Multi-Objective Optimization of Nonlinear Quarter Car Suspension System – PID and LQR Control

Nagarkar, MP; Bhalerao, Yogesh; Patil, GJ Vikhe; Patil, RN Zaware

doi:10.1016/j.promfg.2018.02.061

Cited by 35 publications

(18 citation statements)

References 20 publications

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“…According to the assumption of the Gaussian distribution of stochastic roads, for linear systems, the response should have a Gaussian property and can thus be described by a normal distribution. Therefore, the suspension working space can be expressed by the probability of relative displacement of the wheel and the body [38]. The suspension working space of the front axle and rear axle are written as:…”

Section: B Objectivesmentioning

confidence: 99%

Ride Comfort Analysis and Multivariable Co-Optimization of the Commercial Vehicle Based on an Improved Nonlinear Model

Chen

Zheng

et al. 2020

IEEE Access

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The performance of a suspension system is affected by the behavior and posture of its components. However, published studies usually conduct this research using the based-equivalent model without considering the characteristic curves or postures. In this paper, an improved ride comfort model that considers three nonlinearities in suspensions is first developed, and this model is validated through experimental results and demonstrates good accuracy. Then, the dynamic response is presented to investigate the effects of multilevel suspension parameters and nonlinear factors on ride comfort, and it is concluded that the front chassis suspension is the most significant system for ride comfort. Next, a multivariable co-optimization method based on the improved model is proposed to obtain more accurate optimized results that are more suitable for automotive applications. Subsequently, a multiobjective genetic algorithm (MGA) is applied to obtain the Pareto solution set. Furthermore, comparing the RMS value before and after optimization shows an obvious reduction, with averages of 19.7%, 17.8%, and 12.0% for the weighted root mean square (RMS) of the driver seat acceleration and the RMS of the working spaces of the front chassis suspension and the rear chassis suspension, respectively. Finally, the results are also verified by experiments, indicating that the improved ride comfort model and the multivariable co-optimization method are feasible and practical.INDEX TERMS Ride comfort, nonlinearity of suspension components, multivariable co-optimization, bivariate analysis, commercial vehicle.

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Section: B Objectivesmentioning

confidence: 99%

Ride Comfort Analysis and Multivariable Co-Optimization of the Commercial Vehicle Based on an Improved Nonlinear Model

Chen

Zheng

et al. 2020

IEEE Access

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“…However, physical limitations prevent passive suspension from achieving the best performances for all targets. Other approaches (Ishak et al, 2009; Nagarkar et al, 2018; Pepe et al, 2019; Sun et al, 2014; ) used controlled suspension systems to enhance the optimization process. For active suspension systems, control methods include linear quadratic regulator (Darus and Sam, 2009), active disturbance rejection controller (ADRC) (Hasbullah et al, 2015), composite non-linear feedback (Fahezal Ismail et al, 2014), and others.…”

Section: Introductionmentioning

confidence: 99%

Managing the handling–comfort contradiction of a quarter-car system using Kalman filter

Muhammed

Gavrilov

2021

Transactions of the Institute of Measurement and Control

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This article introduces a new approach to manage the “handling–comfort” contradiction of a vehicle suspension system. The idea is based on determining a specific time constant that reflects the percentage of suspension damping. This time constant is defined using the measurement of the sprung mass acceleration and the suspension deflection. A distinction is made between the control unit design for a semi-active suspension system and the control unit design for an active suspension system. The semi-active design is based on two sensors and a Kalman filter (KF), while the active design is based on three sensors and a dual-estimation KF. For active suspension, a third sensor was added to measure the acceleration of the unsprung mass. Simulation is carried out in Simulink and Simscape environments. The results of the proposed approach were compared with the results achieved by the hybrid-hook system. Simulation results showed a better efficiency of the proposed approach in driving safety during a “comfort” situation.

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“…The gain parameters of the hybrid controller is tuned using the Grey-Wolf Optimizer (GWO) [3]. In [4] a new PID and LQR control system was proposed to improve a nonlinear quarter car suspension system.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Control Approach Based on the Combination of PID Control with LQR Optimal Control

Mohammed¹

2021

Control Based on PID Framework - The Mutual Promotion of Control and Identification for Complex Systems

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Proportional Integral Derivative (PID) is the most popular controller that is commonly used in wide industrial applications due to its simplicity to realize and performance characteristics. This technique can be successfully applied to control the behavior of single-input single-output (SISO) systems. Extending the using of PID controller for complex dynamical systems has attracted the attention of control engineers. In the last decade, hybrid control strategies are developed by researchers using conventional PID controllers with other controller techniques such as Linear Quadratic Regulator (LQR) controllers. The strategy of the hybrid controller is based on the idea that the parameters of the PID controller are calculated using gain elements of LQR optimal controller. This chapter focuses on design and simulation a hybrid LQR-PID controller used to stabilize elevation, pitch and travel axes of helicopter system. An improvement in the performance of the hybrid LQR-PID controller is achieved by using Genetic Algorithm (GA) which, is adopted to obtain best values of gain parameters for LQR-PID controller.

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Multi-Objective Optimization of Nonlinear Quarter Car Suspension System – PID and LQR Control

Cited by 35 publications

References 20 publications

Ride Comfort Analysis and Multivariable Co-Optimization of the Commercial Vehicle Based on an Improved Nonlinear Model

Ride Comfort Analysis and Multivariable Co-Optimization of the Commercial Vehicle Based on an Improved Nonlinear Model

Managing the handling–comfort contradiction of a quarter-car system using Kalman filter

A Hybrid Control Approach Based on the Combination of PID Control with LQR Optimal Control

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