Parallel Distributed Compensation /H∞ Control of Lane-keeping System Based on the Takagi-Sugeno Fuzzy Model

Chen, Wuwei; Zhao, Lian; Wang, Huiran; Huang, Yao

doi:10.1186/s10033-020-00477-9

Cited by 14 publications

(9 citation statements)

References 25 publications

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“…(Huang et al, 2019) presents a cooperative framework developed by adopting data-driven adaptive dynamic programming and an iterative learning scheme based on classical small-gain theory. In (Chen et al, 2020), the lane-keeping control method adopts a driver model based on near and far visual angles with a robust parallel distributed compensation H ∞ controller. These approaches typically validated the cooperative performance of the DiL design for lane keeping tasks.…”

Section: State Of the Artmentioning

confidence: 99%

Online driver model parameter identification using the Lyapunov approach based shared control

Oudainia¹,

Sentouh²,

Nguyen³

et al. 2022

Front. Control Eng.

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The work described in this paper proposes a new conflict minimisation strategy in shared driving control for lane keeping systems (LKS) in intelligent vehicles. This strategy takes into account a dynamic driver model, where the driver’s parameters are identified online using the Lyapunov approach. The design of an adaptive shared controller is based on the dynamic parameters of the driver model which changes according to the driver and the situation encountered. Based on Lyapunov stability arguments, the overall asymptotic stability of the closed-loop control system with the adaptive driver model and the variation of the vehicle speed is proved and an LMI optimization is used to formulate the control design. The simulation results, conducted with the SHERPA dynamic car simulator under real-world driving situations, show the importance of integrating a dynamic driver model in the controller design in order to decrease the conflict between the driver and the lane keeping system and to ensure the safety of the vehicle as well as to increase the confidence and acceptability of the driver.

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Section: State Of the Artmentioning

confidence: 99%

Online driver model parameter identification using the Lyapunov approach based shared control

Oudainia¹,

Sentouh²,

Nguyen³

et al. 2022

Front. Control Eng.

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“…The model is based on the Fuzzy Logic Controller module in Simulink [40][41][42][43]. The article inputs the fuzzy controller file or structural data and outputs the desired value through the fuzzy control box as expressed in Fig.…”

Section: Modeling Of Fuzzy Controllersmentioning

confidence: 99%

Fuzzy logic-based energy management strategy for a novel mechatronics-electro-hydraulic power coupling electric vehicle

Yang¹,

Zhang²,

Hong³

et al. 2021

Preprint

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In order to solve the troubles of electric peak torque and enhance the energy efficiency of electric vehicles, a novel mechatronics-electro-hydraulic power coupling electric vehicle (MEH-PCEV) with low energy consumption is proposed. The hydraulic system and the motor are integrated into a device to simplify the structure, taking the pure electric vehicle as a reference. Simultaneously, a fuzzy logic-based optimization method is proposed for real-time adjustment of the electric torque based on the original rule control strategy. Compared with the pure electric vehicle, the proposed methods can substantially enhance energy utilization and the recovery efficiency of electric energy. Ultimately, the actual driving cycle is analyzed using data acquisition capacity, with the authentic speed as the input signal. The verification results on real-world vehicles demonstrate the consumption rate of the battery state of charge and the electric torque are improved by 7.32% and reduced by 22.24%, respectively. Moreover, this research is expected to provide a reference for the development and engineering applications of the mechatronics-electro-hydraulic coupling systems.

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“…Chen et al established the human-vehicleroad closed-loop model based on TS fuzzy theory for vehicle lane-keeping. The H ∞ robust steering torque compensation controller was designed to reduce the lateral offset [17]. Though H ∞ robust control guarantees the system robustness under a certain parameter perturbation range, the control performance is conservative.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Robust Path Tracking Control for Autonomous Vehicles Considering Multi-Dimensional System Uncertainty

Ren

2023

WEVJ

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As the bottom layer of the autonomous vehicle, path tracking control is a crucial element that provides accurate control command to the X-by-wire chassis and guarantees the vehicle safety. To overcome the deterioration of control performance for autonomous vehicle path-tracking controllers caused by modeling errors and parameter perturbation, an adaptive robust control framework is proposed in this paper. Firstly, the 2-DOF vehicle dynamic model is established and the non-singular fast terminal sliding mode control algorithm is adopted to formulate the control law. The unmeasured model disturbance and parameter perturbation is regarded as the system uncertainty. To enhance the control accuracy, the radial basis forward neural network is introduced to estimate such uncertainty in real time. Then, the dynamic model of an active front steering system is established. The model reference control algorithm is applied for the steering torque control considering model uncertainty brought by the dissipation of manufacturing and mechanical wear. Finally, the Simulink–CarSim co-simulation platform is used and the proposed control framework is validated in two test scenarios. The simulation results demonstrate the proposed adaptive robust control algorithm has satisfactory control performance and good robustness against the system uncertainty.

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Parallel Distributed Compensation /H∞ Control of Lane-keeping System Based on the Takagi-Sugeno Fuzzy Model

Cited by 14 publications

References 25 publications

Online driver model parameter identification using the Lyapunov approach based shared control

Online driver model parameter identification using the Lyapunov approach based shared control

Fuzzy logic-based energy management strategy for a novel mechatronics-electro-hydraulic power coupling electric vehicle

Adaptive Robust Path Tracking Control for Autonomous Vehicles Considering Multi-Dimensional System Uncertainty

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