Vibration Control of Vehicle by Active Suspension with LQG Algorithm

Abstract

In this paper, the ride performance of a vehicle with active suspension and Linear Quadratic Gaussian (LQG) controller has been studied and is compared to the performances of a traditional passive suspension system. The study includes variables that are related to a passenger’s comfort: vertical position, vertical velocity, pitch angle, pitch velocity, roll angle, and roll velocity. The performances of the two systems are evaluated by maximum values and root mean square (RMS) of the variables when riding on a sinusoidal road profile. The simulation results show that the vehicle with active suspension and LQG controller performs better than passive suspension system where the maximum values decrease by 85.77%, 92.73%, 50.31% 86.83%, 89.41%, 43.28%, and RMS values decrease by 88.59%, 92.36%, 42.99%, 87.61%, 90.85%, and 42.79% for vertical position, vertical velocity, pitch angle, pitch velocity, roll angle, and roll velocity, respectively.

Advance the Efficiency of an Active Suspension System by the Sliding Mode Control Algorithm With Five State Variables

“…It is called the LQG controller [16]. This algorithm provides higher stability with noise signals removed [17][18][19][20]. Besides, the active suspension control model using the LPV algorithm was also performed by Rodriguez-Guevara et al According to them, this algorithm uses continuously changing parameters to fit the selected model [21].…”

Fractional-Order PIλDμ Control to Enhance the Driving Smoothness of Active Vehicle Suspension in Electric Vehicles

“…Yin et al [44] recorded an optimization of 21.37% for AVB 3 (k). Zeng et al [81] achieved an optimization of 45.2% for AVB 3 (k), while Gomonwattanapanich et al [83] achieved an optimization of 50.31%. Gong et al [84] utilized multi-sensor information fusion technology to optimize AVB 3 (k) by 26.96%.…”

“…Yin et al [44] achieved a 24.17% optimization of AVB 4 (k) using the fuzzy PID control method. Gomonwattanapanich et al [83] achieved a higher AVB 4 (k) optimization through the linear-quadratic Gaussian method, reaching 89.41%. Gong et al [84] achieved a 38.90% optimization of AVB 4 (k).…”

Performance analysis of fuzzy-LQR and fuzzy-LQG controllers for active vehicle suspension systems

“…The LQR approach computes an optimal state-feedback gain by minimizing a quadratic performance index, which consists of the state and input variables penalized by the weighting matrices. Linear Quadratic Gaussian (LQG) control [13] combines optimal control theory with state estimation techniques to design controllers that minimize a quadratic cost function while accounting for uncertainties and noise. Fuzzy logic can capture the complex and nonlinear relationships inherent in suspension dynamics.…”