scholarly journals Sliding Mode Switch Control of Adjustable Hydro-Pneumatic Suspension based on Parallel Adaptive Clonal Selection Algorithm

2020 ◽  
Vol 10 (5) ◽  
pp. 1852
Author(s):  
Chen Zhou ◽  
Xinhui Liu ◽  
Feixiang Xu ◽  
Wei Chen
Keyword(s):  
Control Strategy ◽  
Ride Comfort ◽  
Sliding Mode ◽  
Clonal Selection ◽  
Parameters Optimization ◽  
Driving Safety ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm ◽  
Switch Control ◽  
Pneumatic Suspension

The hydro-pneumatic suspension, as a widely used suspension for heavy vehicles, has been taken seriously by researchers for a long time because it is crucial in terms of handling stability, riding comfort, and driving safety of these vehicles. Most previous studies only discussed the control of ride comfort or vehicle handling stability of the suspension system separately. This article proposes a dynamic switch control strategy which can switch between ride comfort and handling stability controllers under different road surfaces and driving conditions. The load transfer ratio (LTR) is selected as the switch performance index, and it is calculated through a six-degrees-of-freedom (6-DOF) model. The ride comfort and handling stability controller of the hydro-pneumatic suspension are designed based on the sliding mode control theory. The objective functions of parameters optimization of the sliding mode controller (SMC) are obtained by means of analytic hierarchy process (AHP), and then the controller’s parameters are optimized by the parallel adaptive clonal selection algorithm (PACSA). The simulation results based on MATLAB/Simulink show that: (1) the PACSA performs better than a genetic algorithm in terms of the parameters optimization of the SMC; (2) the proposed switch control strategy can simultaneously improve the ride comfort and handling stability under several typical steering maneuvers and various road profiles compared with the conventional SMC-controlled suspension.

scholarly journals An Optimal Longitudinal Control Strategy of Platoons Using Improved Particle Swarm Optimization

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Zhizhou Wu ◽  
Zhibo Gao ◽  
Wei Hao ◽  
Jiaqi Ma
Keyword(s):  
Particle Swarm Optimization ◽  
Control Strategy ◽  
Ride Comfort ◽  
Particle Swarm ◽  
Experimental Simulation ◽  
Driving Safety ◽  
Relative Speed ◽  
Longitudinal Control ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

Most existing longitudinal control strategies for connected and automated vehicles (CAVs) have unclear adaptability without scientific analysis regarding the key parameters of the control algorithm. This paper presents an optimal longitudinal control strategy for a homogeneous CAV platoon. First of all, the CAV platoon models with constant time-headway gap strategy and constant spacing gap strategy were, respectively, established based on the third-order linear vehicle dynamics model. Then, a linear-quadratic optimal controller was designed considering the perspectives of driving safety, efficiency, and ride comfort with three performance indicators including vehicle gap error, relative speed, and desired acceleration. An improved particle swarm optimization algorithm was used to optimize the weighting coefficients for the controller state and control variables. Based on the Matlab/Simulink experimental simulation, the analysis results show that the proposed strategy can significantly reduce the gap error and relative speed and improve the flexibility and initiative of the platoon control strategy compared with the unoptimized strategies. Sensitivity analysis was provided for communication lag and actuator lag in order to prove the applicability and effectiveness of this proposed strategy, which will achieve better distribution of system performance.

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