Trajectory Tracking of Autonomous Vehicle Based on Model Predictive Control With PID Feedback

Chu, Delin; Li, Haoran; Zhao, Chenyang; Zhou, Tuqiang

doi:10.1109/tits.2022.3150365

Cited by 51 publications

(21 citation statements)

References 36 publications

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“…However, road PF was not discussed because it assumes collision avoidance can be completed within the road. To improve the tracking accuracy, Chu et al [27] used the artificial PF to compute the reference trajectory and combined the MPC with PID feedback for the tracking task. Still, this study established an unalterable road PF and focused more on tracking performance.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Potential Field with Collision Avoidance for Connected Autonomous Vehicles

Lin

Tsukada

2022

2022 13th Asian Control Conference (ASCC)

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Path planning is critical for autonomous vehicles (AVs) to determine the optimal route while considering constraints and objectives. The potential field (PF) approach has become prevalent in path planning due to its simple structure and computational efficiency. However, current PF methods used in AVs focus solely on the path generation of the ego vehicle while assuming that the surrounding obstacle vehicles drive at a preset behavior without the PF-based path planner, which ignores the fact that the ego vehicle's PF could also impact the path generation of the obstacle vehicles. To tackle this problem, we propose a PF-based path planning approach where local paths are shared among ego and obstacle vehicles via vehicle-tovehicle (V2V) communication. Then by integrating this shared local path into an objective function, a new optimization function called interactive speed optimization (ISO) is designed to allow driving safety and comfort for both ego and obstacle vehicles. The proposed method is evaluated using MATLAB/Simulink in the urgent merging scenarios by comparing it with conventional methods. The simulation results indicate that the proposed method can mitigate the impact of other AVs' PFs by slowing down in advance, effectively reducing the oscillations for both ego and obstacle AVs.

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Section: Related Workmentioning

confidence: 99%

Adaptive Potential Field with Collision Avoidance for Connected Autonomous Vehicles

Lin

Tsukada

2022

2022 13th Asian Control Conference (ASCC)

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“…The main challenges are highly nonlinearity, multiple constraints of the vehicle itself and subject to external interference characteristics. So far, some control methodologies, including PID control [1], fuzzy control [2], H∞ control [3], sliding mode control [4][5], and backstepping control [6], turn out to be successful to handle some parameter perturbations and disturbance, but they are unable to solve multiple constraints imposed by the vehicle model, such as actual physical limits, vehicle stability and driving comfortability limits. Therefore, it is important to propose the efficient control scheme to deal with the above problems, This work was supported in part by the National Natural Science Foundation of China under Grant U1808205, Grant 62173079 and Grant 61903072, and in part by the Fundamental Research Funds for the Central Universities under Grant N2223029.…”

Section: Introductionmentioning

confidence: 99%

Efficient Nonlinear Model Predictive and Active Disturbance Rejection Control for Trajectory Tracking of Unmanned Vehicles

Wang,

Liu,

Zhang

et al. 2024

Preprint

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This article proposes an efficient trajectory tracking control strategy of unmanned vehicles. The method is based on nonlinear model predictive control (NMPC) and active disturbance reject control (ADRC). The designed control algorithm considers three challenges including nonlinear characteristics, multiple constraints and external disturbance. First, NMPC method is presented for the nonlinear vehicle model with multiple constraints. To relax inequality constraints and reduce the heavy calculation burden, the penalty term with the variable factor is added to the cost function, an improved continuous/generalized minimum residual method is proposed to solve NMPC online optimization problem. Then, an ADRC scheme is deigned to estimate the unknown disturbance via extended state observer, and compensate them by feedback control law in real time, the corresponding parameters are obtained by novel particle swarm optimization algorithm to further improve the control precision. It ensures the stability to a certain extent. Finally, the results indicate the designed algorithm can raise the calculation efficiency and meet the real-time requirements, and obviously increase the tracking accuracy and robustness performance.

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“…In addition, Shen, C and Shi, Y investigated the nonlinear model predictive control (NMPC) method, looking for possible approaches to alleviate the heavy computational burden, and developed novel distributed NMPC algorithms by exploiting the dynamic properties of the autonomous underwater vehicle motion for trajectory-tracking control [ 10 ]. Chu, D. et al presented a trajectory planning and tracking framework to obtain target trajectory and MPC with PID feedback to effectively track planned trajectory [ 11 ]. In [ 12 ], an improved MPC algorithm with fuzzy adaptive weight control was proposed for autonomous vehicles to ensure tracking accuracy and dynamic stability during path tracking.…”

Section: Introductionmentioning

confidence: 99%

Development of a Sliding-Mode-Control-Based Path-Tracking Algorithm with Model-Free Adaptive Feedback Action for Autonomous Vehicles

Seo

2022

Sensors

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This paper presents a sliding mode control (SMC)-based path-tracking algorithm for autonomous vehicles by considering model-free adaptive feedback actions. In autonomous vehicles, safe path tracking requires adaptive and robust control algorithms because driving environment and vehicle conditions vary in real time. In this study, the SMC was adopted as a robust control method to adjust the switching gain, taking into account the sliding surface and unknown uncertainty to make the control error zero. The sliding surface can be designed mathematically, but it is difficult to express the unknown uncertainty mathematically. Information of priori bounded uncertainties is needed to obtain closed-loop stability of the control system, and the unknown uncertainty can vary with changes in internal and external factors. In the literature, ongoing efforts have been made to overcome the limitation of losing control stability due to unknown uncertainty. This study proposes an integrated method of adaptive feedback control (AFC) and SMC that can adjust a bounded uncertainty. Some illustrative and representative examples, such as autonomous driving scenarios, are also provided to show the main properties of the designed integrated controller. The examples show superior control performance, and it is expected that the integrated controller could be widely used for the path-tracking algorithms of autonomous vehicles.

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Trajectory Tracking of Autonomous Vehicle Based on Model Predictive Control With PID Feedback

Cited by 51 publications

References 36 publications

Adaptive Potential Field with Collision Avoidance for Connected Autonomous Vehicles

Adaptive Potential Field with Collision Avoidance for Connected Autonomous Vehicles

Efficient Nonlinear Model Predictive and Active Disturbance Rejection Control for Trajectory Tracking of Unmanned Vehicles

Development of a Sliding-Mode-Control-Based Path-Tracking Algorithm with Model-Free Adaptive Feedback Action for Autonomous Vehicles

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