Application of Constrained H∞ Control to Active Suspension Systems on Half-Car Models

Abstract

This paper formulates the active suspension control problem as disturbance attenuation problem with output and control constraints. The H∞ performance is used to measure ride comfort such that more general road disturbances can be considered, while time-domain hard constraints are captured using the concept of reachable sets and state-space ellipsoids. Hence, conflicting requirements are specified separately and handled in a nature way. In the framework of Linear Matrix Inequality (LMI) optimization, constrained H∞ active suspensions are designed on half-car models with and without considering actuator dynamics. Analysis and simulation results show a promising improvement on ride comfort, while keeping suspension strokes and control inputs within bounds and ensuring a firm contact of wheels to road.

Proportional-integral-derivative control of nonlinear half-car electro-hydraulic suspension systems

“…Optimal frequency response plots were obtained by using the "Hamming" window setting, a segment length of N/100 (where N is the total number of samples) and a percentage overlap of 2 14 (The MathWorks, Inc., 2001). Frequency responses are plotted for Δ=±30% variation in the linear, nonlinear, and asymmetric damping coefficients in Section 5.1 (Chen et al, 2005). The time domain response plots for the AVSS and PVSS traversing over a sinusoidal bump road input disturbance are given in Section 5.2 (Chen et al, 2005).…”

“…Frequency responses are plotted for Δ=±30% variation in the linear, nonlinear, and asymmetric damping coefficients in Section 5.1 (Chen et al, 2005). The time domain response plots for the AVSS and PVSS traversing over a sinusoidal bump road input disturbance are given in Section 5.2 (Chen et al, 2005).…”

“…Hence, the need for a controller design which takes into account parametric uncertainty within acceptable bounds (Chen et al, 2005;Gao et al, 2006;Ryu et al, 2008;Du and Zhang, 2010).…”

“…These include optimal control (Hassanzadeh et al, 2010), H 2 (Pedro, 2007), H ∞ (Chen et al, 2005;Du and Zhang, 2007;Ryu et al, 2008), H 2 /H ∞ (Akcay and Turkay, 2009), linear parameter varying (LPV) (Fialho and Balas, 2002;Szaszi et al, 2002), sliding mode control (SMC) (Yoshimura et al, 2001), fuzzy logic control (FLC) (Du and Zhang, 2009a), backstepping control (Yagiz and Hacioglu, 2008), feedback linearization (FBL) (Chien et al, 2008;Fateh and Alavi, 2009), and various neural network (NN)-based control methods (Buckner et al, 2000;Dahunsi et al, 2009;Pedro and Dahunsi, 2011).…”

Comparing Pid And Fuzzy Logic Control A Quarter-car Suspension System

“…The performance of the fuzzy logic active suspension was compared to a passive suspension and to a benchmark active suspension. A large number of different arrangements from semi active to fully active schemes have been investigated [5,6,7,8,9] In[10 , a complete set of constraints was derived on the road and load disturbance response transfer functions and results on the choice of sensors needed to achieve these degrees of freedom independently were obtained for the quarter car model The generalization of these results to half and full car models was then presented in [11] In [12], it was shown that the road and load disturbance responses cannot be adjusted independently for any passive suspension applied to a quarter car model In this paper, an automatic suspension system for a quarter car is considered and a fuzzy logic controller is designed when the vehicle is experiencing any road disturbance, the vehicle body should not have large oscillations, and the oscillations should dissipate quickly The road disturbance is simulated by a step input as a soft road test and rough road as a simulated to real way and the distance between the body mass and simulation mass is output of the system…”