Vehicle Lateral Dynamics Control Through AFS/DYC and Robust Gain-Scheduling Approach

Zhang, Hui; Wang, Jun‐Min

doi:10.1109/tvt.2015.2391184

Cited by 314 publications

(123 citation statements)

References 33 publications

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“…In [101], a gain scheduling strategy is employed to improve the control performance of networked control system. In [102], the delay issue in MG is solved by designing the gain scheduling method. An algebra Riccati equation (ARE) method is adopted to design control gain in [103].…”

Section: B Fixed Communication Delaymentioning

confidence: 99%

“…Model predictive control [90][91][92][93] ·Reduce data loss ·Good robustness at large delay error ·Can handle fixed and random delay ·Complex algorithm ·Large computation load ·Low dynamic response Smith predictor [90,94,95] ·Fast dynamic response ·Can handle fixed delay and random delay ·Model uncertainties and external disturbances are existed Neural network predictive control [96] ·Linearized ·High robustness ·Not handling random delay Weighted average predictive control [97,98] ·Improve consensus convergence ·Fast tracking speed ·Strong anti-interference ability ·Influence of practical factors on prediction are not considered ·Not handling random delay Gain scheduling method [99][100][101][102][103][104][105][106] ·Provide a general modeling approach ·Cost reduction ·Good power sharing performance in delay margin ·High robustness ·Gain coefficient and integral term are difficult to be selected ·Not handling random delay H∞ control method [107][108][109] ·Handle fixed and random delay ·Large computation ·Complex algorithm ·Low robustness Sliding mode control [110][111][112][113] ·Simple implementation ·Handle fixed and random delay ·Fast dynamic response ·Robustness under parameter variation and disturbance ·Chattering exists…”

Section: Random Communication Delaymentioning

confidence: 99%

“…The multiple time-scale property of the hierarchical control causes communication delays among devices, or between equipment and upper control level [83]. It is worth mentioning that, delays are mainly divided into the fixed communication delays and random communication delays, and various delay compensation schemes for fixed communication delays are compared in this paper, such as the neural network predictive control [96], weighted average predictive control [97,98], the gain scheduling [99][100][101][102][103][104][105][106] and synchronization schemes using multi-timer model [114]. Moreover, some compensation methods for random communication delays are also discussed, such as the generalized predictive control (GPC) [84,85], networked predictive control (NPC) [86][87][88][89], model predictive control (MPC) [90][91][92][93], Smith predictor (SP) [90,94,95], H ∞ control [107][108][109] and sliding mode control [110][111][112][113], etc.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

MAS-Based Distributed Coordinated Control and Optimization in Microgrid and Microgrid Clusters: A Comprehensive Overview

Han

Zhang

et al. 2018

IEEE Trans. Power Electron.

366

182

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Abstract-The increasing integration of the distributed renewable energy sources highlights the requirement to design various control strategies for microgrids (MGs) and microgrid clusters (MGCs). The multi-agent system (MAS)-based distributed coordinated control strategies shows the benefits to balance the power and energy, stabilize voltage and frequency, achieve economic and coordinated operation among the MGs and MGCs. However, the complex and diverse combinations of distributed generations in multi-agent system increase the complexity of system control and operation. In order to design the optimized configuration and control strategy using MAS, the topology models and mathematic models such as the graph topology model, non-cooperative game model, the genetic algorithm and particle swarm optimization algorithm are summarized. The merits and drawbacks of these control methods are compared. Moreover, since the consensus is a vital problem in the complex dynamical systems, the distributed MAS-based consensus protocols are systematically reviewed. NOMENCLATURE

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Section: B Fixed Communication Delaymentioning

confidence: 99%

Section: Random Communication Delaymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

MAS-Based Distributed Coordinated Control and Optimization in Microgrid and Microgrid Clusters: A Comprehensive Overview

Han

Zhang

et al. 2018

IEEE Trans. Power Electron.

366

182

View full text Add to dashboard Cite

show abstract

“…In previous studies, the DYC in the upper-level controller usually chooses the sideslip angle and the yaw rate or one of them as the control target [15,16]. Hu et al [17] investigated a robust yaw moment control for motion stabilization to realize an accurate control of the yaw rate.…”

Section: Introductionmentioning

confidence: 99%

Continuous Steering Stability Control Based on an Energy-Saving Torque Distribution Algorithm for a Four in-Wheel-Motor Independent-Drive Electric Vehicle

Zhai

Hou

Sun³

et al. 2018

Energies

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Abstract:In this paper, a continuous steering stability controller based on an energy-saving torque distribution algorithm is proposed for a four in-wheel-motor independent-drive electric vehicle (4MIDEV) to improve the energy consumption efficiency while maintaining the stability in steering maneuvers. The controller is designed as a hierarchical structure, including the reference model level, the upper-level controller, and the lower-level controller. The upper-level controller adopts the direct yaw moment control (DYC), which is designed to work continuously during the steering maneuver to better ensure steering stability in extreme situations, rather than working only after the vehicle is judged to be unstable. An adaptive two-hierarchy energy-saving torque distribution algorithm is developed in the lower-level controller with the friction ellipse constraint as a basis for judging whether the algorithm needs to be switched, so as to achieve a more stable and energy-efficient steering operation. The proposed stability controller was validated in a co-simulation of CarSim and Matlab/Simulink. The simulation results under different steering maneuvers indicate that the proposed controller, compared with the conventional servo controller and the ordinary continuous controller, can reduce energy consumption up to 23.68% and improve the vehicle steering stability.

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“…With the development of intelligent vehicle technologies, many advanced technologies such as active braking, active suspension system (ASS), and active front steering (AFS) are applied to VDC [11][12][13][14]. ASS can isolate the vibration of vehicle body and keep the rider comfortable through reducing the sprung mass acceleration and providing adequate suspension deflection [15,16].…”

Section: Introductionmentioning

confidence: 99%

A Human-Machine-Cooperative-Driving Controller Based on AFS and DYC for Vehicle Dynamic Stability

Cheng

Liu

et al. 2017

Energies

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It is a difficult and important project to coordinate active front steering (AFS) and direct yaw moment control (DYC), which has great potential to improve vehicle dynamic stability. Moreover, the balance between driver's operation and advanced technologies' intervention is a critical problem. This paper proposes a human-machine-cooperative-driving controller (HMCDC) with a hierarchical structure for vehicle dynamic stability and it consists of a supervisor, an upper coordination layer, and two lower layers (AFS and DYC). The range of AFS additional angle is constrained, with consideration of the influence of AFS on drivers' feeling. First, in the supervisor, a nonlinear vehicle model was utilized to predict vehicle states, and the reference yaw rate, and side slip angle values were calculated. Then, the upper coordination layer decides the control object and control mode. At last, DYC and AFS calculate brake pressures and the range of active steering angle, respectively. The proposed HMCDC is evaluated by co-simulation of CarSim and MATLAB. Results show that the proposed controller could improve vehicle dynamic stability effectively for the premise of ensuring the driver's intention.

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Vehicle Lateral Dynamics Control Through AFS/DYC and Robust Gain-Scheduling Approach

Cited by 314 publications

References 33 publications

MAS-Based Distributed Coordinated Control and Optimization in Microgrid and Microgrid Clusters: A Comprehensive Overview

MAS-Based Distributed Coordinated Control and Optimization in Microgrid and Microgrid Clusters: A Comprehensive Overview

Continuous Steering Stability Control Based on an Energy-Saving Torque Distribution Algorithm for a Four in-Wheel-Motor Independent-Drive Electric Vehicle

A Human-Machine-Cooperative-Driving Controller Based on AFS and DYC for Vehicle Dynamic Stability

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