Vehicle Dynamics Control Using Control Allocation

Advancement of the state of the art of automotive technologies is a continuous process. It is essential for automotive engineers to combine the knowledge of vehicle dynamics and control theory to develop useful applications that meet requirements of improved safety, comfort and performance. A road vehicle is equipped with several actuators that can assist a user during a dynamic driving task and ensure overall system reliability. Using all available actuators effectively to make a vehicle move in the desired manner is necessary. Typically, the available actuators outnumber the states of motion to be controlled. Such mechanical systems are referred to as over-actuated.An effective way to control an over-actuated system is through the use of control allocation (CA). CA ensures coordination between, and the optimal use, of all available actuators. This strategy also considers the limits of the actuators. Despite its features, a lot of CA methods have a drawback that actuator dynamics are neglected. This drawback has been addressed with a method called model predictive control allocation (MPCA). The behaviour of mechanical actuators is usually approximated by simplified models. Un-modelled system dynamics are always a source of uncertainty. Also, the aging of actuators introduces the element of uncertainty. The ability of MPCA to handle uncertainties is investigated and a solution is proposed to overcome this shortcoming. The proposed solution is the combination of an online adaptive parameter estimator with the MPCA strategy. This way, the CA solver is constantly updated with the parameters of each actuator. This technique is used to design vehicle stability controllers and their performance on simulation is reported.The results indicate that the proposed control allocation technique is effective for vehicle stability control in various scenarios. However, scope for betterment has been recognised and relevant recommendations are made, to conclude this work.Mechanical Engineering | Vehicle Engineerin

Vehicle Dynamics Control Using Model Predictive Control Allocation Combined with an Adaptive Parameter Estimator

Advanced passenger vehicles are complex dynamic systems that are equipped with several actuators, possibly including differential braking, active steering, and semi-active or active suspensions. The simultaneous use of several actuators for integrated vehicle motion control has been a topic of great interest in literature. To facilitate this, a technique known as control allocation (CA) has been employed. CA is a technique that enables the coordination of various actuators of a system. One of the main challenges in the study of CA has been the representation of actuator dynamics in the optimal CA problem (OCAP). Using model predictive control allocation (MPCA), this problem has been addressed. Furthermore, the actual dynamics of actuators may vary over the lifespan of the system due to factors such as wear, lack of maintenance, etc. Therefore, it is further required to compensate for any mismatches between the actual actuator parameters and those used in the OCAP. This is done by combining the MPCA solver with an online adaptive parameter estimation (APE) algorithm. In this study, an advanced solution to the OCAP is proposed by combining MPCA with APE. This solution coordinates differential braking and active front steering (AFS) of a passenger vehicle, to stabilize the lateral and yaw motion. The simulation results indicate that the APE+MPCA combination effectively accounts for actuator dynamics and actuator parameter mismatches.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Intelligent Vehicle