Modern road vehicles are employing features of driver assistance systems (DAS) to improve drivability performance, comfort, and safety. In the future perspective, the advances in this field will lead these systems to the level of autonomous and cooperative driving, based on sensors networks and sensor fusion. This paper aims to present the readers a novel strategy for lane detection and tracking, which fits as a functional requirement to deploy DAS features like Lane Departure Warning and Lane Keeping Assist. To achieve the presented results, the digital image processing was divided into three levels. At the low-level, the input image dimensionality is reduced from three to one layer, the sharpness is improved, and region of interest is defined based on the minimum safe distance from the vehicle ahead. The feature extractor for lane edges detection design is part of the mid-level processing. The lane tracking strategy development is discussed in the high-level stage; Hough Transform and a shape-preserving spline interpolation are used to achieve a smooth lane fitting. The experimental outcomes were qualitatively and quantitatively evaluated using a ground truth comparison. The strategy shows good accuracy levels, including scenarios with shadows, curves, and road slope.

This paper presents the control strategies of nonlinear vehicle suspension using a magnetorheological (MR) damper. We used two different approaches for modeling and control of the mechanical and electrical parts of the suspension systems with the MR damper. First, we have formulated and resolved the control problem in order to design the linear feedback dumping force controller for a nonlinear suspension system. Then the values of the control dumping force functions were transformed into electrical control signals by the application of a fuzzy logic control method. The numerical simulations were provided in order to show the effectiveness of this method for the semi-active control of the quarter-car suspension.

Micro-electromechanical systems (MEMS) are micro scale devices that are able to convert electrical energy into mechanical energy or vice versa. In this paper, the mathematical model of an electronic circuit of a resonant MEMS mass sensor, with time-periodic parametric excitation, was analyzed and controlled by Chebyshev polynomial expansion of the Picard interaction and Lyapunov-Floquet transformation, and by Optimal Linear Feedback Control (OLFC). Both controls consider the union of feedback and feedforward controls. The feedback control obtained by Picard interaction and Lyapunov-Floquet transformation is the first strategy and the optimal control theory the second strategy. Numerical simulations show the efficiency of the two control methods, as well as the sensitivity of each control strategy to parametric errors. Without parametric errors, both control strategies were effective in maintaining the system in the desired orbit. On the other hand, in the presence of parametric errors, the OLFC technique was more robust.

An analysis of a new energy harvester model is presented, based on a simple portal frame structure, considered a nonideal system due to the kind of excitation influenced by the response of the system, such as a direct current motor with limited power supply. The horizontal motion of the portal frame is considered under a nonideal excitation, and the approximated mathematical model of the system is obtained, considering the coupled oscillators. To model the piezoelectric coupling, the nonlinearities of the piezoelectric material were considered. A constantly sustained energy harvesting is essential for using these devices in real applications; for this, a control strategy is required. Passive control was obtained by means of a nonlinear substructure with properties of nonlinear energy sink. Numerical simulations were performed in order to find best values of control parameters. To check the robustness of the control strategy, an analysis considering uncertainties in the parameters of the model was performed, showing the efficiency of the passive control (energy pumping) in the suppression of the chaotic behavior, as well as the sensitivity of the control system to parametric errors. Passive control leads the system to a stable periodic orbit, allowing a more efficient energy harvest, due to the higher peak-to-peak amplitude of oscillation mean value. The passive control strategy eliminates the need for an active microcontroller to stabilize the system in a periodic orbit, improving the energy budget (harvested versus expended). The results show the displacement of the structure and the maximum power harvested by the device with and without passive nonlinear energy sink. It can be concluded that the application of passive control was successful. The control was robust and improved the energy harvested through the suppression of the chaotic motion, leading the system to a periodic orbit with stable amplitude of vibration, without damaging the structure.