Abstract-This paper presents a novel control strategy for real-time controlled restraint systems. Today's restraint systems typically include a number of airbags, and a three-point seat belt with load limiter and pretensioner. In the class of realtime controlled restraint systems, the restraint actuator settings are continuously manipulated during the crash. The control strategy developed here is based on reference management, in which a nonlinear device -a reference governor -is added to a primal closed loop controlled system. This governor determines an optimal setpoint in terms of injury reduction and constraint satisfaction by solving a constrained optimization problem. Prediction of the vehicle motion, required to predict future constraint violation, is included in the design and is based on linear regression of past crash data. Simulation results with a MADYMO model show that a significant injury reduction is possible, without prior knowledge of the crash. Furthermore, it is shown that the algorithms are sufficiently fast to be implemented on-line.

Abstract-This paper presents a novel control strategy for real-time controlled restraint systems. Today's restraint systems typically include a number of airbags, and a three-point seat belt with load limiter and pretensioner. In the class of realtime controlled restraint systems, the restraint actuator settings are continuously manipulated during the crash. The control strategy developed here is based on reference management, in which a nonlinear device -a reference governor -is added to a primal closed loop controlled system. This governor determines an optimal setpoint in terms of injury reduction and constraint satisfaction by solving a constrained optimization problem. Prediction of the vehicle motion, required to predict future constraint violation, is included in the design and is based on linear regression of past crash data. Simulation results with a MADYMO model show that a significant injury reduction is possible, without prior knowledge of the crash. Furthermore, it is shown that the algorithms are sufficiently fast to be implemented on-line.