An ACC Design Method for Achieving Both String Stability and Ride Comfort

Yamamura, Yoshinori; Seto, Yoji; Nishira, Hikaru; Kawabe, Taketoshi

doi:10.1299/jsdd.2.979

Cited by 20 publications

(18 citation statements)

References 4 publications

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“…In order to address the combined longitudinal and lateral control, the state-space representation of the IV dynamic system is adopted to build the multiple-input multiple-output (MIMO) control models [20][21][22][23][24] . However, the implementation of these MIMO controllers (e.g., prediction control [20] or H ∞ control [25] ) often imposes offline computation and strict constraints in both simulations and experiments. For example, when forming the Hamilton function in the H ∞ control, the feedback factor is often formulated by using Riccati equation or algebraic iteration [25] , which increase the offline computation.…”

Section: Energy Dissipation Based Longitudinal and Lateral Couplingmentioning

confidence: 99%

“…However, the implementation of these MIMO controllers (e.g., prediction control [20] or H ∞ control [25] ) often imposes offline computation and strict constraints in both simulations and experiments. For example, when forming the Hamilton function in the H ∞ control, the feedback factor is often formulated by using Riccati equation or algebraic iteration [25] , which increase the offline computation. In addition, the control inputs need to be weighted for the MIMO controllers and the convergence of iterative approximate solution is not always guaranteed.…”

Section: Energy Dissipation Based Longitudinal and Lateral Couplingmentioning

confidence: 99%

See 1 more Smart Citation

Energy Dissipation Based Longitudinal and Lateral Coupling Control for Intelligent Vehicles

Zhang

et al. 2018

IEEE Intell. Transport. Syst. Mag.

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This paper proposes a combined longitudinal and lateral control approach for an intelligent vehicle system based on energy dissipation. The vehicle system dynamics resembles a series of mass/spring/damper systems that are dissipative, i.e., the energy of the system decays to zero eventually. Thus, the nonlinear-optimal longitudinal and lateral coupling control problem of the intelligent vehicle system is transformed into a dissipative control design based on an energy storage function. To satisfy the γ-performance, with respect to the quadratic supply rate, the storage function is developed by using a back-stepping based Lyapunov method and a step-by-step improvement of performance bounds. A dissipative feedback control law is formulated by successive approximation based on the step-by-step reduction of the value of γ. The results of the adaptive vehicle control simulations and test-bed experiments are provided and verified by the respective comparison of energy consumption on different values of γ and speed adaption under different road geometries.

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Section: Energy Dissipation Based Longitudinal and Lateral Couplingmentioning

confidence: 99%

Section: Energy Dissipation Based Longitudinal and Lateral Couplingmentioning

confidence: 99%

Energy Dissipation Based Longitudinal and Lateral Coupling Control for Intelligent Vehicles

Zhang

et al. 2018

IEEE Intell. Transport. Syst. Mag.

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“…In the previous researches, there has been a large collection of papers on ACC systems aimed at highways [1][2][3][4][5], but only a few researches have paid attention to the Stop & Go system used for the city traffic condition [6][7][8][9]. The research on combining the ACC system with the Stop & Go systems has not been extensively discussed until recently [10].…”

Section: Introductionmentioning

confidence: 99%

Research on the Intelligent Control and Simulation of Automobile Cruise System Based on Fuzzy System

Chen

Zhang

Liu

2016

Mathematical Problems in Engineering

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In order to improve the active safety driving vehicle and alleviate the intension of driving fatigue, an intelligent control strategy of automobile cruise is put forward based on the throttle or braking pedal combined control adopting the fuzzy control theory. A fuzzy logic controller is presented, which consists of the two input variables, the deviation of the theoretical safe distance and relative distance and the relative velocity between the preceding vehicle and the cruise vehicle, and the single output variable, that is, the throttle opening or the braking pedal travel. Taking the test data of 1.6 L vehicle with auto-transmission as an example, the function on the intelligent cruise control system is simulated adopting MATLAB/Simulink aiming at different working conditions on the city road. The simulation results show that the control strategy possesses integrated capability of automated Stop & Go control, actively following the preceding vehicle on the conditions of keeping the safety distance and the constant velocity cruise. The research results can offer the theory and technology reference for setting dSPACE type and developing the integrated control product of automobile cruise system.

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“…This method is implemented by neglecting the powertrain dynamics. For the uncertainties, the majority rely on robust control techniques, including sliding model control (SMC) [19], H ∞ control [20,21], adaptive control [22][23][24], fuzzy control [25,26], etc. Considering parametric variations, an adaptive SMC was designed by Swaroop et al [19] by adding an on-line estimator for vehicle parameters, such as mass, aerodynamic drag coefficient and rolling resistance.…”

Section: Introductionmentioning

confidence: 99%

“…Considering parametric variations, an adaptive SMC was designed by Swaroop et al [19] by adding an on-line estimator for vehicle parameters, such as mass, aerodynamic drag coefficient and rolling resistance. Higashimata and Adachi [20] and Yamamura and Seto [21] designed a Model Matching Controller (MMC) based controller for headway control. This design used an H ∞ controller as feedback and a forward compensator for a faster response.…”

Section: Introductionmentioning

confidence: 99%

Robust Accelerating Control for Consistent Node Dynamics in a Platoon of CAVs

Gao¹,

Li²,

Li³

2016

Autonomous Vehicle

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Driving as a platoon has potential to significantly benefit traffic capacity and safety. To generate more identical dynamics of nodes for a platoon of automated connected vehicles (CAVs), this chapter presents a robust acceleration controller using a multiple model control structure. The large uncertainties of node dynamics are divided into small ones using multiple uncertain models, and accordingly multiple robust controllers are designed. According to the errors between current node and multiple models, a scheduling logic is proposed, which automatically selects the most appropriate candidate controller into loop. Even under relatively large plant uncertainties, this method can offer consistent and approximately linear dynamics, which simplifies the synthesis of upper level platoon controller. This method is validated by comparative simulations with a sliding model controller and a fixed H ∞ controller.

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An ACC Design Method for Achieving Both String Stability and Ride Comfort

Cited by 20 publications

References 4 publications

Energy Dissipation Based Longitudinal and Lateral Coupling Control for Intelligent Vehicles

Energy Dissipation Based Longitudinal and Lateral Coupling Control for Intelligent Vehicles

Research on the Intelligent Control and Simulation of Automobile Cruise System Based on Fuzzy System

Robust Accelerating Control for Consistent Node Dynamics in a Platoon of CAVs

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