Modeling, analysis and PID controller implementation on suspension system for quarter vehicle model

In order to further improve the stability and comfort of automobile active suspension, a BP neural network controller based on Q-learning algorithm optimization (QBP-PID) is proposed. QBP-PID uses BP neural network to adjust the PID gain, introduces the optimal strategy of Q-learning to correct the weight momentum factor, and optimizes the key weights in the neural network, so that the controller has better learning ability and online correction ability. A quarter suspension simulation model with random road excitation as the system input is established in Simulink software. The root mean square of body acceleration and tire dynamic displacement are used as the evaluation indexes of active suspension performance. The simulation results show that compared with the traditional passive suspension, PID control suspension and BP-PID control suspension, the active suspension using QBP-PID control algorithm can significantly improve the driving stability and comfort of the vehicle.

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Energy Dissipation Characteristics Analysis of Automotive Vibration PID Control Based on Adaptive Differential Evolution Algorithm

Jie,

Dou,

Zhao

et al. 2024

SAE Technical Paper Series

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<div class="section abstract"><div class="htmlview paragraph">To address the issue of PID control for automotive vibration, this paper supplements and develops the evaluation of automotive vibration characteristics, and proposes a vibration response quantity for evaluating the energy dissipation characteristics of automotive vibration. A two-degree-of-freedom single wheel model for automotive vibration control is established, and the conventional vibration response variables for ride comfort evaluation and the energy consumption vibration response variables for energy dissipation characteristics evaluation are determined. This paper uses the Adaptive Differential Evolution (ADE) algorithm to tune the PID control parameters and introduces an adaptive mutation factor to improve the algorithm's adaptability. Several commonly used adaptive mutation factors are summarized in this paper, and their effects on algorithm improvement are compared. Design a simulation test plan for commonly used B-class road surfaces and a common speed of 60 km/h under urban driving conditions. To demonstrate the ADE-PID control effect of the ADE algorithm tuning, the ADE algorithm is compared with manually tuned PID control and passive suspension under the same simulation conditions. The results show that the adaptive differential evolution algorithm can effectively improve the tuning efficiency of PID control parameters; PID control can effectively improve the vehicle ride comfort, but it makes the vibration energy dissipation characteristics worse; the ADE-PID control proposed in this paper can improve the conventional ride comfort of vehicles and reduce the negative effects on vibration energy dissipation characteristics; the energy dissipation vibration response as a supplement to conventional vibration response is beneficial for expanding the research and application scope of automotive vibration and its control in the past.</div></div>

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Modeling, analysis and PID controller implementation on suspension system for quarter vehicle model

Cited by 3 publications

References 26 publications

Application of a Sliding Mode Control Solution to Control the Active Suspension System Equipped with Hydraulic Actuator

Application of a Sliding Mode Control Solution to Control the Active Suspension System Equipped with Hydraulic Actuator

Simulation analysis of quarter car active suspension control based on QBP-PID

Energy Dissipation Characteristics Analysis of Automotive Vibration PID Control Based on Adaptive Differential Evolution Algorithm

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