MPC-based path tracking controller design for autonomous ground vehicles

Shen, Chen; Guo, Hongyan; Liu, Feng; Chen, Hong

doi:10.23919/chicc.2017.8028887

Cited by 38 publications

(12 citation statements)

References 5 publications

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“…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.…”

Section: G Mpc Control Methodsmentioning

confidence: 99%

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

Yao

Tian

Wang³

et al. 2020

IEEE Access

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Autonomous vehicle technology aims to improve driving safety, driving comfort, and its economy, as well as reduce traffic accident rate. As the basic part of autonomous vehicle motion control module, path tracking aims to follow the reference path accurately, ensure vehicle stability and satisfy the robust performance of the control system. This paper introduces the representative control strategies, robust control strategies and parameter observation-based control strategies on path tracking for autonomous vehicle. Furthermore, the implementations and disadvantages are summarized. Most importantly, the critical review in this paper provides a list and discussion of the remaining challenges and unsolved problems on path tracking control.

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Section: G Mpc Control Methodsmentioning

confidence: 99%

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

Yao

Tian

Wang³

et al. 2020

IEEE Access

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“…More recently, with the advances of computer performance, model predictive control (MPC) has been shown to be an attractive control algorithm for path tracking problem [9]- [12]. It has the advantage of handling the constraints on the state variables and control inputs and achieving multi-objective optimization, such as driver comfort, time consumption, tracking accuracy.…”

Section: Introductionmentioning

confidence: 99%

An Improved Kinematic Model Predictive Control for High-Speed Path Tracking of Autonomous Vehicles

et al. 2020

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Kinematic model predictive control (MPC) is well known for its simplicity and computational efficiency for path tracking of autonomous vehicles, however, it merely works well at low speed. In addition, earlier studies have demonstrated that tracking accuracy is improved by the feedback of yaw rate, as it improves the system transients. With this in mind, it is expected that the performance of path tracking can be improved by a cascaded controller that utilizes kinematic MPC to determine desired yaw rate rather than steering angle, and uses proportional-integral-derivative (PID) control to follow the reference yaw rate. However, directly combining MPC with PID feedback control of yaw rate results in a controller with poor tracking accuracy. The simulation results show that the cascaded MPC-PID controller has relatively stable but larger error compared to classic kinematic and dynamic MPC. Based on the analysis of vehicle sideslip angle, a novel path tracking control method is proposed, which is designed using a kinematic MPC to handle the disturbances on road curvature, a PID feedback control of yaw rate to reject uncertainties and modeling errors, and a vehicle sideslip angle compensator to correct the kinematic model prediction. The proposed controller performances involving steady-state and transient response, robustness, and computing efficiency were evaluated on Carsim/Matlab joint simulation environment. Furthermore, field experiments were conducted to validate the robustness against sensor disturbances and time lag. The results demonstrate that the developed vehicle sideslip compensator is sufficient to capture steer dynamics, and the developed controller significantly improves the performance of path tracking and follows the desired path very well, ranging from low speed to high speed even at the limits of handling. INDEX TERMS Autonomous vehicles, path tracking, lateral control, model predictive control. The associate editor coordinating the review of this manuscript and approving it for publication was Jenny Mahoney.

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“…Similarly, Snider (2009) linearized the lateral dynamic of a vehicle considering the traditional "bicycle model" with Taylor series approach. Linear time-varying model of lateral dynamics based on Taylor expansion to design control algorithms for path trajectory of autonomous vehicle are developed by Shen et al (2017); Lin et al (2019). Second, the dynamic system is simplified into a double integrator plant, which is one of the most fundamental systems in control applications (Rao and Bernstein, 2001).…”

Section: Introductionmentioning

confidence: 99%

“…The results were considered satisfactory after practical experiments to validate the simulation data. Shen et al (2017) applied a MPC-based path tracking control in a ground vehicle over three different types of roads: wet and dry asphalt pavement, and ice-covered soil. Li et al (2019) proposed a nonlinear model predictive controller for path tracking of a ground vehicle considering the Magic Formula.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Model Approximation Methods for Off-road Vehicle Path Tracking with MPC

Sousa

Ayala

2020

Anais Do Congresso Brasileiro De Automática 2020

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This paper presents a comparative study between two different approaches for dynamic systems based on Taylor series and based on a double-integrator, and their influence in path tracking control using model predictive controller (MPC) with the view to solve the path tracking problem of an autonomous off-road vehicle. A physical-mathematical model, including the celebrated tire model known as “Magic Formula”, is modified and linearized to control the steering of the vehicle considering different reference trajectory inputs. Simulation results are considered satisfactory, showing that the model linearized considering Taylor series presented better results compared to the double-integrator which indicates that the prediction of the plant output is driven close to the reference. Moreover, the results show that the implemented control strategies for path tracking are adequate for applications in off-road autonomous vehicles.

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MPC-based path tracking controller design for autonomous ground vehicles

Cited by 38 publications

References 5 publications

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

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

An Improved Kinematic Model Predictive Control for High-Speed Path Tracking of Autonomous Vehicles

Nonlinear Model Approximation Methods for Off-road Vehicle Path Tracking with MPC

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