A Model Predictive Control with Preview-Follower Theory Algorithm for Trajectory Tracking Control in Autonomous Vehicles

Xu, Ying; Tang, Wen; Qiu, Li; Yang, Rong

doi:10.3390/sym13030381

Cited by 31 publications

(23 citation statements)

References 32 publications

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“…With the increasing use of robots in the fields of industry, rehabilitation, aviation, and marine exploration, the demand for robots that can adapt to complex environments and enable human-robot interaction is increasing, which introduces mission planning and trajectory tracking control on several applications related to robotics vehicles [5][6][7]. Besides, the author Mofid O., in collaboration with other colleagues, has published important information related to the tracking of Quadrotor helicopter trajectories, applying robust control techniques such as adaptive backstepping and sliding mode control to reduce external disturbances as well as uncertainties in the model, demonstrating stability and convergence in finite time, which guarantees adequate and correct tracking performance of the Quadrotor helicopter on the references provided for the fulfillment of a mission.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Improvement of a Trajectory-Tracking Control Based on Super-Twisting Algorithm for a Quadrotor Aircraft

González-Hernández

Salazar

Lozano

et al. 2022

Drones

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This article addresses the development and experimental validation of a trajectory-tracking control for a miniature autonomous Quadrotor helicopter system (X4-prototype) using a robust algorithm control based on second-order sliding mode technique or also known as super-twisting algorithm in outdoor environments. This nonlinear control strategy guarantees the convergence in finite time to a desired path r(t) in the presence of external disturbances or uncertainties in the model affecting the appropriate behavior of our Quadrotor helicopter. For this purpose, a polynomial smooth curve trajectory is selected as a reference signal where the corresponding derivatives of the function are bounded. Moreover, we consider disturbances due to wind gusts acting on the aerial vehicle, and the reference signal is pre-programmed in an advanced autopilot system. The proposed solution consists of implementing a real-time control law based on super-twisting control using GPS measurements in order to obtain the position in the xy-plane to accomplish the desired trajectory. Simulation and experimental results of trajectory-tracking control are presented to demonstrate the performance and robustness of the proposed nonlinear controller in windy conditions.

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Section: Introductionmentioning

confidence: 99%

Real-Time Improvement of a Trajectory-Tracking Control Based on Super-Twisting Algorithm for a Quadrotor Aircraft

González-Hernández

Salazar

Lozano

et al. 2022

Drones

View full text Add to dashboard Cite

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“…Three main types of MPC methods are used in the field of path tracking, the linear parameter-varying MPC (LPV-MPC) [ 7 , 8 , 9 ] the linear time-varying MPC (LTV-MPC) [ 10 , 11 , 12 , 13 ], and the nonlinear MPC (NLMPC) [ 10 , 14 , 15 , 16 ] solutions. The LPV-MPC applies a linear vehicle model for state prediction and the structure of that model does not change over time; however, a few of the variables, e.g., the velocity of the vehicle, can change.…”

Section: Introductionmentioning

confidence: 99%

LTV-MPC Approach for Automated Vehicle Path Following at the Limit of Handling

Domina

Tihanyi

2022

Sensors

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In this paper, a linear time-varying model predictive controller (LTV-MPC) is proposed for automated vehicle path-following applications. In the field of path following, the application of nonlinear MPCs is becoming more common; however, the major disadvantage of this algorithm is the high computational cost. During this research, the authors propose two methods to reduce the nonlinear terms: one is a novel method to define the path-following problem by transforming the path according to the actual state of the vehicle, while the other one is the application of a successive linearization technique to generate the state–space representation of the vehicle used for state prediction by the MPC. Furthermore, the dynamic effect of the steering system is examined as well by modeling the steering dynamics with a first-order lag. Using the proposed method, the necessary segment of the predefined path is transformed, the linearized model of the vehicle is calculated, and the optimal steering control vector is calculated for a finite horizon at every timestep. The longitudinal dynamics of the vehicle are controlled separately from the lateral dynamics by a PI cruise controller. The performance of the controller is evaluated and the effect of the steering model is examined as well.

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“…Trajectory tracking is an indispensable part of control execution technology, which relies on the underlying control technology of the vehicle and also requires good tracking of the planned trajectory, and good tracking effect has important significance for the safety and control of intelligent vehicles, which can reduce the probability of traffic accidents due to driver negligence and can reduce the dependence of previous vehicles on driver operation. The preview follower theory can accurately reflect the driver's control behavior [2], and the lateral position deviation of the preview point position from the desired trajectory can be obtained based on the two-degree-of-freedom vehicle dynamic model, and then the ideal yaw rate which is used to provide input for the sliding mode controller can be calculated.…”

Section: Introductionmentioning

confidence: 99%

Research on Trajectory Tracking of Sliding Mode Control Based on Adaptive Preview Time

Bei

Zhao

et al. 2022

Actuators

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The preview model is one of the common methods used in trajectory tracking. The traditional fixed preview time is not adaptable to most speeds and road conditions, which not only reduces the tracking accuracy but also reduces the vehicle stability. Therefore, a controller can be designed to determine the adaptive preview time based on an optimization function of the lateral deviation, the road boundary, and the road boundary of the whole vehicle motion response characteristics. Traditional optimal preview control theory predicts the next state of the vehicle by the assumption of constant transverse pendulum angular velocity. In this paper, an expectation-based approach is used to find the ideal steering wheel turning angle based on the adaptive preview time, and a single-point preview model is established. Based on the two-degree-of-freedom dynamics model, a sliding mode controller is designed for control, and the low-pass filters are designed to suppress jitter in the sliding mode controller. Simulation results with different preview times, different speeds and different road adhesion coefficients prove that the controller has a good control effect and has good effectiveness and adaptability to speed and adhesion coefficient.

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A Model Predictive Control with Preview-Follower Theory Algorithm for Trajectory Tracking Control in Autonomous Vehicles

Cited by 31 publications

References 32 publications

Real-Time Improvement of a Trajectory-Tracking Control Based on Super-Twisting Algorithm for a Quadrotor Aircraft

Real-Time Improvement of a Trajectory-Tracking Control Based on Super-Twisting Algorithm for a Quadrotor Aircraft

LTV-MPC Approach for Automated Vehicle Path Following at the Limit of Handling

Research on Trajectory Tracking of Sliding Mode Control Based on Adaptive Preview Time

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