A New Optimization Control Policy for Fuzzy Vehicle Suspension Systems Under Membership Functions Online Learning

Zhang, Zhenxing; Dong, Jie

doi:10.1109/tsmc.2022.3224739

Cited by 8 publications

(3 citation statements)

References 48 publications

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“…At present, in the research of active suspension control, the 1/4 vehicle model, the 1/2 vehicle model, and the whole vehicle model are the research objects [ 24 , 25 , 26 ]. Using the two-degrees-of-freedom model as the object of study can better reflect the problem of vertical vibration but it ignores the mutual coupling between the suspensions and the influence of the angular motion of the body in the pitch and roll directions on the comfort, and the control requirements for vehicle comfort cannot be fully described.…”

Section: Seven-dof Vehicle Modelmentioning

confidence: 99%

Fast Distributed Model Predictive Control Method for Active Suspension Systems

Zhang

Yang

et al. 2023

Sensors

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In order to balance the performance index and computational efficiency of the active suspension control system, this paper offers a fast distributed model predictive control (DMPC) method based on multi-agents for the active suspension system. Firstly, a seven-degrees-of-freedom model of the vehicle is created. This study establishes a reduced-dimension vehicle model based on graph theory in accordance with its network topology and mutual coupling constraints. Then, for engineering applications, a multi-agent-based distributed model predictive control method of an active suspension system is presented. The partial differential equation of rolling optimization is solved by a radical basis function (RBF) neural network. It improves the computational efficiency of the algorithm on the premise of satisfying multi-objective optimization. Finally, the joint simulation of CarSim and Matlab/Simulink shows that the control system can greatly minimize the vertical acceleration, pitch acceleration, and roll acceleration of the vehicle body. In particular, under the steering condition, it can take into account the safety, comfort, and handling stability of the vehicle at the same time.

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Section: Seven-dof Vehicle Modelmentioning

confidence: 99%

Fast Distributed Model Predictive Control Method for Active Suspension Systems

Zhang

Yang

et al. 2023

Sensors

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“…However, the controller structure is often complex. The FLC is widely researched, and applied in active damping systems [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Optimal FLC-Sugeno Controller based on PSO for an Active Damping System

Nguyen,

Deng,

Nguyen

2024

Eng. Technol. Appl. Sci. Res.

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In this paper, a method for the design of an optimal Sugeno model (FLC-Sugeno) fuzzy logic controller for the active suspension system of quarter-vehicle models is presented. The parameters of the FLC-Sugeno controller are optimally searched, using the Particle Swarm Optimization (PSO) algorithm. The 16 optimized parameters include 3 parameters for adjusting the domain of the input state variables and control variables at the controller’s output, 4 fuzzy set adjustment numbers of the linguistic variables, and 9 parameters as the fuzzy rule weights of the rule system control. To compare and evaluate the effectiveness of the optimal FLC-Sugeno controller, an optimal PID controller using PSO is also implemented. Simulation results of the active damping system with the controllers when affected by the same type and standard road surface excitation show that the FLC-Sugeno controller is optimal for the quick ending of the oscillations of vehicle body displacement. The result shows that the proposed controlling scheme can be extended and applied to more complex active damping system models.

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“…Based on Lyapunov theory and backstepping technique, Wang [ 25 ] studied the adaptive control problem of nonlinear active suspension systems with random perturbations and time delay. Moreover, some scholars have considered the time-delay characteristics of active suspension systems and controlled them according to the Takagi–Sugeno fuzzy model [ 26 , 27 , 28 ]. Further, scholars have been searching for more robust control methods to ensure the stability and performance of the suspension system [ 29 , 30 , 31 ].…”

Section: Introductionmentioning

confidence: 99%

Research on Deep Reinforcement Learning Control Algorithm for Active Suspension Considering Uncertain Time Delay

Wang,

Zhao

et al. 2023

Sensors

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The uncertain delay characteristic of actuators is a critical factor that affects the control effectiveness of the active suspension system. Therefore, it is crucial to develop a control algorithm that takes into account this uncertain delay in order to ensure stable control performance. This study presents a novel active suspension control algorithm based on deep reinforcement learning (DRL) that specifically addresses the issue of uncertain delay. In this approach, a twin-delayed deep deterministic policy gradient (TD3) algorithm with system delay is employed to obtain the optimal control policy by iteratively solving the dynamic model of the active suspension system, considering the delay. Furthermore, three different operating conditions were designed for simulation to evaluate the control performance: deterministic delay, semi-regular delay, and uncertain delay. The experimental results demonstrate that the proposed algorithm achieves excellent control performance under various operating conditions. Compared to passive suspension, the optimization of body vertical acceleration is improved by more than 30%, and the proposed algorithm effectively mitigates body vibration in the low frequency range. It consistently maintains a more than 30% improvement in ride comfort optimization even under the most severe operating conditions and at different speeds, demonstrating the algorithm’s potential for practical application.

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A New Optimization Control Policy for Fuzzy Vehicle Suspension Systems Under Membership Functions Online Learning

Cited by 8 publications

References 48 publications

Fast Distributed Model Predictive Control Method for Active Suspension Systems

Fast Distributed Model Predictive Control Method for Active Suspension Systems

Optimal FLC-Sugeno Controller based on PSO for an Active Damping System

Research on Deep Reinforcement Learning Control Algorithm for Active Suspension Considering Uncertain Time Delay

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