Comparative Performance Analysis of Active and Semi-active Suspensions with Road Preview Control

Cvok, Ivan; Deur, Joško; Tseng, H. Eric; Hrovat, Davor

doi:10.1007/978-3-030-38077-9_206

Cited by 7 publications

(9 citation statements)

References 11 publications

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“…To improve ride comfort and the driver's ability to perform specific tasks, a systematic comparative analysis of various active suspension types and associated controls is necessary. At the same time, the corresponding control system design tools should be developed for specialized laboratory tests (Cvok et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

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A comfort evaluation method based on an intelligent car cockpit

Yang

Chen

Xing

et al. 2022

Hum Ftrs & Erg Mfg Svc

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With the rapid development of automobiles, car cockpits are becoming more and more intelligent and advanced, and the intelligent requirements of automobile cockpits are gradually increasing. However, the real value of intelligence can only be realized when it makes passengers in a cockpit feel comfortable. In this study, seven factors that affect passenger comfort in intelligent cockpits are defined. Under these factors, a total of 33 evaluation indicators were developed. The core of the method was to determine the dissatisfaction indicators and degree of dissatisfaction in the intelligent cockpit by analyzing the relationship between people's perceived performance and their expectations. This method was used to evaluate the Tesla Model 3, and it was found in the results that the higher the degree of dissatisfaction with the indicator, the more subjective feedback it had, which in turn proved the effectiveness of the model. According to the degree of dissatisfaction, the indicators affecting comfort were also divided into three levels. This hierarchical division helps clarify which indicators should be prioritized for improvement. Generally, this method has a certain feasibility, which is helpful for the development and redesign of an intelligent car cockpit, and provides some reference strategies for other transportation fields.comfort, evaluation method, expectation, intelligent cockpit, the degree of dissatisfactionWith the rapid development of technology, the number of intelligent cars has also increased in recent years (Arena et al., 2020). Related research describes the intelligent car of the future as not only improving traffic safety like a robot but also further meeting people's demands by connecting to the Internet (Huang et al., 2016;James, 2012). Intelligent vehicles have become a trend now. Companies such as Tesla, Jaguar, and Google are now launching more intelligent vehicles from innovative materials and innovative technologies (Sun et al., 2021).However, for the vast majority of consumers, it is not the technology itself that cares about them, but how they feel in the cockpit (Park et al., 2019). The benefits of intelligent cars include increased safety, reduced need for infrastructure investment, improved fuel economy, and reduced congestion. But most importantly, they make passengers more relaxed and comfortable (Jorlöv et al., 2017). The real benefits of intelligent cars can only be realized when the human driver is comfortable in the intelligent cockpit and the interaction between the driver and the automated system is at a reasonable level (Helldin et al., 2013). Beggiato et al. (2019 believe that comfort plays an important role in the wide acceptance of intelligent vehicles. For some products such as smart cars, consumer satisfaction is a factor for business success (Park et al., 2019;Salomo et al., 2003). Industry and research are working to develop systems that can automatically adapt to maximize well-being (Olugbade et al., 2021). Therefore, making people feel comfortable is

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Section: Discussionmentioning

confidence: 99%

“…At the same time, the corresponding control system design tools should be developed for specialized laboratory tests (Cvok et al, 2019).…”

Section: Degree Of Dissatisfaction and Open-ended Questionnaire Data ...mentioning

confidence: 99%

A comfort evaluation method based on an intelligent car cockpit

Yang

Chen

Xing

et al. 2022

Hum Ftrs & Erg Mfg Svc

View full text Add to dashboard Cite

show abstract

“…This information can be pursued with different schemes from the literature, such as frequencial preview loops, adaptative estimation schemes or even extended observers that estimate the road together with the states. Some options for these algorithms are available in [48,5,17,49].…”

Section: Implementation Remarksmentioning

confidence: 99%

Sub‐optimal recursively feasible Linear Parameter‐Varying predictive algorithm for semi‐active suspension control

Morato

Normey‐Rico

Sename

2020

IET Control Theory & Appl

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This paper proposed a control algorithm to enhance the comfort of passengers in a vehicle equipped with Semi-Active Suspension systems, manipulating the force delivered by the Semi-Active damper. For this goal, the vertical dynamics of the car are represented through a quasi-Linear Parameter Varying (qLPV) model. Then, the algorithm resides in solving a set-constrained Model Predictive Control (MPC) problem, embedding a comfort performance index to the MPC optimization function. The sub-optimality of this algorithm resides in the fact that the MPC is synthesized considering a frozen guess for the evolution of the qLPV scheduling parameters along the prediction horizon. Assuming bounds on the variation rates of the qLPV scheduling parameters, the method enables a replacement of the original complex nonlinear LPV MPC optimization by a much simpler Quadratic Program (QP). This QP includes a Lyapunov-decreasing stage cost and embeds set-based terminal ingredients, which guarantee that the domain of attraction of the controller is enlarged and that recursive feasibility can be maintained despite the non-exact model scheduling (and suboptimality). The control structure is tested and compared to other optimal control approaches, such as a clipped Linear Quadratic Regulator. The paper ends with successful realistic nonlinear simulations of a 1/5-scaled car with Electro-Rheological suspensions, which illustrate the overall good operation and behaviour achieved with the proposed regulation algorithm. With the proposed method, the comfort of the passengers is substantially improved.

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“…The method presented by Cvok et al. [8] reproduces the active control input without considering constraints of the semi‐active dampers. It is so called the clipped‐optimal control.…”

Section: Introductionmentioning

confidence: 99%

“…In the active suspension, the application of the preview control theory to improve control performance considering future disturbances [6,7]. However, there are only a few applications of the preview control to semiactive suspensions than those to active suspensions because conventional optimal preview control theory cannot be applied to a nonlinear model such as the MLD model [8,9]. Some studies on vibration control with semi-active suspension system using forward road surface information have been reported so far.…”

Section: Introductionmentioning

confidence: 99%

A novel preview control for MLD models and its neural network approximation for real‐time implementation: Application to semi‐active vibration control of a vehicle suspension

Sato

Hiramoto

2022

IET Control Theory & Appl

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Advances in image processing technology have made it possible to measure the surface shape of the road ahead while driving. A new semi‐active suspension control method considering the forward road surface shape is proposed. A vehicle model equipped with a semi‐active suspension can be expressed as an mixed logical dynamical model. When the shape of the road ahead can be measured, the information on future disturbances is available before the vehicle undergoes. In this paper, the finite time optimization problem for the mixed logical dynamical model is formulated to consider the future disturbances as a mixed integer quadratic programming problem in the same way as the conventional control problem without future disturbance. However, the mixed integer quadratic programming problem is hard to obtain the control action within the control cycle period required in the real‐time vibration control with general computers for vehicles. In this paper, the reduction of the computational load is achieved by constructing an approximation function of the designed controller. A neural network is adopted for the approximation. The performance evaluation of the proposed method is evaluated by simulations. In the simulation study, the proposed method achieves better ride comfort with the equivalent suspension stroke compared to the traditional methods.

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Comparative Performance Analysis of Active and Semi-active Suspensions with Road Preview Control

Cited by 7 publications

References 11 publications

A comfort evaluation method based on an intelligent car cockpit

A comfort evaluation method based on an intelligent car cockpit

Sub‐optimal recursively feasible Linear Parameter‐Varying predictive algorithm for semi‐active suspension control

A novel preview control for MLD models and its neural network approximation for real‐time implementation: Application to semi‐active vibration control of a vehicle suspension

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