Model predictive control with fuzzy logic switching for path tracking of autonomous vehicles

Awad, Nada; Lasheen, Ahmed; Elnggar, Mahmoud; Kamel, Ahmed

doi:10.1016/j.isatra.2021.12.022

Cited by 37 publications

(23 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a short sampling time i.e., 0.1 s, the algorithm needs to compute several heavy mathematical operations such as prediction, optimization, and constraints handling using a standard quadratic cost function. Of course, there are several options to reduce the computation such as offline implementation [9] and changing the prediction structures using a special function [7,10], yet still the basic microprocessor in existing vehicles needs to be upgraded. This requirement will increase the cost and, in the future, full implementation of autonomous vehicles would further lead to a greater computation demands.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Predictive Functional Control for Automobile Adaptive Cruise Control System

Zainuddin¹,

Abdullah²,

Ahmad³

et al. 2023

IIUMEJ

View full text Add to dashboard Cite

This paper presents the performance analysis of Predictive Functional Control (PFC) for Adaptive Cruise Control (ACC) application. To cope with multiple driving objectives of modern ACC systems such as passenger comfort, safe distancing, and fast time response, an advanced optimal controller such as Model Predictive Control (MPC) is often used. Nevertheless, MPC requires a high computation load due to its complex formulation and may overload the processing power of a microcontroller. Thus, the prime objective of this work is to propose a PFC algorithm as an alternative controller, while providing a formal comparison between MPC and the traditional Proportional Integral (PI) controller. A standard kinematic model for vehicle longitudinal dynamics was modelled and used to derive the control law of PFC. Since the open-loop dynamic of the derived transfer function is not stable, the second objective is to propose a pre-stabilized loop or cascade PFC structure for the system. A complete tuning procedure and analysis were presented. The simulation result shows that although MPC performance is the best for the ACC application with Root Mean Square Error (RMSE) of 1.4873, PFC has shown a promising response with RMSE of 1.5501, which is better compared to the PI controller with RMSE of 1.6219. All the imposed driving constraints such as maximum acceleration, maximum deceleration and safe distance were satisfied in the car following application. Thus, the findings from this work can become a good initial motivation to further explore the capability of the PFC algorithm for future ACC development. ABSTRAK: Kajian ini adalah berkenaan analisis prestasi Kawalan Fungsi Ramalan (PFC) aplikasi Kawalan Mudah Suai (ACC). Bagi memenuhi pelbagai keperluan objektif sistem pemanduan moden ACC seperti keselesaan penumpang, penjarakan selamat dan tindak balas pantas, kawalan optimum terbaru seperti Model Kawalan Ramalan (MPC) sering digunakan. Walau bagaimanapun, MPC memerlukan beban pengiraan tinggi kerana rumusnya yang kompleks dan mungkin mengakibatkan beban berlebihan kuasa pemprosesan mikrokawalan. Oleh itu, matlamat utama kajian ini adalah bagi mencadangkan algoritma PFC yang mempunyai pengiraan mudah sebagai kawalan alternatif, sementara menyediakan perbandingan formal antara MPC dan kawalan tradisional Berkadar Keseluruhan (PI). Oleh kerana model ini tidak stabil, objektif kedua adalah mencadangkan penggunaan struktur PFC berlapis bagi menstabilkan sistem terlebih dahulu sebelum algorithma kawalan digunakan atau dengan menggunakan struktur PFC secara berturut pada sistem. Prosedur lengkap dan terperinci untuk analisis PFC dibentangkan. Dapatan simulasi kajian menunjukkan walaupun prestasi MPC adalah baik bagi aplikasi ACC dengan Ralat Punca Min Kuasa Dua (RMSE) bernilai 1.4873, namun PFC menunjukkan tindak balas baik dengan RMSE bernilai 1.5501 berbanding kawalan PI yang mempunyai RMSE sebanyak 1.6219. Kesemua kekangan seperti pecutan dan nyahpecutan maksima, dan penjarakan selamat bertepatan dengan aplikasi kenderaan ini. Dengan itu, penemuan ini adalah motivasi awal yang baik bagi meneroka lebih jauh keupayaan algoritma PFC bagi membangun ACC pada masa hadapan.

show abstract

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Predictive Functional Control for Automobile Adaptive Cruise Control System

Zainuddin¹,

Abdullah²,

Ahmad³

et al. 2023

IIUMEJ

View full text Add to dashboard Cite

show abstract

“…Taghavifar et al, proposed an exponential sliding mode fuzzy type neural network control method to reduce the nonlinearity of autonomous vehicle systems and the uncertainty of the mathematical model, and to improve the path tracking and lane-keeping performance [ 3 ]. Awad et al, proposed a linear model predictive controller with a Laguerre network for longitudinal and lateral control of path tracking by linearizing a fuzzy-model-based nonlinear system [ 4 ]. Mishra et al, derived the relative pixel density around the path while minimizing the uncertainty of illumination, occlusion, and observation images using a fuzzy-system-based vision sensor.…”

Section: Introductionmentioning

confidence: 99%

Development of a Particle Filter-Based Path Tracking Algorithm of Autonomous Trucks with a Single Steering and Driving Module Using a Monocular Camera

Kim

Jang

et al. 2023

Sensors

View full text Add to dashboard Cite

Recently, in various fields, research into the path tracking of autonomous vehicles and automated guided vehicles has been conducted to improve worker safety, convenience, and work efficiency. For path tracking of various systems applied to autonomous driving technology, it is necessary to recognize the surrounding environment, determine technology accordingly, and develop control methods. Various sensors and artificial-intelligence-based perception methods have limitations in that they must learn a large amount of data. Therefore, a particle-filter-based path tracking algorithm using a monocular camera was used for the recognition of target RGB. The path tracking errors were calculated and a linear-quadratic-regulator-based desired steering angle were derived. The autonomous trucks were steered and driven using a pulse-width-modulation-based steering and driving motor. Based on an autonomous truck with a single steering and driving module, it was verified that the path tracking could be used in three evaluation scenarios. To compare the LQR-based path tracking control performance proposed in this paper, an elliptical path tracking scenario using a conventional sliding mode control with robust control performance was performed. The results show that the RMS of the lateral preview error of the SMC was approximately 18% larger than that of the LQR-based method.

show abstract

“…In the past few years, numerous control techniques have been studied to address the problem of trajectory tracking in autonomous vehicles. The existing control methods, such as sliding mode control (SMC) [ 3 , 4 , 5 ], robust control [ 6 ], model predictive control (MPC) [ 7 , 8 , 9 , 10 , 11 , 12 ], the linear quadratic regulator (LQR) [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ], and the classic PID control [ 8 , 19 ], were proposed to pursue the task of lateral and longitudinal control. However, most of these studies aimed to address the lateral and longitudinal control separately.…”

Section: Introductionmentioning

confidence: 99%

“…The MPC and its derivative algorithms have been extensively investigated with respect to trajectory-tracking control because of its ability to deal with multiple constraints and nonlinear dynamics. For instance, the authors of [ 10 ] presented a multi-input–multi-output linear MPC approach that calculates the steering angle and the angular velocity of the vehicle to track the desired path by considering the vehicle dynamics constraint. Reference [ 7 ] proposed a novel MPC approach to force the vehicle to track the desired vehicle speed, and the non-PDC controller and the Lyapunov theorem were proposed to guarantee the stabilization.…”

Section: Introductionmentioning

confidence: 99%

“…Compared with [ 16 ], this paper considers the coupling relationships between lateral and longitudinal dynamics and designs a longitudinal controller to track the planned vehicle speed. Compared with the path tracking approach presented in [ 10 ], the designed controller is tested in a wider speed range (30–118 km/h). Moreover, compared with the longitudinal control approach presented in [ 7 , 9 ], an EKF observer is established to estimate the longitudinal velocity, which is sensitive to the control process and is hard to measure directly.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

LQR-MPC-Based Trajectory-Tracking Controller of Autonomous Vehicle Subject to Coupling Effects and Driving State Uncertainties

Yuan

Zhao

2022

Sensors

View full text Add to dashboard Cite

This paper presents a lateral and longitudinal coupling controller for a trajectory-tracking control system. The proposed controller can simultaneously minimize lateral tracking deviation while tracking the desired trajectory and vehicle speed. Firstly, we propose a hierarchical control structure composed of upper and lower-level controllers. In the upper-level controller, the linear quadratic regulator (LQR) controller is designed to compute the desired front wheel steering angle for minimizing the lateral tracking deviation, and the model-predictive controller is developed to compute the desired acceleration for maintaining the planed vehicle speed. The lower-level controller enables the achievement of the desired steering angle and acceleration via the corresponding component devices. Furthermore, an observer based on the Extended Kalman Filter (EKF) is proposed to update the vehicle driving states, which are sensitive to the trajectory-tracking control and difficult to measure directly using the existing vehicle sensors. Finally, the Co-simulation (CarSim-MATLAB/Simulink) results demonstrate that the proposed coupling controller is able to robustly realize the trajectory tracking control and can effectively reduce the lateral tracking error.

show abstract

Model predictive control with fuzzy logic switching for path tracking of autonomous vehicles

Cited by 37 publications

References 20 publications

Performance Analysis of Predictive Functional Control for Automobile Adaptive Cruise Control System

Performance Analysis of Predictive Functional Control for Automobile Adaptive Cruise Control System

Development of a Particle Filter-Based Path Tracking Algorithm of Autonomous Trucks with a Single Steering and Driving Module Using a Monocular Camera

LQR-MPC-Based Trajectory-Tracking Controller of Autonomous Vehicle Subject to Coupling Effects and Driving State Uncertainties

Contact Info

Product

Resources

About