Coordination of Motion Actuators in Heavy Vehicles using Model Predictive Control Allocation

The paper presents a Model Predictive Control Allocation (MPCA) method in order to coordinate the motion actuators of a heavy vehicle. The presented method merges the strong points of two different control theories: Model Predictive Control (MPC) and Control Allocation (CA); MPC explicitly considers the motion actuators dynamics before deciding on a suitable input for the actuators while CA dynamically decides how to use the motion actuators in order to modify the vehicle behaviour. The designed MPCA formulation belongs to the class of Quadratic Programming (QP) problems so that the solution is optimization based, i.e. at every step a quadratic cost function has to be minimized while fulfilling a set of linear constraints. Three scenarios were set up to evaluate the effectiveness of the controller: split-mu braking, split-mu acceleration and brake blending. Split-mu means that the wheels on one side of the vehicle are in contact with a slippery surface (e.g. ice) while the wheels of the other side lay on a normal surface (e.g. dry asphalt). The split-mu scenarios aim to combine three different types of motion actuators, disc brakes, powertrain and rear active steering (RAS), in order to brake/accelerate the vehicle while keeping it on course. The third scenario is a mild braking event on a normal road and its purpose is to combine the use of the engine brake with the disc brakes. Simulation results of the scenarios have shown promising vehicle performance when using MPCA to coordinate the motion actuators. Tests on a real vehicle have then confirmed the expected vehicle behaviour in a slit-mu braking scenario. MPCA has also been compared to a simpler CA formulation, in all scenarios. The performance of the two is comparable in steady state, but MPCA shows advantages in transients, whereas CA is less computationally demanding.