Driving simulators play an important role in the development of new vehicles and advanced driver assistance devices. In fact, on the one hand, having a human driver on a driving simulator allows automotive OEMs to bridge the gap between virtual prototyping and on-road testing during the vehicle development phase. On the other hand, novel driver assistance systems (such as advanced accident avoidance systems) can be safely tested by having the driver operating the vehicle in a virtual, highly realistic environment, while being exposed to hazardous situations. In both applications, it is crucial to faithfully reproduce in the simulator the drivers perception of forces acting on the vehicle and its acceleration. The strategy used to operate the simulator platform within its limited working space to provide the driver with the most realistic perception goes under the name of motion cueing. In this paper we describe a novel approach to motion cueing design that is based on Model Predictive Control techniques. Two features characterize the algorithm, namely, the use of a detailed model of the human vestibular system and a predictive strategy based on the availability of a virtual driver. Differently from classical schemes based on washout filters, such features allows a better implementation of tilt coordination and to handle more efficiently the platform limits
The use of dynamic driving simulators is constantly increasing in the automotive community, with applications ranging from vehicle development to rehab and driver training. The effectiveness of such devices is related to their capabilities of well reproducing the driving sensations, hence it is crucial that the motion control strategies gen- erate both realistic and feasible inputs to the platform. Such strate- gies are called motion cueing algorithms (MCAs). In recent years several MCAs based on model predictive control (MPC) techniques have been proposed. The main drawback associated with the use of MPC is its computational burden, that may limit their application to high performance dynamic simulators. In the paper, a fast, real-time implementation of an MPC-based MCA for 9 DOF, high performance platform is proposed. Effectiveness of the approach in managing the available working area is illustrated by presenting experimen- tal results from an implementation on a real device with a 200 Hz control frequency
Telescope resolution is theoretically limited by the diffraction effect, and hence it is inversely proportional to the lens diameter. However, the real resolution of images acquired by large ground telescopes is reduced by the atmospheric turbulence effect. For this reason, telescopes are provided with an adaptive optics (AO) system which aims at compensating the turbulence effect. In this paper we consider a control algorithm for the AO system based on a turbulence prediction method. We propose two linear models, both based on a principal component analysis (PCA) spatial representation, to fit the turbulence temporal dynamic and provide its temporal prediction. We assume that some information about the turbulence has already been estimated, and we exploit it in the computation of the model parameters. The first proposed model yields the best performance but at a quite high computational cost, whereas the second model is best suited in the case of high sampling rates. Furthermore, our simulations show that the PCA spatial representation is robust to errors in the parameter estimation
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