H-infinity control via scenario optimization for handling and stabilizing vehicle using AFS control

Wasim, Muhammad; Kashif, Amer Sohail; Awan, Asad Ullah; Khan, Muhammad Ahsan; Wasif, Muhammad; Ali, Waqar

doi:10.1109/icecube.2016.7495243

Cited by 10 publications

(8 citation statements)

References 9 publications

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“…Zhang et al (2017) developed a mixed H 2 / H ∞ actuator fault detector design method for electric ground vehicle equipped with the AFS system. In order to handle and stabilize the vehicles as the uncertainty of the parameters exists, a novel H ∞ controller was put forward for AFS vehicles via scenario optimization (Wasim et al, 2016). Ji et al (2017) utilized a novel dynamic game theory for the modeling of shared steering control between the driver and AFS mode; in addition, two different driving scenarios were simulated where the driver’s target path had high or low consistency with that of AFS mode, respectively applying three steering control strategies of Nash equilibrium solution, Stackelberg equilibrium solution and Cooperative Pareto game strategy.…”

Section: Introductionmentioning

confidence: 99%

Stability control of in-wheel motor electric vehicles under extreme conditions

Zhao

Wang

Zhang

2018

Transactions of the Institute of Measurement and Control

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To investigate the stability of in-wheel motor electric vehicles (IWMEVs) under extreme conditions, a novel control strategy including active rear steering (ARS) mode and direct yaw moment control (DYC) mode is proposed in this paper, utilizing the adaptive dynamic neural network (ADNN) algorithm to make the most of the two control modes. Firstly, a three-degree of freedom nonlinear vehicle model as well as some subsystems is established. Then, a two-layer stability control strategy is put forward, where the upper-layer calculates the desired rear steering angle as well as the differential torque of the rear wheels and the lower-layer executes commands and returns relevant signals. Besides, a stability controller based on ADNN algorithm is designed in the upper-layer so as to take advantage of the two modes under extreme conditions. Next, the impacts of initial values of the connection weights on the ability of ADNN algorithm to train and learn are revealed. Consequently, the optimal initial values can be ascertained before the following simulations. Finally, the closed loop simulations of ARS and DYC are carried out under some extreme conditions such as high velocity and low adhesion coefficient roads, and the results indicate that compared with DYC’s difficulty in playing its role, ARS mode can significantly improve the stability of IWMEVs even under extreme conditions.

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Section: Introductionmentioning

confidence: 99%

Stability control of in-wheel motor electric vehicles under extreme conditions

Zhao

Wang

Zhang

2018

Transactions of the Institute of Measurement and Control

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“…As the stability of yaw motions of the ground vehicle is of great importance in terms of safety, various control strategies have been proposed and used for active yaw stability control, including PID control (Lacroix et al, 2011), H ∞ (Wasim et al, 2016), linear matrix inequality (LMI) static state feedback control (Du et al, 2011), SMC (Ding et al, 2017), optimal control (Yang et al, 2009), optimization control technique (Cerone et al, 2009), internal model control (IMC) (Canale et al, 2007), classical PI control (Marino et al, 2011), adaptive control (Ding and Taheri, 2010), feedback control (Zhang et al, 2008), model predictive control (MPC) (Ren et al, 2016), optimal control (Wang et al, 2018), and μ-synthesis approach (Zhao et al, 2018b). In addition, the integration of various control schemes have been investigated, for example SMC and FLC (Lu et al, 2011), SMC and backstopping method (Zhou and Liu, 2010), IMC and SMC (Canale et al, 2009), and SMC and linear quadratic regulator (Zheng et al, 2006).…”

Section: Yaw Stability Control Strategiesmentioning

confidence: 99%

A review of vehicle active safety control methods: From antilock brakes to semiautonomy

Ahangarnejad

Radmehr

Ahmadian

2020

Journal of Vibration and Control

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A comprehensive review of technologies and approaches for active safety systems designed to reduce ground vehicle crashes, as well as the associated severity of injuries and fatalities, is provided. Active safety systems are commonly referred to as systems that can forewarn a driver of a potential safety hazard, or automatically intervene to reduce the likelihood of an accident without requiring driver intervention. The data from naturalistic drivers has shown that such systems are instrumental in improving vehicle safety in various conditions, particularly at higher speeds and under adverse road conditions. The increased integration of sensors, electronics, and real-time processing capabilities has served as one of the critical enabling elements in the widespread integration of active safety systems in modern vehicles. The emphasis is placed on control approaches for active safety systems and their progression over the years from antilock brakes to more advanced technologies that have nearly enabled semiautonomous driving. A review of key active safety control approaches for antilock braking, yaw stability, traction control, roll stability, and various collision avoidance systems is provided.

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“…One of the primary benefits of this control is that it is simple to implement, but the vehicle may exhibit performance degradation and even instability as speeds are increased due to the neglect of vehicle dynamics. (2) The second category is dynamics-based feedback control without prediction, including state feedback control [ 9 , 10 , 11 ], sliding-mode control [ 12 ], and H ∞ control [ 13 , 14 ]. Kritayakirana et al [ 9 ] held the view that using the center of percussion as the control point decoupled the vehicle’s lateral motion and yaw motion, which simplified the solution of the steering angle.…”

Section: Introductionmentioning

confidence: 99%

“…A sliding-mode controller was presented by Imine and Madani [ 12 ] to prevent the lane departure of heavy vehicles. Considering the impact of model uncertainties on driving stability, a H ∞ control via scenario optimization was applied to stabilize the vehicle in [ 13 ]. Mammar et al [ 14 ] proposed an active steering method using robust control theory for vehicle handling improvement.…”

Section: Introductionmentioning

confidence: 99%

Preview Control with Dynamic Constraints for Autonomous Vehicles

Ouyang

Cui

et al. 2021

Sensors

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In this paper, a preview theory-based steering control approach considering vehicle dynamic constraints is presented. The constrained variables are predicted by an error states system and utilized to adjust the control law once the established dynamic constraints are violated. The simulated annealing optimization algorithm for preview length is conducted to improve the adaptability of the controller to varying velocities and road adhesion coefficients. The theoretical stability of a closed-loop system is guaranteed using Lyapunov theory, and further analysis of the system response in time domain and frequency domain is discussed. The results of simulations implemented on Carsim–Simulink demonstrate the favorable performance of the proposed control in tracking accuracy and system stability under extreme conditions.

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H-infinity control via scenario optimization for handling and stabilizing vehicle using AFS control

Cited by 10 publications

References 9 publications

Stability control of in-wheel motor electric vehicles under extreme conditions

Stability control of in-wheel motor electric vehicles under extreme conditions

A review of vehicle active safety control methods: From antilock brakes to semiautonomy

Preview Control with Dynamic Constraints for Autonomous Vehicles

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