Estimation based on acceleration measures of an active suspension plant

García, Sara D.; Patino, Diego

doi:10.1109/ccac.2015.7345220

Cited by 4 publications

(6 citation statements)

References 11 publications

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“…With the closed-loop system introduced, the following theorem is proposed. (19) and (20) will also be controllable.…”

Section: Problem Formulation: the Position-feedback Controlmentioning

confidence: 99%

“…Assuming, initially, that ( ) is available, a gain is designed so that the feedback system (19) shows adequate performance. This procedure can be done, for example, using techniques based on LMIs, considering several performance indices, such as: robustness, operating region, decay rate, among others.…”

Section: Problem Formulation: the Position-feedback Controlmentioning

confidence: 99%

“…The presence of polytopic uncertainties in the active suspension system was considered for carrying out control designs, with the uncertainties relating to the mass of the vehicle body portion (values ranging between 1.455 and 2.45 kg). Some papers, like [19], use acceleration measurements for the control method of the Quanser active suspension system, but for the methods proposed on this paper these measurements are not necessary. Substituting the values presented in Table 1, considering the polytopic uncertainties, we have the following local models for the vertices of the polytope:…”

Section: B Quanser Active Suspension Systemmentioning

confidence: 99%

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Implementations of Full State Feedback Robust Control Designs for Mechanical Systems Using Only Positions or Velocities Output Feedback

et al. 2022

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This paper proposes a new design and practical implementation of a robust H ∞ control applied to some mechanical systems considering in the controller design full access to the state variables and in the equivalent controller implementation only the feedback of all positions or of all velocities of the plant. An important characteristic, though, is that limitations are considered for the state feedback. Two methods are presented, one that uses feedback only from state variables related to positions of the system and another that uses feedback only from state variables related to velocities. The control strategy is based on Linear Matrix Inequalities (LMIs), using the theory of D-stability, which allows the designer to allocate the closed loop system eigenvalues in a negative complex semi-plane region, which, in addition to ensuring stability, also allows to attend certain performance requirements of the feedback system. The paper motivation is to provide satisfactory control results with limited states access, without any kind of estimation of the state variables that are not available. Therefore, the proposed control systems are interesting options as alternatives for the design of full-order state feedback for plants with uncertain parameters using only output feedback, considering that it is not required to build an observer to estimate any plant state variable. Furthermore, their implementations are relatively cheaper because it is not necessary to measure all state variables of the plant.

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“…With the closed-loop system introduced, the following theorem is proposed. (19) and (20) will also be controllable.…”

Section: Problem Formulation: the Position-feedback Controlmentioning

confidence: 99%

Section: Problem Formulation: the Position-feedback Controlmentioning

confidence: 99%

Section: B Quanser Active Suspension Systemmentioning

confidence: 99%

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Implementations of Full State Feedback Robust Control Designs for Mechanical Systems Using Only Positions or Velocities Output Feedback

et al. 2022

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“…The purposes of the suspension system are to support the vehicle weight, keep the wheels on the ground, minimize the transient force to the body, maintain good ride comfort, and enhance handling performance [4]. Thus, the most vital factor to improve handling and comfort of driver and passengers is by improving the vehicle suspension system [5]. One way to improve the suspension system is by designing its controller [1], [5]- [7].…”

Section: Introductionmentioning

confidence: 99%

“…Thus, the most vital factor to improve handling and comfort of driver and passengers is by improving the vehicle suspension system [5]. One way to improve the suspension system is by designing its controller [1], [5]- [7].…”

Section: Introductionmentioning

confidence: 99%

Speed Effect to a Quarter Car ARX Model Based on System Identification

Hanafi¹,

Suid²,

Ribuan³

et al. 2017

International Journal on Advanced Science, Engineering and Information Technology

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This paper presents the effect of car speeds on a quarter car passive suspension system model dynamics. The model is identified using system identification technique, in which the input-output data are collected by running a test car on an artificial road surface with two different speeds i.e., 10 km/h and 20 km/h. The quarter car passive suspension system dynamics is assumed to have an ARX model structure and identified using linear least-square estimation algorithm. The car vertical body acceleration, which is the output variable, is measured by installing an accelerometer sensor on the car body, above the suspension. On the other hand, the car shaft acceleration, which is the input variable, is measured by installing an accelerometer sensor at the lower arm of the car suspension. The best model for the 10 km/h car speed gives the output order () = 4, the input order () = 2, delay (d) = 1, the best fit = 90.65%, and the Akaike's Final Prediction Error (FPE) = 5.315e-06. In contrast, the 20 km/h speed results in 4 th output order (), 1 st the input order (), 1 st delay (d), the best fit of 91.05%, and 7.503e-05 Akaike's FPE. These results show that the higher speed reduces the effect of the road surface to car dynamics, which is indicated by the order of the model Keywords-a quarter car passive suspension system dynamics; speed; running test; artificial road surface; ARX model

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