Application of Artificial Fish Swarm Algorithm in LQR Control for Active Suspension

Zhao, Weipeng; Li, Gu; Dong, Ming

doi:10.23919/ascc56756.2022.9828115

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…Additionally, it may imply the need for a more capable workforce to tune control parameters to eliminate tracking errors as much as possible, as significant tracking errors can compromise ride comfort and lead to unnecessary energy consumption. Another method that effectively maintains a balance between ride comfort and suspension deflection is the multi-objective active control approach, such as the LQR control strategy [15]. It achieves control objectives by adjusting conflicting system states, but its performance in handling the effects of nonlinear systems is unsatisfactory.…”

Section: Introductionmentioning

confidence: 99%

Dual objective nonlinear PD sliding mode control based on an adaptive reference model for an active suspension system

Yuan,

Shao,

Nan

et al. 2024

Preprint

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This article focuses on the study of nonlinear active suspension tracking control problems based on an adaptive reference model. To mitigate the drawback of compromised ride comfort caused by tracking errors while tracking the adaptive trajectory of body displacement, the paper explores a tracking controller with dual objectives. This includes a nonlinear PD controller for tracking the adaptive desired trajectory and a sliding mode controller concerning body velocity and acceleration. This approach ensures that the body displacement approximately tracks the desired trajectory while significantly improving ride comfort, additionally, this control method has the advantages of structural simplicity and insensitivity to tracking errors, implying that control parameters can be easily tuned, and control inputs can be effectively reduced. The stability of the controlled system is demonstrated through the Lyapunov stability theory, and a range for the body displacement tracking error is derived. Finally, the performance of the controller is tested on an experimental platform, with results indicating a substantial reduction of 69.22% and 54.66% in the RMS values of body acceleration under bump and random excitation, respectively.

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Section: Introductionmentioning

confidence: 99%

Dual objective nonlinear PD sliding mode control based on an adaptive reference model for an active suspension system

Yuan,

Shao,

Nan

et al. 2024

Preprint

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“…Ref. [7] used the artificial fish swarm algorithm (AFSA) to determine the weighting coefficient in the LQR controller. Ref.…”

Section: Introductionmentioning

confidence: 99%

“…Different suspension controllers have been designed which use particle swarm algorithm, fuzzy control algorithm, deep learning algorithm, etc. to adjust the parameters in the controller, which also have good control effects [6][7][8][9]. In fact, according to the "no free lunch" (NFL) theorem, for the same optimization problem, in the limited search space, different optimization algorithms can achieve different optimization effects, and no other algorithm can be better than the linear enumeration method of the search space or the pure random search algorithm [10,11].…”

Section: Introductionmentioning

confidence: 99%

Tuning Parameters of the Fractional Order PID-LQR Controller for Semi-Active Suspension

Gao,

2023

Electronics

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In order to further improve the control effect of proportion integral differential (PID) control and linear quadratic regulator (LQC) control, and improve vehicle ride comfort and enhance body stability, the 7 DOF semi-active suspension model was established, and the fractional order PIλDμ-LQR controller was designed by combining fractional order PIλDμ control theory and LQR control theory. The semi-active suspension model in this paper is more complex, and there are many parameters in the controller. The optimal weighting coefficient of 12 vehicle smoothness evaluation indicators and parameters Kp, Ki, Kd, λ and μ in the controller were founded by NSGA-II algorithm. After optimization, the optimized parameters were brought into the controller for random pavement simulation. Compared to the traditional passive suspension, fractional order PIλDμ individual control and LQR separate control, the simulation results show that the effect of fractional order PIλDμ-LQR control is very significant. The evaluation index of vehicle smoothness has been significantly improved, and the use of fractional order PIλDμ-LQR control has significantly improved the working performance of the suspension and improved the smoothness of the vehicle. At the same time, the adjusting force output of the actuator is very balanced, which inhibits the roll of the body and improves the anti-roll performance. After simulation, the excellent performance of the designed fractional PIλDμ-LQR controller was verified, and the introduced NSGA-II algorithm played an important role in the controller parameter tuning work, which shows that the fractional order PIλDμ-LQR controller and NSGA-II algorithm cooperate with each other to achieve good control effects.

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Comprehensive Review of Metaheuristic Algorithms (MAs) for Optimal Control (OCl) Improvement

Mohammed,

Karataev,

Oshiga

et al. 2024

Arch Computat Methods Eng

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Application of Artificial Fish Swarm Algorithm in LQR Control for Active Suspension

Cited by 3 publications

References 13 publications

Dual objective nonlinear PD sliding mode control based on an adaptive reference model for an active suspension system

Dual objective nonlinear PD sliding mode control based on an adaptive reference model for an active suspension system

Tuning Parameters of the Fractional Order PID-LQR Controller for Semi-Active Suspension

Comprehensive Review of Metaheuristic Algorithms (MAs) for Optimal Control (OCl) Improvement

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