Model predictive control‐based longitudinal control for autonomous electric vehicle with changing mass

Wang, Yijing; Deng, Yuxiang; Wang, Haoyu; Zuo, Zhiqiang

doi:10.1002/asjc.2915

Cited by 5 publications

(3 citation statements)

References 27 publications

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“…Equation ( 46) satisfies the constraints of control variables, and the corresponding sequence of states and outputs in the [k + 1, k + N p ] are as follows [31]:…”

Section: Stability Analysis Of the Closed-loop Systemmentioning

confidence: 99%

Model predictive path tracking control of intelligent vehicles based on dual-stage disturbance observer under multi-channel disturbances

Guo,

Guan

et al. 2024

Meas. Sci. Technol.

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Parameter fluctuations, unmodeled dynamics, speed variation, steering actuator faults, and other multi-channel uncertain disturbances are the key challenges faced by the path tracking control of intelligent vehicles, which will affect the accuracy and stability of the path tracking. Therefore, a model predictive control (MPC) method based on a dual-stage disturbance observer (DDOB) is proposed in this paper. First, a tracking error dynamics model considering multi-channel uncertain disturbances is constructed, based on which a model predictive controller is designed to obtain the nominal front wheel steering angle by the Karush-Kuhn-Tucker (KKT) condition. Furthermore, the dual-stage disturbance observer is designed to enable real-time estimation of the system disturbances, and then the estimated disturbances are used as the compensation for the nominal front wheel steering angle, which establishes the MPC control law with parallel compensation of the dual-stage disturbance observer. Finally, the error boundedness of the dual-stage disturbance observer and the global stability of the model predictive controller are analyzed. The effectiveness and superiority of the proposed algorithm are verified through Carsim-Simulink simulation and hardware-in-the-loop (HiL) experiments.

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“…Equation ( 46) satisfies the constraints of control variables, and the corresponding sequence of states and outputs in the [k + 1, k + N p ] are as follows [31]:…”

Section: Stability Analysis Of the Closed-loop Systemmentioning

confidence: 99%

Model predictive path tracking control of intelligent vehicles based on dual-stage disturbance observer under multi-channel disturbances

Guo,

Guan

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…To improve the robustness of the controller to handle the environment and uncertainties of external disturbances and rotational inertia, the receding horizon control [41,42] is introduced to SDIHP method.…”

Section: Receding Horizon Controlmentioning

confidence: 99%

State‐dependent indirect hp‐pseudospectral method for rigid spacecraft attitude optimal control problem

Liu,

2024

Asian Journal of Control

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The state‐dependent indirect ‐pseudospectral (SDIHP) method for rigid spacecraft attitude control is presented in this paper. The optimal controller is obtained by the SDIHP method through classifying the attitude optimal control problem as a nonlinear quadratic optimal control problem. In the proposed control scheme, successive state‐dependent coefficient (SDC) parameterization is initially applied to the spacecraft dynamics to derive an available linear system to decrease the calculation cost and speed. Then, the indirect ‐adaptive spectral discretization is developed to convert the resultant optimal control problem into a sequence of linear equations, which can improve the global computational efficiency and guarantee the solving precision. Hence, the optimal control can be efficiently solved using basic matrix operations under the necessary collocation points based on the ‐adaptive features. Considering the inertia uncertainties and external disturbances, a state feedback controller is generated by the receding horizon control (RHC) framework to improve the robustness. Simulation results are included to show the effectiveness and performance of the suggested optimal method.

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“…As an alternative, game theory optimizes the action of each AGV from the perspective of group performance to achieve the Nash equilibrium (NE) of group OCS, thereby improving group performance [17,18]. In particular, differential games can quantify the dynamic interaction and confrontation process between multi-AGV, which are often used to solve coordination control problems [19], such as pursuit-evasion (PE) [20] and formation control problems [21].…”

Section: Introductionmentioning

confidence: 99%

Optimal coordination in logistics warehousing with sensing and communication limits: A distributed differential game approach

Xue,

Zhan,

Chen

et al. 2023

Asian Journal of Control

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Optimal coordination is essential for multi‐automated guided vehicle (AGV) systems, particularly in logistic transportation cases, where the system task completion time needs to be minimized, with the guarantee of safe operation. This is because an optimal coordination strategy (OCS), if achieved, can significantly improve the transportation system's efficiency. In this paper, to deal with the dynamic interaction process among AGVs, and sensing and communication range limits, we formulate the optimal coordination problem into a distributed differential game (DDG) framework, where individual AGVs only use information communicated from nearby AGVs to design their optimal operation trajectories. This helps to significantly reduce the computational and communication requirements for the multi‐AGV logistic transportation systems. Targeting operation safety and working efficiency requirements, we incorporate collision avoidance and trajectory optimization objectives into the proposed framework. It is shown that local OCS, obtained by solving the DDG problem for each AGV, will converge to the global Nash equilibrium, which represents the most efficient operating condition for the entire logistic transportation system. Finally, the efficacy of the proposed method is demonstrated, based on simulations and experiments, benchmarked with existing logistic warehousing planning and differential game methods. Compared with conventional methods, the proposed framework successfully helps reduce the task completion time by up to 16%.

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Model predictive control‐based longitudinal control for autonomous electric vehicle with changing mass

Cited by 5 publications

References 27 publications

Model predictive path tracking control of intelligent vehicles based on dual-stage disturbance observer under multi-channel disturbances

Model predictive path tracking control of intelligent vehicles based on dual-stage disturbance observer under multi-channel disturbances

State‐dependent indirect hp‐pseudospectral method for rigid spacecraft attitude optimal control problem

Optimal coordination in logistics warehousing with sensing and communication limits: A distributed differential game approach

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