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In this paper we propose a complete strategy for the longitudinal control of automotive vehicles in Stop-and-Go situations. Firstly, a upper level grey-box torque control is proposed to compensate for neglected dynamics at chassis level (due for example to road slopes, aerodynamic forces, rolling resistance forces, etc.). Secondly, to obtain the desired torque, we have considered a model-free approach to elaborate the suitable low level engine or braking torque. Convincing simulation results are presented to validate our method.

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This paper describes a robust stop-and-go control strategy for vehicles. Since sensors used in a real automotive context are generally low cost, measurements are quite noisy. Furthermore, many vehicle/road interaction factors (road slope, rolling resistance, aerodynamic forces) are very poorly known. Hence, a robust strategy to noise and parameters is proposed within the same theoretical framework: algebraic nonlinear estimation and control techniques. On the one hand, noisy signals will be processed in order to obtain accurate derivatives, and thereafter, variable estimates. On the other hand, a grey-box closedloop control will be implemented to reject all kind of disturbances caused by exogenous parameter uncertainties.

Stem cell therapy had a favourable effect on the restoration of LV synchronous contraction in patients with AMI.

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