Nonlinear model predictive control is proposed in multiple academic studies as an ad-vanced control system technology for vehicle operation at the limits of handling, allow-ing high tracking performance and formal consideration of system constraints. How-ever, the implementation of implicit nonlinear model predictive control (NMPC), in which the control problem is solved on-line, poses significant challenges in terms of computational load. This issue can be overcome through explicit NMPC, in which the optimization problem is solved off-line, and the resulting explicit solution, with guar-anteed level of sub-optimality, is evaluated on-line. Due to the simplicity of the explicit solution, the real-time execution of the controller is possible even on automotive control hardware platforms with low specifications. The explicit nature of the control law fa-cilitates feasibility checks and functional safety validation. This study presents a yaw and lateral stability controller based on explicit NMPC, actuated through the electro-hydraulically controlled friction brakes of the vehicle. The controller performance is demonstrated during sine-with-dwell tests simulated with a high-fidelity model. The analysis includes a comparison of implicit and explicit implementations of the control system.
This study presents a traction control system for electric vehicles with in-wheel motors, based on explicit non-linear model predictive control. The feedback law, available beforehand, is described in detail, together with its variation for different plant conditions. The explicit controller is implemented on a rapid control prototyping unit, which proves the real-time capability of the strategy, with computing times in the order of microseconds. These are significantly lower than the required sampling time for a traction control application. Hence, the explicit model predictive controller can run at the same frequency as a simple traction control system based on Proportional Integral (PI) technology. High-fidelity model simulations provide: i) a performance comparison of the proposed explicit non-linear model predictive controller with a benchmark PI-based traction controller with gain scheduling and anti-windup features; and ii) a performance comparison among two explicit and one implicit non-linear model predictive controllers based on different internal models, with and without consideration of transient tire behavior and load transfers. Experimental test results on an electric vehicle demonstrator are shown for one of the explicit non-linear model predictive controller formulations.
Nonlinear model predictive control (NMPC) is proposed in multiple academic studies as an advanced control system technology for vehicle operation at the limits of handling, allowing high tracking performance and formal consideration of system constraints. However, the implementation of implicit NMPC, in which the control problem is solved on-line, poses significant challenges in terms of computational load. This issue can be overcome through explicit NMPC, in which the optimization problem is solved off-line, and the resulting explicit solution, with guaranteed level of sub-optimality, is evaluated on-line. This study presents a yaw and lateral stability controller based on explicit NMPC, actuated through the friction brakes of the vehicle. The controller performance is demonstrated during sine-with-dwell tests simulated with a high-fidelity model. The analysis investigates the influence of the weights in the cost function formulation and includes a comparison of different settings of the optimal control problem.
This study addresses the development and Hardware-in-the-Loop (HiL) testing of an explicit nonlinear model predictive controller (eNMPC) for an anti-lock braking system (ABS) for passenger cars, actuated through an electro-hydraulic braking (EHB) unit. The control structure includes a compensation strategy to guard against performance degradation due to actuation dead times, identified through experimental tests. The eNMPC is run on an automotive rapid control prototyping unit, which shows its real-time capability with comfortable margin. A validated high-fidelity vehicle simulation model is used for the assessment of the ABS on a HiL rig equipped with the braking system hardware. The eNMPC is tested in 7 emergency braking scenarios, and its performance is benchmarked against a proportional integral derivative (PID) controller. The eNMPC results show: i) the control system robustness with respect to variations of tire-road friction condition and initial vehicle speed; and ii) a consistent and significant improvement of the stopping distance and wheel slip reference tracking, with respect to the vehicle with the PID ABS.
This paper presents a traction controller for combined driving and cornering
conditions, based on explicit nonlinear model predictive control. The prediction
model includes a nonlinear tire force model using a simplified version of
the Pacejka Magic Formula, incorporating the effect of combined longitudinal
and lateral slips. Simulations of a front-wheel-drive electric vehicle with multiple
motors highlight the benefits of the proposed formulation with respect to a controller
with a tire model for pure longitudinal slip. Objective performance indicators
provide a performance assessment in traction control scenarios.
Anti-jerk controllers compensate for the torsional oscillations of automotive
drivetrains, caused by swift variations of the traction torque. In the literature
model predictive control (MPC) technology has been applied to anti-jerk
control problems, by using a variety of prediction models. However, an analysis
of the influence of the prediction model complexity on anti-jerk control performance
is still missing. To cover the gap, this study proposes six anti-jerk MPC
formulations, which are based on different prediction models and are fine-tuned
through a unified optimization routine. Their performance is assessed over multiple
tip-in and tip-out maneuvers by means of an objective indicator. Results
show that: i) low number of prediction steps and short discretization time provide
the best performance in the considered nominal tip-in test; ii) the consideration
of the drivetrain backlash in the prediction model is beneficial in all test cases;
iii) the inclusion of tire slip formulations makes the system more robust with respect
to vehicle speed variations and enhances the vehicle behavior in tip-out
tests; however, it deteriorates performance in the other scenarios; and iv) the inclusion
of a simplified tire relaxation formulation does not bring any particular