Advanced Driver Assistance Systems (ADAS) is a group of technologies that support the driving with safety measures, such as the Adaptive Cruise Control (ACC). The ACC System computes a reference, known as cruise speed, that tracks the desired speed but it changes if a leading vehicle draws close. In this paper, the inner loop of ACC was designed using two different Model Predictive Control techniques: finite horizon and infinite horizon of prediction. The dynamic model of the vehicle was obtained using System Identification. The controllers were embedded in an ACC Module that communicates directly with a customized Electronic Control Unit (ECU) of the vehicle. The validation of the controllers is performed with practical experiments using a dynamometer. INDEX TERMSAutomotive applications, cruise control, predictive control, quadratic programming, system identification.

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Recently, a new state-space representation has been developed for output feedback control design. With this transformation, many mechanical systems can be represented with the system output measurements as the full state vector. Therefore, these models allow the design of output feedback controllers using state feedback gains. For this work, a set of uncertain models for a Control Moment Gyroscope is assembled and a polytope discretization is performed. The resulting set of models is transformed into the Implicit Derivative Estimator and integrators are added for the output tracking. Finally, a robust H2 digital control is designed, considering the set of uncertain models. The controller is validated through simulation and practical experiments.

Model Predictive Control is a control technique that has been greatly investigated in recent years. It has the versatility of different types of models for the prediction of the system and aptitude to handle the system constraints. In the last decade, the multi-parametric optimization has been applied to the control theory that allowed for the MPC optimization to be performed offline, which was denominated as explicit Model Predictive Control. This work investigates the application of this control technique in Inverted Pendulum systems, which are commonly used as didactic control systems. The complete control design is described considering its validation for two Inverted Pendulum systems through simulations.