Top PDF Feasibility of Using Nonlinear Time-Frequency Control for Magnetorheological Dampers in Vehicle Suspension

Feasibility of Using Nonlinear Time-Frequency Control for Magnetorheological Dampers in Vehicle Suspension

Feasibility of Using Nonlinear Time-Frequency Control for Magnetorheological Dampers in Vehicle Suspension

The proposed nonlinear time-frequency control method evaluated in this research can be successfully applied to MR dampers in vehicle suspension to reduce vibration amplitudes and forces transmitted to the passenger. For every test case presented, the nonlinear time-frequency control outperformed the skyhook control algorithm in terms of minimizing acceleration of the car body. If the priority of the controller is only to reduce the amplitude of vibrations, the skyhook controller should be selected over the proposed nonlinear time-frequency controller. Because nonlinear time-frequency control is a feed-forward controller, it has a better ability to compensate for the time delay of the MR damper compared to a feed-back controller such as skyhook. Another benefit of the nonlinear time-frequency controller is that it is adaptive. This is important so that the controller can account for changes in the physical model. The adaptive algorithm also benefits the system because when the controller is implemented in a physical system, additional nonlinearities not previously accounted for in simulation can be managed. For example, depending on how the vehicle is loaded, the mass values will be different and the controller must be able to adapt to provide an ideal response.
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Hardware-in-the-loop simulation of magnetorheological dampers for vehicle suspension systems

Hardware-in-the-loop simulation of magnetorheological dampers for vehicle suspension systems

based directly upon the frequency response of the system. An alternative approach is to compare the direction to the actual force, and so – as expected – RMS value of one performance indicator against the performance of the semi-active force generator another, as a function of a control parameter. This is deteriorates. Nonetheless, when the desired force is known as a conflict diagram [9], and the optimal a dissipative one, the force tracking accuracy is very system will have its operating point closest to the good as before. Furthermore, it was found that an origin where all of the performance indicators have energy input was only required for 20 per cent of the been minimized.
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Advanced suspension system using magnetorheological technology for vehicle vibration control

Advanced suspension system using magnetorheological technology for vehicle vibration control

In order to further enhance the vibration reduction performance of the vehicle, a full- size variable stiffness and variable damping (VSVD) suspension was further designed, fabricated, and tested in this project. The suspension can be easily installed into a vehicle suspension system without any change to the original configuration. A new 3- degree of freedom (DOF) phenomenological model to further accurately describe the dynamic characteristic of the VSVD suspension was also presented. Based on a simple on-off controller, the performance of the variable stiffness and damping suspension was verified numerically. In addition, an innovative TS fuzzy modelling based VSVD controller was designed. The TS fuzzy modelling controller includes a skyhook damping control module and a state observer based stiffness control module which considering road dominant frequency in real-time. The performance evaluation of the VSVD control algorithm was based on the quarter-car test rig which equipping the VSVD suspension. The experiment results showed that this strategy increases riding comfort effectively, especially under off-road working condition.
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Vehicle longitudinal motion modeling for nonlinear control

Vehicle longitudinal motion modeling for nonlinear control

Modeling the efforts at the wheel-road contact has been given a great deal of interest over the last years. In this respect, several tire models have been developed with quite different properties, e.g. (Guo and Ren, 2000; Pacejka and Besselink , 1997; Dugoff and Segel, 1990; Gim and Nikravesh, 1990; Kiencke and Nielsen, 2004). For control design use, the most suitable tire model is one that presents the best accuracy/simplicity compromise. From this viewpoint, Kiencke‟s model turns out to be a quite satisfactory choice (Kiencke and Nielsen, 2004). Indeed, this model is sufficiently accurate as it accounts for the main features such as the vertical load F v , slip angle , slip coefficient . On the other hand, it has already proved to be useful in designing simple estimators for state variables like slip angle and lateral efforts (You et al., 2009). In the present paper, this model will prove to be useful in control design.
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Active Suspension Control Of Ground Vehicle Heave And Pitch Motion

Active Suspension Control Of Ground Vehicle Heave And Pitch Motion

This chapter describes the background of active suspension control of ground vehicle heave and pitch motions. Next, the problem statement of this study and the objective of the project are explained. Following that, the scope of the project is mentioned and finally this chapter is ended with the outline of the Thesis.

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System Approach to Vehicle Suspension System Control in CAE Environment

System Approach to Vehicle Suspension System Control in CAE Environment

the active suspension system, it is recommended to simulate the obtained analytical solutions on the computer in order to examine the range of change of regulative action and the response of the regulated variable to a typical input signal, and thus confirm the possibility of the system physical realization, bearing in mind the actuator limitations, as well as check whether other possible limitations within the object of control as a whole have been reached. The above statements by no means suggest that laboratory testing is no longer necessary, but that it can be carried out in a shorter period of time and with reduced cost [24]. The key challenge associated with active suspension and their actuators is the size, weight and energy consumption required to achieve acceptable performance. For this reason, the physical properties of the actuator should be included in the optimization problem. In practice, actuator faults are quite often the largest source of control system degradation. Suspension system modelling was performed on ¼ vehicle model, using the Matlab interactive environment and by the state-space equation. The step input is the unit step function, that is, a certain value of road disturbance. It has been concluded that after encountering any kind of obstacle, the settling time and overshoot of the vehicle, are too long, and that a controller must be introduced into the suspension system. We designed a digital controller by the pole- placement method, which makes only one of the possible solutions. The presented dynamic model is only a very rough representation of the true dynamic system, which is applicable only in the early concept design phases of the research and development process. It is expected that, in the near future, this design and the above-mentioned modifications and the necessary improvements, would be used in designing such systems in the motor vehicles industry of our country.
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A novel robust optimal active control of vehicle suspension systems

A novel robust optimal active control of vehicle suspension systems

A suspension system of a vehicle mainly is designed so as to adequately support the vehicle weight, to provide effective isolation of the chassis against road irregularities, to maintain the wheels in appropriate position on the road surface and to keep tire contact with the ground. However, these objectives are in conflict due to requirements of the road holding and passenger comfort in wide range of road irregularities. For example, a soft damping is required to achieve superior ride quality at the expense of larger suspension deflection. In contrast, a large damping yields a better road-holding ability at the cost of comfort. While in primary vehicle suspensions, the geometric and dynamical properties of the suspension structure would be chosen by compromising some of those criteria, in modern suspension structures, a fully active or semi-active device is incorporated to meet these conflicting requirements (Fallah, S. et. al., [2009]).
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Explicit nonlinear model predictive control for vehicle stability control

Explicit nonlinear model predictive control for vehicle stability control

Nonlinear model predictive control is proposed in multiple academic studies as an ad- vanced control system technology for vehicle operation at the limits of handling, allow- ing high tracking performance and formal consideration of system constraints. How- ever, the implementation of implicit nonlinear model predictive control (NMPC), in which the control problem is solved on-line, poses significant challenges in terms of computational load. This issue can be overcome through explicit NMPC, in which the optimization problem is solved off-line, and the resulting explicit solution, with guar- anteed level of sub-optimality, is evaluated on-line. Due to the simplicity of the explicit solution, the real-time execution of the controller is possible even on automotive control hardware platforms with low specifications. The explicit nature of the control law fa- cilitates feasibility checks and functional safety validation. This study presents a yaw and lateral stability controller based on explicit NMPC, actuated through the electro- hydraulically controlled friction brakes of the vehicle. The controller performance is demonstrated during sine-with-dwell tests simulated with a high-fidelity model. The analysis includes a comparison of implicit and explicit implementations of the control system.
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Disturbance observer based control for nonlinear MAGLEV suspension system

Disturbance observer based control for nonlinear MAGLEV suspension system

In this part, the load variation of the MAGLEV suspension is considered. The suspension has to support the large mass of the vehicle as well as the load (weight of the passengers) which can vary up to 40% of the total mass of the vehicle. This is a considerable variation of the total mass and may result in undesirable performance. To this end, the robustness against load variations should be taken into account to ensure performance and stability for a fully laden or unladen vehicle. For testing, we assume that the load variation is up to 25% of the total vehicle mass, i.e., the load can vary from 1000kg to 1250kg for a fully unladen and laden vehicle, respectively. The details of load variation are shown in Fig. 5. The robustness against such case of load variation can be seen in Figs. 3 and 4 by dashed lines. It can be observed from Fig. 3(a) that the maximum air gap deviation is less than 0.006m and there is still no steady-state error. Fig. 3(b) shows that the magnitude of the coil voltage is within the
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Control of MacPherson active suspension system using sliding mode control with composite nonlinear feedback technique

Control of MacPherson active suspension system using sliding mode control with composite nonlinear feedback technique

stiffness of the car body spring, 𝑘 𝑡 stiffness of car tyre, and 𝐶 𝑑 damper coefficient. Other than that 𝑋 𝑠 , 𝑋 𝑢 , and 𝑋 𝑟 are state variables for sprung mass, unsprung mass, and road profile. Generally, the frequency of the unsprung mass lies in the range of 10-15 Hz, while the frequency of the sprung mass lies in the range of 1-2 Hz. (Xue et al. 2011, Appleyard and Wellstead 1995). Moreover, active suspensions commercially implemented in automobiles today are based on hydraulic or pneumatic actuator, Appleyard and Wellstead (1995). Figure 1.2 shows the schematic diagram of the MacPherson strut suspension system applied in modern automotive active suspension system. (Hong et al. 1999). The 𝑓 𝑑 is a body force of
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Active suspension control of electric vehicle with in-wheel motors

Active suspension control of electric vehicle with in-wheel motors

In a four-wheel independent drive electric vehicle (4WID-EV), the torque of each IWM can be independently controlled. AFS and DYC are two effective ways to enhance the handling and stability of EVs. A fuzzy logic driver-assist stability system for 4WID-EV based on a yaw reference DYC is proposed in [112]. Speed sensor-less fuzzy direct torque control for PMSM driven EV is introduced in [113]. Moreover, the integration control system with active AFS and DYC in 4WID-EV is explored recently. A nonlinear integrated control system which combine AFS and DYC together is proposed for 4WID- EV based on a triple-step nonlinear method [114]. Vehicle lateral motion control for 4WID-EV with the combination of AFS and DYC via in-vehicle networks is studied in [115]. The QP-based torque allocation method, and the message priority scheduling method and generalized PI upper-level controller are used to enhance the lateral performance. A combination of AFS and DYC with good robustness is proposed for 4WID-EVs to deal with in-vehicle network caused time-varying delays issues in [116]. The IWM fault taking place in the 4WID-EV, possibly be resulted from mechanical failures and motor vibration. As a result, the faulty wheel and motor possibly cannot supply enough torque and power. Several fault diagnosis and FLC strategies for 4WID- EV are investigated. An IWM fault diagnosis and FLC method for 4WID-EV is proposed in [117]. A fault-tolerant controller for EV with four-wheel-independent-steering (4WIS) and 4WID is presented in [118] based on a modified SMC method. Active fault tolerant controller for EVs with rear wheel IWMs to improve yaw motion control performance is proposed in [119]. Chukwuma proposes a fault-tolerant IWM design which could achieve a large motor torque, high power density, and maintain a certain level of performance following a failure [120].
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Development Of Magnetorheological Damper Test Rig Machine For Automotive Suspension

Development Of Magnetorheological Damper Test Rig Machine For Automotive Suspension

6 Various types of testing are conceivable: sine test (clear in frequency); basic wave frame on every actuator; open circle test (driven by an outside pressure signal); a specific testing technique called ICS control, which permits to recreate on an auto a genuine administration environment, beginning from those information originating from the outdoor procurement sessions. The four actuators are introduced on a storm cellar made of cast-iron. This is disconnected from the beginning six pneumatic springs. The testing apparatus is finished by a power hydraulic central and a control comfort, including a PC based controller (DCS2000) and an information procurement unit. Every actuator is furnished with one displacement LVDT sensor, which is situated in the pole. The displacement control circle is made probable by these transducers. (Vetturi et al., 2007).
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MR dampers in smart structures with nonlinear non-affine dynamics improvising intelligent control

MR dampers in smart structures with nonlinear non-affine dynamics improvising intelligent control

3ATMDs on top ....................................................................................................................................96 Figure 4.5. Last storey displacement (mm) versus time with a sole ATMD on top, fifteen storey structure .................................................................................................................................................96 Figure 4.6. Last storey displacement (mm) versus time, equal mass, fifteen storey structure, 3ATMDs

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Semi-Active LQG Control of Seismically Excited Nonlinear Buildings using Optimal Takagi-Sugeno Inverse Model of MR Dampers

Semi-Active LQG Control of Seismically Excited Nonlinear Buildings using Optimal Takagi-Sugeno Inverse Model of MR Dampers

In order to obtain a high quality trained fuzzy model, high quality training and testing data must be obtained first. In this study data collection has been done from the phenomenological model of 1000kN MR damper (Spencer et.al 1997). Signals of generated displacement and voltage for training the fuzzy model are shown in Figure 3. A time step of 0.005 second is used to produce a total of 10,000 data set through 50s simulation. For the current study, it is assumed that the input vector for the T-S fuzzy model consists of 17 input variables. The 17 candidates to the model include the past and current displacements x ( t 5 ), x ( t 3 ) , x ( t 2 ) , x ( t 1 ) and x (t ) , velocities x ( t 5 ), x ( t 4 ), x ( t 3 ), x ( t 2 ),
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An Explicit Nonlinear MPC Approach to Vehicle Stability Control

An Explicit Nonlinear MPC Approach to Vehicle Stability Control

With respect to the published explicit NMPC work, such as [5], the contribution of this study is in the nonlinear vehicle model for control system design, which considers: i) the interaction of the longitudinal and lateral tire forces; and ii) the effect of the load transfers in cornering. i) and ii) are crucial to the exploitation of the benefits of NMPC for vehicle control at the limits of handling. Moreover, the flexibility of the NMPC cost function formulation adopted in this study allows ease of implementation on real vehicles, with different and usually rather complex performance requirements for the stability control function.
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The analysis of nonlinear systems in the frequency domain using Nonlinear Output Frequency Response Functions

The analysis of nonlinear systems in the frequency domain using Nonlinear Output Frequency Response Functions

The direct extension of the FRF concept to the nonlinear case is known as the Generalized Frequency Response Functions (GFRFs) (George, 1959), which were proposed under the assumption that the output of the nonlinear systems under study can be described by a convergent Volterra series (Boyd and Chua, 1985). The difficulties with the practical application of the GFRFs are that the GFRFs can only be graphically studied up to the second order (Yue et al., 2005). This implies that the well- established Bode or Nyquist diagram based frequency domain analysis cannot be generally extended to the nonlinear case. Therefore, although some specific applications can be found in literatures such as, e.g., in image processing (Ramponi, 1986), channel equalization (Karam and Sari, 1989) and fault detection (Tang et al., 2010), a systematic approach for the analysis of nonlinear systems in the frequency domain that can be generally applied in practice still does not exist.
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Fast Nonlinear SSI Analysis Using A Hybrid Time-Complex Frequency Domain Approach

Fast Nonlinear SSI Analysis Using A Hybrid Time-Complex Frequency Domain Approach

The paper presents a highly efficient nonlinear SSI approach based on a hybrid time-complex frequency approach using an iterative procedure. The hybrid approach uses a piece-wise equivalent-linearization for computing the FEA solution in complex frequency. The local linearized hysteretic models are calibrated based on the “true” nonlinear concrete wall behaviour in time domain. Sophisticated shear deformation hysteretic models with pinching were implemented. Comparative results of the hybrid approach and the true nonlinear time-integration approach showed very good matching. The hybrid approach is applicable to nonlinear SSI analysis for i) design level for correctly including the concrete cracking in structures as a function of stress levels in accordance to the new ASCE 04-2015 standard (Sections 3.2.2 and 3.3.2) and the USNRC SRP requirements, and ii) beyond design level in accordance with the ASCE 43-2005 recommendations, to properly compute the inelastic absorption factors for structural fragility analyses. The paper presents a validation case study of a typical low-rise shearwall nuclear structure for which the hybrid approach results are compared with the “true” nonlinear time-integration approach results. Nonlinear SSI analyses performed for rigid rock and soft soil sites demonstrate the capabilities of the hybrid approach. The nonlinear SSI hybrid approach is also much faster and robust than the “true” nonlinear SSI time-integration approach.
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Modelling and Optimizing of Vehicle Suspension

Modelling and Optimizing of Vehicle Suspension

We developed a programme for calculation of 2D vehicle models built of rigid bodies intercon­ nected by spring and damper elements. It is first necessary to specify all the required data for mass points and rigid bodies, then data for spring and damping elements and connections between indivi­ dual elements. Finally, it is necessary to select the points on the vehicle at which the responses should be calculated. With all these data to hand, the program will set up a mass and stiffness matrix and the damping matrix by itself. Next comes the calculation of eigenfrequencies and mode shapes. It is characteristic of vehicle models that the systems are damped considerably, so damping is not proportional. The calculation of complex eigenfrequencies and mode shapes, as well as of various transfer functions is carried out according to the procedure described in 121. The autospectra of responses are calculated ac­ cording to the following formula for systems with several inputs and outputs 131.
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Control-focused, nonlinear and time-varying modelling of dielectric elastomer actuators with frequency response analysis

Control-focused, nonlinear and time-varying modelling of dielectric elastomer actuators with frequency response analysis

The need to use nonlinear models to accurately describe DEA dynamics, found here, is consistent with current models of DEAs derived by considering the physical properties of the material [20, 43–45]. A distinct, novel contribution of the work presented here is that the NARX models identified are compact difference equation descriptions with few terms, unlike many of the physical models, making them particularly amenable to control design. Further to this, the NARX modelling framework is able to represent a broad range of nonlinear systems because the model class can be used to describe general multiple-input multiple-output (MIMO) systems with a defined input and output [46]. Therefore, it is likely that the modelling and analysis framework could be applied to a wider range of actuator configurations in the future.
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LQR Anti rollover Control of Active Suspension in Vehicle

LQR Anti rollover Control of Active Suspension in Vehicle

Under the stable state of straight, if the left mass and right mass is symmetrical,then LTR = 0, If left or right side of the wheel is raised off the ground, so LTR = 1 or -1, Without the wheels from the ground up the |LTR | < 1, the automobile in side tumbling stability state, And | LTR | value is smaller, the better side tumbling stability of vehicle.

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