Design and Implementation of H_∞ Miscellaneous Information Feedback Control for Vehicle Suspension System

Abstract

In this paper, we investigate an H∞ miscellaneous information feedback controller (HMIFC) for active suspension system stabilization. An auxiliary feedback signal is introduced in the control channel via a wireless communication network. Under this scheme, the onboard measured signal and network transmission signal are simultaneously used to improve the control performance. To handle the impact of network-induced delays, a delay-dependent stability criterion for the closed-loop system is derived based on Lyapunov theory and the linear matrix inequality (LMI) method. By considering an augmented Lyapunov–Krasovskii functional (LKF) and a relaxed integral inequality, a new existence condition of the miscellaneous feedback controller can be obtained. Compared with the traditional H∞ controller, the designed controller can acquire better ride comfort subject to the same constraints. Finally, comparative simulation and experimental results prove the effectiveness of the presented H∞ miscellaneous information feedback controller.

Backstepping LQG controller design for stabilizing and trajectory tracking of vehicle suspension system

“…Table 3 presents a comparative analysis with respect to settling time (s), peak overshoot (m), peak undershoot (m), noise (%), and steady state error e ss . The effect of road impact in the oscillation of the suspension system applying different control approaches such as PID, fuzzy, NF, LQR, H∞ and SM is also presented in Table 3 based on the references [6,11,17,18,20,23] respectively. Similar working conditions are followed with the same level of actuator and sensor noise in all control techniques application for comparison.…”

“…However, the optimal gain parameter setting, a lesser range of robust control and need of change of gain setting with varying conditions are the major limitations to limit the real time application of these controllers. Among other projected robust control algorithms applied for limiting the oscillation and velocity of the VS system are fuzzy-logic control [8][9][10][11][12][13], fuzzy-PID control [14], genetic algorithm [15], neural network [16], neuro-fuzzy (NF) control [17], linear quadratic regulator (LQR) [18], H-infinity (H∞) control [19,20], and sliding mode (SM) control [21][22][23]. However, even if these control techniques are implemented effectively by absorbing the shocks due to the rough and bumpy road in case of VS system with enhanced accuracy and damping of oscillation, still fail to handle various constraints and random change found in a suspension environment.…”

Control parameter optimization of a nonlinear vehicle suspension system with time-delayed acceleration feedback

“…where p 1 , q 1 , p 2 , and q 2 are unsolved coefficients. Substituting equations 3and 4into equations (1) and (2) and then extracting the coefficients of sin xt ð Þ and cos xt ð Þ from both sides of the equations, the nonlinear algebraic equations about p 1 , q 1 , p 2 , and q 2 are obtained…”

“…Hence, a great deal of effort has been devoted to the studies of active and semi-active suspensions in recent years. [1][2][3][4][5][6] Various control strategies are presented to improve the performance of the suspension such as sliding mode control, fuzzy control, H 1 control and so on.…”

Effect of time-delay feedback control on vehicle seat vibration reduction and suspension performance

“…4 The conventional passive suspension consists of elastic and damping elements with fixed parameters, which leads to a contradiction between comfort and stability in the vehicle system. 5,6 Therefore, numerous types of active vehicle suspensions with excellent performance have been proposed, such as adaptive control, [7][8][9] sliding-mode control, 10,11 and H ∞ control. 12 Although the performance of the active vehicle suspension is excellent, a high cost is required.…”