A comparison between LTV-MPC and LQR Yaw Rate-Side Slip Controller

Meola, Daniela; Gambino, Giovanni; Palmieri, G; Glielmo, Luigi

doi:10.3182/20091130-3-fr-4008.00021

Cited by 11 publications

(6 citation statements)

References 7 publications

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“…However, Nayl et al have also demonstrated that MPC performs better than LQR. In addition, other researchers have also found that MPC performs better than LQR in the path tracking of front-wheel steering vehicles [24,25]. The reason why MPC performs better is that this control method can handle system constraints well and can add the feedforward information of the reference path.…”

Section: Related Workmentioning

confidence: 99%

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A New Path Tracking Method Based on Multilayer Model Predictive Control

et al. 2019

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At present, many path tracking controllers are unable to actively adjust the longitudinal velocity according to path information, such as the radius of the curve, to further improve tracking accuracy. For this problem, we propose a new path tracking framework based on model predictive control (MPC). This is a multilayer control system that includes three path tracking controllers with fixed velocities and a velocity decision controller. This new control method is named multilayer MPC. This new control method is compared to other control methods through simulation. In this paper, the maximum values of the displacement error and the heading error of multilayer MPC are 92.92% and 77.02%, respectively, smaller than those of nonlinear MPC. The real-time performance of multilayer MPC is very good, and parallel computation can further improve the real-time performance of this control method. In simulation results, the calculation time of multilayer MPC in each control period does not exceed 0.0130 s, which is much smaller than the control period. In addition, when the error of positioning systems is at the centimeter level, the performance of multilayer MPC is still good.

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Section: Related Workmentioning

confidence: 99%

“…However, the real-time performance of nonlinear MPC is suboptimal, requiring further work in the future solve this problem. Figures 18,19,20,21,22,23,and 24 show the results of this group of simulations. The trend of each variable with positioning error was the same as the trend of the ideal variable.…”

Section: Performance With Positioning Errormentioning

confidence: 99%

A New Path Tracking Method Based on Multilayer Model Predictive Control

et al. 2019

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“…Since then, more research results of path tracking based on this method have emerged . In these studies, Meola et al [12] and Yakub et al [15] compared LTV-MPC and linear quadratic regulator (LQR) and showed that LTV-MPC performed better than LQR in terms of path tracking accuracy. Gong et al [13] introduced LTV-MPC-based path tracking to China first and promoted the development of this field in this country.…”

Section: Lmpcmentioning

confidence: 99%

Review and Comparison of Path Tracking Based on Model Predictive Control

et al. 2019

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Recently, model predictive control (MPC) is increasingly applied to path tracking of mobile devices, such as mobile robots. The characteristics of these MPC-based controllers are not identical due to the different approaches taken during design. According to the differences in the prediction models, we believe that the existing MPC-based path tracking controllers can be divided into four categories. We named them linear model predictive control (LMPC), linear error model predictive control (LEMPC), nonlinear model predictive control (NMPC), and nonlinear error model predictive control (NEMPC). Subsequently, we built these four controllers for the same mobile robot and compared them. By comparison, we got some conclusions. The real-time performance of LMPC and LEMPC is good, but they are less robust to reference paths and positioning errors. NMPC performs well when the reference velocity is high and the radius of the reference path is small. It is also robust to positioning errors. However, the real-time performance of NMPC is slightly worse. NEMPC has many disadvantages. Like LMPC and LEMPC, it performs poorly when the reference velocity is high and the radius of the reference path is small. Its real-time performance is also not good enough.

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“…Once this method is used to simulate the driver to achieve trajectory following, the control goal is to eliminate the trajectory error. [11][12][13] Compared with real drivers, this method does not take into account the actual driver characteristics. Because the driver's control goal does not only include trajectory following accuracy, it also has the path error neglecting feature.…”

Section: Introductionmentioning

confidence: 99%

Shared control strategy based on trajectory following and driver intention optimization

Abi

Jin

Xiong

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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During the emergency braking process on the split-[Formula: see text] road, the lateral stability of the vehicle is poor, and the intervention of ABS will cause corresponding lateral disturbance. It is difficult for the driver to control the vehicle accurately. Especially at the end of the braking process, due to the withdrawal of ABS, the increase in braking pressure causes the longitudinal force of the tires on both sides to be inconsistent, which reduces the stability of the vehicle at this time. This paper proposed a shared control strategy to solve the related problems. First, a segmented active steering strategy is used in the driver’s intention optimization algorithm to optimize the driver’s actions in time at the initial stage of the braking process and to optimize the lateral stability of the vehicle by tracking the estimated tire slip angle at the end of the braking process. Then, according to the path envelope based on the driver’s path error neglecting feature and the dynamic state of the vehicle, a flexible control transfer mechanism is established. The trajectory following algorithm based on linear quadratic regulator is used to correct the driver’s intention optimization algorithm according to the flexible control transfer mechanism.

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A comparison between LTV-MPC and LQR Yaw Rate-Side Slip Controller

Cited by 11 publications

References 7 publications

A New Path Tracking Method Based on Multilayer Model Predictive Control

A New Path Tracking Method Based on Multilayer Model Predictive Control

Review and Comparison of Path Tracking Based on Model Predictive Control

Shared control strategy based on trajectory following and driver intention optimization

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