In this Thesis a new Optimal control-based algorithm is presented, FLOP is part of a new class of algorithms the group of Mechatronic and Vehicle Dynamic Lab of Sapienza is developing under the name of Variational Feedback Controllers (VFC). The proposed method starts from classical optimal variational principles, usually part of the Pontryagin’s or Bellman’s methods, but it provides the user with the possibility to implement a feedback control, even in the presence of nonlinearities. In fact, even though Pontryagin approach provide the best solution for the considered system, it has an engineering weakness, since the identified solution is a feedforward control law. The control program form of the solution presents an engineering weakness, that is they use only one single information on the system state: the initial condition. This approach would be natural if the system’s model is not affected by any error, the state of the system is perfectly known, and all the environment forces are known in advance. Under these conditions, the system response for any future time depends only on the initial information provided by the initial condition. However, engineering practice and real world meet a different scenario. Models of the controlled process have some degree of approximation, because the real dynamics is only roughly represented by the estimated differential equations, and the environment external disturbance is generally unknown. In this context, use of measurements by sensors is of great value and feedback control strategies use the valuable support of measurements. Variation Feedback Control is aimed at using the power of variational functional calculus to state a well posed optimality principle, but using the information coming from sensors, integrating in this way the available information contained into initial conditions, the only one used in the context of control programs, providing a more reliable controlled system. This chance is obtained by changing the optimality principle used in the classical ...