Model Predictive Stabilization Control of High-Speed Autonomous Ground Vehicles Considering the Effect of Road Topography

Liu, Kai; Gong, Jianya; Chen, Shuping; Zhang, Yu; Chen, Huiyan

doi:10.3390/app8050822

Cited by 30 publications

(14 citation statements)

References 27 publications

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“…Model predictive control (MPC) is an advanced control algorithm that can handle constraints in states and inputs, and it is considered one of the most influential modern control techniques nowadays [1]. Recently, there has been growing interest in MPC in the autonomous driving community, such as adaptive cruise control [2], active steering control [3], path following [4,5] and multi-vehicle cooperation [6]. However, the computational complexity of the MPC algorithm limits its application in fast dynamic systems [7].…”

Section: Introductionmentioning

confidence: 99%

Accelerated convergence of time‐splitting algorithm by relaxation method

Gao

et al. 2022

IET Control Theory & Appl

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The alternating direction method of multipliers (ADMM) is a widely used model predictive control (MPC) acceleration method. It adopts the time‐splitting technique, splitting the original problem into independent subproblems. Relaxed ADMM (R‐ADMM) is a generalization of ADMM that often achieves faster convergence. However, its parameters must be chosen by an expert user. Besides, the existing convergence proof of R‐ADMM adopts a first‐order Taylor approximation, which makes the range of relaxation factors conservative. We tackle these weaknesses by giving rigorous evidence and finding the optimal relaxation factor. Firstly, we deduce the convergence of the R‐ADMM algorithm, yielding an accurate range of relaxation factors. Then, we analyze the relationship between convergence rate and relaxation factor and conclude that the optimal relaxation factor depends on a recurrence condition. Since splitting introduces the equality constraint, the decoupled states are getting close in the iteration, meeting the recursive requirements and helping find the optimal relaxation factor. Finally, various trajectory tracking tasks are conducted to verify the efficiency of the R‐ADMM algorithm. And the simulation results show that the R‐ADMM algorithm reduces the number of iterations by 63.7% compared with the ADMM algorithm in the double lane change task.

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Section: Introductionmentioning

confidence: 99%

Accelerated convergence of time‐splitting algorithm by relaxation method

Gao

et al. 2022

IET Control Theory & Appl

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“…Beijing Institute of Technology proposes a safety envelope control strategy to ensure that autonomous vehicles can track the expect trajectory effectively [12]. e model predictive control theory is used to design the stability control strategy of the autonomous vehicle during high-speed driving considering the terrain factors of the vehicle [13]. A fuzzy controller is designed to control the preview distance and reduce the trajectory tracking deviation from Hefei University of Technology and verified the effectiveness of the control method through actual car experiments [14].…”

Section: Introductionmentioning

confidence: 99%

Trajectory Tracking Control in Real‐Time of Dual‐Motor‐Driven Driverless Racing Car Based on Optimal Control Theory and Fuzzy Logic Method

Zhang

Liu

et al. 2021

Complexity

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To improve the accuracy and timeliness of the trajectory tracking control of the driverless racing car during the race, this paper proposes a track tracking control method that integrates the rear wheel differential drive and the front wheel active steering based on optimal control theory and fuzzy logic method. The model of the lateral track tracking error of the racing car is established. The model is linearized and discretized, and the quadratic optimal steering control problem is constructed. Taking advantage of the differential drive of dual-motor-driven racing car, the dual motors differential drive fuzzy controller is designed and integrated driving with active steering control. Simulation analysis and actual car verification show that this integrated control method can ensure that the car tracks different race tracks well and improve the track tracking control accuracy by nearly 30%.

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“…With mobile robots, uncertain parameters are unavoidable, which are caused by many factors, such as physical error in the machining [7], external disturbances in the environment [8], equipment wear during working [9], and disturbances in the control signals [10]. To deal with these uncertainties parameters, various controllers have been proposed, such as the back-stepping controller [11], model predictive controller [12,13], sliding mode controller [14,15], fuzzy controller [16,17], adaptive tracking controller [18][19][20], and the neural network controller [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Design Hybrid Iterative Learning Controller for Directly Driving the Wheels of Mobile Platform against Uncertain Parameters and Initial Errors

Qiao

Luo

et al. 2021

Applied Sciences

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In this paper, we develop a hybrid iterative learning controller (HILC) for a non-holonomic wheeled mobile platform to achieve trajectory tracking with actual complex constraints, such as physical constraints, uncertain parameters, and initial errors. Unlike the traditional iterative learning controller (ILC), the control variable selects the rotation speed of two driving wheels instead of the forward speed and the rotation speed. The hybrid controller considers the physical constraints of the robot’s motors and can effectively handle the uncertain parameters and initial errors of the system. Without the initial errors, the hybrid controller can improve the convergence speed for trajectory tracking by adding other types of error signals; otherwise, the hybrid controller achieves trajectory tracking by designing a signal compensation for the initial errors. Then, the effectiveness of the proposed hybrid controller is proven by the relationship between the input, output, and status signals. Finally, the simulations demonstrate that the proposed hybrid iterative learning controller effectively tracked various trajectories by directly controlling the two driving wheels under various constraints. Furthermore, the results show that the controller did not significantly depend on the system’s structural parameters.

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Model Predictive Stabilization Control of High-Speed Autonomous Ground Vehicles Considering the Effect of Road Topography

Cited by 30 publications

References 27 publications

Accelerated convergence of time‐splitting algorithm by relaxation method

Accelerated convergence of time‐splitting algorithm by relaxation method

Trajectory Tracking Control in Real‐Time of Dual‐Motor‐Driven Driverless Racing Car Based on Optimal Control Theory and Fuzzy Logic Method

Design Hybrid Iterative Learning Controller for Directly Driving the Wheels of Mobile Platform against Uncertain Parameters and Initial Errors

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