an autonomous electric vehicle able to drive its motion while minimizing as much as possible its energy consumption. To this aim, we consider a Nonlinear Model Predictive Control (NMPC) method enhanced with an off-line Computational-intelligence (CI)-based optimization algorithm, i,e. the Improved-Grey Wolf Optimizer (I-GWO). Specifically, since the control performances strongly depend on the proper selection of the NMPC cost function, we propose the I-GWO algorithm to help the control designer find the sub-optimal weighting factors of the dynamic cost function optimized via the NMPC. An extensive numerical analysis involving realistic vehicle dynamics and a real-life Italian road network route confirm the effectiveness of the proposed approach in guaranteeing the ACC control objectives while ensuring energy saving.