Vehicle Path-Tracking Linear-Time-Varying Model Predictive Control Controller Parameter Selection Considering Central Process Unit Computational Load

Abstract

Model predictive control (MPC) has drawn a considerable amount of attention in automotive applications during the last decade, partially due to its systematic capacity of treating system constraints. Even though having received broad acknowledgements, there still exist two intrinsic shortcomings on this optimization-based control strategy, namely the extensive online calculation burden and the complex tuning process, which hinder MPC from being applied to a wider extent. To tackle these two drawbacks, different methods were proposed. Nevertheless, the majority of these approaches treat these two issues independently. However, parameter tuning in fact has double-sided effects on both the controller performance and the real-time computational burden. Due to the lack of theoretical tools for globally analyzing the complex conflicts among MPC parameter tuning, controller performance optimization, and computational burden easement, a look-up table-based online parameter selection method is proposed in this paper to help a vehicle track its reference path under both the stability and computational capacity constraints. matlab-carsim conjoint simulations show the effectiveness of the proposed strategy.

Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges

“…To realize vehicle path tracking MPC control under different speed and different curvature conditions, Reference [69][70][71][72] proposed parameters adaptive MPC control strategies using fuzzy rules and multiple controllers combination to achieve adaptive adjustment of control parameters under different operating conditions. Reference [73][74][75][76][77] studied the MPC fast online solution methods of path tracking for autonomous vehicle using differential evolution algorithm, Laguerre function, and look-up table to improve the efficiency of MPC controller calculations. When the vehicle is under high-speed, large curvature and complex operating conditions, the vehicle dynamics show nonlinearity, strong coupling, and parameter uncertainty.…”

Compensatory model predictive control for post-impact trajectory tracking via active front steering and differential torque vectoring

“…Note that multi-constrained systems will become unstable since instability can be caused by the deteriorated states, the predictive horizon and the constraints. Although the post-impact path-following performance can be improved by increasing the predictive horizon N p , it causes large model discrepancies and a costly computational load within the predictive process, 25,33 further leading the problem to infeasibility. Accordingly, advanced post-impact measures should be investigated in deteriorated states manipulation and secondary collision mitigation.…”

“…A novel MPC method was presented to implement active steering and braking in critical driving by employing a linear time-varying model, 24 another predictive controller was discussed how hard and soft constraints worked in a path-following framework. 25 To explicitly demonstrate the physical limitations, a multi-constraint MPC algorithm was set to a collision avoidance problem within a time horizon. 26 Thus, front steering angle and differential torque vectoring are formulated as the system input sequences in this paper.…”

A Model Predictive Controller with Longitudinal Speed Compensation for Autonomous Vehicle Path Tracking

“…The path tracking model is shown in Figure 5, illustrating the relationship between the lateral deviation e, the heading deviation θ e , and the distance s along the path. In most of the existing path-tracking controllers, the lateral deviation e, the heading deviation θ e are chosen as the reference states [28,29,32,33], solving the optimization problem by minimizing e and θ e . However, path tracking lateral deviation is minimized when vehicle sideslip is held tangent to the desired path at all times [19,20,37].…”

“…A differential evolution algorithm was introduced into the MPC path tracking controller in order to improve the computational efficiency [31]. The influences caused by the parameters such as prediction horizon, control horizon, and sampling time on tracking accuracy and computational efficiency were analyzed, and a parameter adjustment method for an MPC controller was proposed in [32]. Ref.…”