Ride Comfort Optimization via Speed Planning and Preview Semi-Active Suspension Control for Autonomous Vehicles on Uneven Roads

Wu, Jian; Zhou, Hongliang; Liu, Zhiyuan; Gu, Ming

doi:10.1109/tvt.2020.2996681

Cited by 59 publications

(45 citation statements)

References 45 publications

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“…24~Fig. 27 shows that the convergence rate with our proposed optimal controller using the PSO algorithm is larger than the conventional fuzzy controller.…”

Section: Comparative Examples To Existing Approachesmentioning

confidence: 91%

“…For the control of nonlinear system, some performances including the reduction of the disturbance, input command reduction and stability should be simultaneously solved [26]. To address the compromise between these performances, some effective control methods, such as model predictive control [27], deep reinforcement learning [28], multiobjective control [29], backstepping control [30] and preview control [31], have been applied for nonlinear systems. However, in the aforementioned researches, a serious common requirement is that all systems should be approximated to be linear model by using locally Jacobian linearized approach.…”

Section: Introductionmentioning

confidence: 99%

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Feedback Linearized Optimal Control Design for Quadrotor With Multi-Performances

Chen

2021

IEEE Access

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Design of tracking controller for quadrotor is an important issue for many engineering fields such as COVID-19 epidemic prevention, intelligent agriculture, military photography and rescue nowadays. This study applies the feedback linearized method and linear quadratic regulator (LQR) method using particle swarm optimization (PSO) to analysis and stabilize the highly nonlinear quadrotor system without applying any nonlinear function approximator that includes neural network approach and fuzzy approach. The article proposes a new method based on the firstly proposed convergence rate formula to achieve the optimal weighting matrices of LQR such that the composite controller can reduce the amplitudes of system control inputs. Determination of the LQR tuning parameters is conventionally achieved via trial and error approach. In addition to being very troublesome, it is difficult to find the globally best tuning matrices with LQR method. This article firstly uses the convergence rate formula of the nonlinear system as the fitness function of LQR approach by using PSO to take the place of the trial and error method. The generalities and implications of proposed approach are globally valid, whereas the Jacobian linearized approach is locally valid due to the Taylor expansion theorem. In addition to these two major achievements, the significant innovation of the proposed method is to possess "simultaneously" additional performances including the almost disturbance decoupling, input amplitude reduction, tuning parameter optimization and globally exponential stability performances. Comparative examples show that the convergence rate with our proposed optimal controller using the PSO algorithm is larger than the fuzzy method, and better than the singular perturbation method with high-gain feedback.INDEX TERMS Almost disturbance decoupling, COVID-19 epidemic prevention , feedback linearized approach, linear quadratic regulator, particle swarm optimization, quadrotor.

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“…24~Fig. 27 shows that the convergence rate with our proposed optimal controller using the PSO algorithm is larger than the conventional fuzzy controller.…”

Section: Comparative Examples To Existing Approachesmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Feedback Linearized Optimal Control Design for Quadrotor With Multi-Performances

Chen

2021

IEEE Access

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“…Now we will design an input to track the desired tracking signals for the full-car system and represses the realistic bump road disturbance's effect, that is z r1 = z r2 = 0.01 sin 30 t , 0.1 ≤ t ≤ 0.35 ( for front tire) (169) and z r3 = z r4 = 0.01 sin 30 t , 1.1 ≤ t ≤ 1.35 ( for rear tire) (170) Let's appropriately select 1 1 = 2 1 = 3 1 = 4 1 = 0.06 to meet that A i c matrices are Hurwitze. Using the Matlab software yields…”

Section: Global Optimization Controller Design Using Pso For Full-car Active Driving Suspension Systemmentioning

confidence: 99%

“…© 2021 The Authors. IET Control Theory & Applications published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology matically adjust the driving behaviour according to the comfort requirement [1]. Motivated by this issue and based on the road information obtained from the network and the front view sensors, this article is dedicated to finding a driving control strategy that coordinates speed and suspension based on feedback linearized approach and linear quadratic regulator optimization control approach using particle swarm optimization.…”

Section: Introductionmentioning

confidence: 99%

Global optimization control for nonlinear full‐car active suspension system with multi‐performances

Chen

2021

IET Control Theory & Appl

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In this article, based on feedback linearized approach and linear quadratic regulator optimization control approach using particle swarm optimization, the almost disturbance decoupling, input amplitude reduction, tuning parameter optimization, controllable convergence rate, improved suspension and globally exponential stability multi-performances of highly nonlinear multi-input multi-output full-car uncertain system are simultaneously achieved without applying any nonlinear function approximator including neural network approach and fuzzy logic approach. The article proposes a new method to achieve the optimal control matrices of LQR such that the composite controller can reduce the amplitudes of system control inputs. Determination of the LQR tuning parameters is conventionally determined via trial and error approach. In addition to being very troublesome, it is difficult to find the globally best tuning matrices with LQR method. This study has first proposed the convergence rate formula of the nonlinear system response as the fitness function of LQR approach by using PSO optimization algorithm to take the place of the conventional trial and error method and locally Jacobian linearized approach under the guarantee of globally exponential stability. The simulation results show how the full-car system can be controlled optimally and satisfactorily and confirm the superiority of the proposed method.

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“…Therefore, the semi-active suspension control in this study is founded on the Linear Parameter Varying (LPV) framework, which is available for the configuration by changing the scheduling variable [10][11][12]. According to the detailed literature review [13][14][15][16][17][18], they do not consider the integration of velocity design, oncoming road conditions and the trade-off between vehicle safety and driving comfort simultaneously. This study introduces a new method where the integration of look-ahead road data in the control of the adaptive semi-active suspension.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Semi-Active Suspension and Cruise Control through LPV Technique

et al. 2020

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Several studies exist on topics of semi-active suspension and vehicle cruise control systems in the literature, while many of them just consider actual road distortions and terrain characteristics, these systems are not adaptive and their subsystems designed separately. This study introduces a new method where the integration of look-ahead road data in the control of the adaptive semi-active suspension, where it is possible to the trade-off between comfort and stability orientation. This trade-off is designed by the decision layer, where the controller is modified based on prehistorical passive suspension simulations, vehicle velocity and road data, while the behavior of the controller can be modified by the use of a dedicated scheduling variable. The adaptive semi-active suspension control is designed by using Linear Parameter Varying (LPV) framework. In addition to this, it proposes designing the vehicle velocity for the cruise controller by considering energy efficiency and comfort together. TruckSim environment is used to validate the operation of the proposed integrated cruise and semi-active suspension control system.

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Ride Comfort Optimization via Speed Planning and Preview Semi-Active Suspension Control for Autonomous Vehicles on Uneven Roads

Cited by 59 publications

References 45 publications

Feedback Linearized Optimal Control Design for Quadrotor With Multi-Performances

Feedback Linearized Optimal Control Design for Quadrotor With Multi-Performances

Global optimization control for nonlinear full‐car active suspension system with multi‐performances

Adaptive Semi-Active Suspension and Cruise Control through LPV Technique

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