Abstract
This paper presents an adaptive inertia weight particle swarm optimization (AIWPSO) employed for solving the multiobjective weight optimization problem of LQR applied for the vehicle active suspension system (ASS). To meet the competing control objectives of ASS including the ride comfort, road handling, and suspension travel, the state feedback controller design for ASS is formulated as an optimization problem and an improved PSO is employed for finding the optimal weights of the linear-quadratic regulator (LQR). Specifically, for solving the premature convergence of the particles and imbalance between exploration and exploitation capabilities of PSO, an adaptive inertia weight that updates the velocity of the particles based on the success rate is used. The efficacy of the AIWPSO-tuned LQR is experimentally tested on a quarter-car ASS plant using the hardware in loop (HIL) testing for an uneven road surface. Experimental results highlight that, compared to conventional PSO-tuned LQR, the proposed scheme can significantly minimize the vehicle body acceleration due to irregular road profile while guaranteeing the minimum tire friction for passenger safety. The ISO 2361-1 standards adopted to evaluate the ride and health criteria substantiate that the proposed scheme reduces the vibration dose value by 25.34% for a bumpy road profile. Moreover, the cumulative power spectral density (CPSD) of vehicle body acceleration assessed in both low- and high-frequency regions manifests the significant improvement in the ride comfort.
Highlights
Active suspension systems in vehicles have gained considerable attention in both academia and industry for their potential to improve the ride comfort, road handling, and passenger safety
A servomotor placed between the top and middle plates acts as an actuator to counteract the vertical forces created due to an uneven road profile. e middle plate maintains contact with the bottom plate through a spring. e bottom plate, which resembles the wheel of the quarter car, is connected to a DC motor to generate the road excitation in the system. e rotational motion created due to the torque from the motor is translated into linear motion using the lead screw and gearing mechanism for creating different road profiles
To acquire the vehicle body acceleration relative to the ground, the top plate is attached with an accelerometer with a range of ±10 g. e power amplifiers, which drive the servomotors, can offer a regulated supply of ±10 V at 3 A. e data acquisition module (DAQ) board, which has a resolution of 12 bits, acquires signals at a sampling rate of 500 Hz
Summary
Active suspension systems in vehicles have gained considerable attention in both academia and industry for their potential to improve the ride comfort, road handling, and passenger safety. Compared to passive suspension systems, ASS provides better control performance in terms of minimizing the vibrations of the vehicle body due to road irregularities. E competing control objectives of ASS, including the ride comfort, suspension travel, and body motion, have motivated researchers to employ linear-quadratic control to realize the optimal performance without violating the constraints of the system. LQR, the cornerstone of linear-quadratic Gaussian (LQG)/loop transfer recovery (LTR), is an optimal state feedback controller which offers several advantages including robustness, guaranteed stability, and a structured procedure which can be extended to multiple-input-multiple-output (MIMO) systems. In spite of the potential benefits of LQR, one of the major fundamental design challenges with it, for real-time applications, is the optimal choice of weighting matrices
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