Adaptive control of dual-motor autonomous steering system for intelligent vehicles via Bi-LSTM and fuzzy methods

He, Shenguang; Xu, Xing; Xie, Ju; Wang, Rui; Liu, Zhenyu

doi:10.1016/j.conengprac.2022.105362

Cited by 17 publications

(6 citation statements)

References 27 publications

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“…The algorithm was tested in a Carsim/Matlab cosimulation environment and compared with a baseline MPC for only tracking the desired path and speeds. Similarly, in [36], the authors proposed a steering control tracking strategy for roads with different tire-road friction coefficients. To estimate the friction coefficient, they developed a long short-term memory (LSTM) network which consisted of four LSTM layers and two fully connected (FC) layers.…”

Section: Adverse Weather Conditionsmentioning

confidence: 99%

Autonomous Vehicle Decision-Making and Control in Complex and Unconventional Scenarios—A Review

2023

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The development of autonomous vehicles (AVs) is becoming increasingly important as the need for reliable and safe transportation grows. However, in order to achieve level 5 autonomy, it is crucial that such AVs can navigate through complex and unconventional scenarios. It has been observed that currently deployed AVs, like human drivers, struggle the most in cases of adverse weather conditions, unsignalized intersections, crosswalks, roundabouts, and near-accident scenarios. This review paper provides a comprehensive overview of the various navigation methodologies used in handling these situations. The paper discusses both traditional planning methods such as graph-based approaches and emerging solutions including machine-learning based approaches and other advanced decision-making and control techniques. The benefits and drawbacks of previous studies in this area are discussed in detail and it is identified that the biggest shortcomings and challenges are benchmarking, ensuring interpretability, incorporating safety as well as road user interactions, and unrealistic simplifications such as the availability of accurate and perfect perception information. Some suggestions to tackle these challenges are also presented.

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Section: Adverse Weather Conditionsmentioning

confidence: 99%

Autonomous Vehicle Decision-Making and Control in Complex and Unconventional Scenarios—A Review

2023

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“…Extensive research investigations have focused on path-tracking control for road vehicles, aiming to develop a diverse range of robust control algorithms. These encompass the implementation of sliding-mode controller (SMC) techniques [16], the utilization of neural networks [17,18], integration of fuzzy systems (FLS) [19,20], adoption of backstepping methodologies [21], and exploration of various optimal and model-predictive control approaches [22]. In particular, SMC has been successfully employed for the chassis control of four-wheel independent control electric vehicles [23], as well as for hierarchical energy efficiency optimization control strategies in distributed drive electric vehicles [24].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The proposed method incorporated a neural network autoregressive model, Frenet-Serret differential geometry-based path following, and vehicle vertical motion modeling in order to accomplish enhanced yaw stabilization and transient tracking performance while considering input saturation. In [20], a robust fuzzy control approach was presented for lateral path following of autonomous road vehicles subject to parametric uncertainties, disturbances, and varying speeds. The proposed method utilized a non-singleton fuzzy system to account for parametric variations and errors related to measurements to guarantee path-following performance under diverse operating conditions and external disturbances.…”

Section: Literature Reviewmentioning

confidence: 99%

Adaptive Robust Terminal Sliding Mode Control with Integral Backstepping Synthesized Method for Autonomous Ground Vehicle Control

Taghavifar,

Mohammadzadeh

2023

Vehicles

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Autonomous ground vehicles (AGVs) operating in complex environments face the challenge of accurately following desired paths while accounting for uncertainties, external disturbances, and initial conditions, necessitating robust and adaptive control strategies. This paper addresses the critical path-tracking task in AGVs through a novel control framework for multilevel speed AGVs, considering both structured and unstructured uncertainties. The control system introduced in this study utilizes a nonlinear adaptive approach by integrating integral backstepping with terminal sliding mode control (IBTSMC). By incorporating integral action, IBTSMC continuously adjusts the control input to minimize tracking errors, improving tracking performance. The hybridization of the terminal sliding mode method enables finite time convergence, robustness, and a chatter-free response with reduced sensitivity to initial conditions. Furthermore, adaptive control compensators are developed to ensure robustness against unknown but bounded external disturbances. The Lyapunov stability theorem is employed to guarantee the global asymptotic stability of the closed-loop system and the convergence of tracking errors to the origin within finite time. To validate the effectiveness of the proposed control scheme, high-fidelity cosimulations are conducted using CarSim and MATLAB. Comparative analysis is performed with other methods reported in the literature. The results confirm that the proposed controller demonstrates competitive effectiveness in path-tracking tasks and exhibits strong efficiency under various road conditions, parametric uncertainties, and unknown disturbances.

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“…In recent years, researchers in this field have continuously improved tracking control technology through breakthroughs in sensor technology and innovation in control theory [6][7][8]. For model predictive control methods, it is necessary to deal with finite time domain optimization problems with future time steps, and their computational efficiency and optimization solving ability directly determine the control accuracy and real-time performance of the model predictive controller, which in turn affect the effectiveness of vehicle trajectory tracking and stability control [9,10]. Thus, the optimization and improvement of model predictive control algorithms have important practical significance.…”

Section: Introductionmentioning

confidence: 99%

Method for the Trajectory Tracking Control of Unmanned Ground Vehicles Based on Chaotic Particle Swarm Optimization and Model Predictive Control

Jin,

Li,

Chen

2024

Symmetry

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The symmetry principle has significant guiding value in vehicle dynamics modeling and motion control. In complex driving scenarios, there are problems of low accuracy and large time delay in the trajectory tracking control of unmanned ground vehicles. In order to solve this problem and improve the motion control of unmanned ground vehicles, a vehicle coordination control method based on chaotic particle swarm optimization (CPSO) and model predictive control (MPC) algorithms is proposed. To achieve coordinated control of vehicle trajectory tracking and yaw stability, a model predictive controller was designed with the objective of minimizing trajectory tracking errors and yaw stability tracking errors. The required front wheel angle and yaw torque control variables were obtained by solving nonlinear constraint optimization. At the same time, considering the problems of low computational efficiency, high solving time, and local optimization in model predictive control, a chaotic particle swarm optimization algorithm is introduced to solve the optimization constraint problem within model predictive control, thereby effectively improving the computational efficiency and accuracy of the model predictive trajectory tracking controller. The results show that compared with MPC, the multi-objective function optimization solution time and vehicle lane changing time of CPSOMPC improved by 24.51% and 7.21%, respectively, which indicates the coordinated control method that combines the CPSO and MPC algorithms can effectively improve trajectory tracking performance while ensuring vehicle lateral stability.

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Adaptive control of dual-motor autonomous steering system for intelligent vehicles via Bi-LSTM and fuzzy methods

Cited by 17 publications

References 27 publications

Autonomous Vehicle Decision-Making and Control in Complex and Unconventional Scenarios—A Review

Autonomous Vehicle Decision-Making and Control in Complex and Unconventional Scenarios—A Review

Adaptive Robust Terminal Sliding Mode Control with Integral Backstepping Synthesized Method for Autonomous Ground Vehicle Control

Method for the Trajectory Tracking Control of Unmanned Ground Vehicles Based on Chaotic Particle Swarm Optimization and Model Predictive Control

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