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

Abstract

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. thus reduce the rollover propensity. Awad, Magdy Naeem [5] designed a hydro-pneumatic energy harvesting suspension system based on a PID-PSO (particle swarm optimization) controller, which achieved the maximum riding comfort as the peak values of the acceleration.However, past research on adjustable hydro-pneumatic suspension mainly focused on the analysis of vehicles' ride comfort or handling stability under different operating conditions separately, because it is difficult to achieve optimality in both aspects of ride comfort and handling stability due to the conflicting relationship between them. Hence, it is urgent to concentrate on the dynamic switch control strategy between the vehicle's ride comfort and handling stability to meet different requirements of working conditions.As a powerful controller, the sliding mode controller (SMC) has a great use in control systems because of its good robustness to external disturbance and fast dynamic response. Pradhan, Subarni [6] applied a composite SMC for wind power extraction in a remotely located solar PV-wind hybrid system. Biricik, Samet [7] used an SMC three-phase dynamic voltage restorer with adaptive notch filter to protect sensitive loads. Farahmandrad, Maryam [8] designed a fuzzy SMC for a cooperative robotic system with uncertainty for handling an object. Abtahi, S. Mahdi [9] suppressed chaotic vibrations in a suspension system of vehicle dynamics based on the optimal SMC. Inspired by the previous studies mentioned above, an SMC is used to control the hydro-pneumatic suspension system...

Nonlinear finite‐time control of hydroelectric systems via a novel sliding mode method

“…From Equation ( 14), it rationally obtains the finite settling time for the controlled hydroelectric power system in different scenarios, which are shown in Table 1. Based on Equation (14), the finite settling time is dependent on the initial values, thus, there are varying in different cases. While, since the fixed settling time is regardless of the initial values, thus the fixed settling time of the different cases is the same shown as in Table 1.…”

“…is satisfied, the controlled system can reach the reference within the finite settling time T x in Equation (14).…”

“…Theorem 1. Implementation of the proposed finite-time controller (10) in the system (4), as long as 𝜁 2 < 4𝜎𝜀n − 1−𝛽 2 , 𝜖 > 0, 𝜁 > 0 and 𝜎 > 0, then the system ( 4) is with the finite-time stability, and the settling time is shown in Equation (14), where V (⋅) and L(⋅) are selected Lyapunov functions.…”

“…Any controlling law designed based on a linearized representation is a compromise [13]. For this reason, state-of-the-art methods have been introduced to hydroelectric systems, for instance, sliding mode control [14][15][16][17], fuzzy control [18,19], predictive control [20,21], and so on.…”

Advance the stability of the vehicle by using the pneumatic suspension system integrated with the hydraulic actuator

“…The predictive control model for the pneumatic suspension system has also been used, the effect of which is extremely positive [31]. The sliding mode control method has also been shown in the studies of Chen et al and Zhou et al [32,33]. Besides, other control methods for the active suspension system that have been highly effective can also be applied to the pneumatic suspension system [34][35][36][37][38][39].…”