Roslan Abd. Rahman

Effects of control techniques and damper constraint on the performance of a semi-active magnetorheological damper

Semi-active dampers change their damping force in real time by simply changing the damping coefficient according to a control policy. In this study, a number of semi-active control algorithms namely modified skyhook, modified groundhook and modified hybrid skyhook-groundhook controllers are evaluated to be used with a magnetorheological damper. The effectiveness of these control algorithms in disturbance rejection are investigated along with their ability to consistently provide the target force in the same direction with the damper velocity to overcome the damper constraint. From the simulation and experimental results, the modified hybrid skyhook-groundhook controller shows significant improvement in body acceleration, body displacement, suspension displacement without allowing excessive wheel acceleration magnitude. The modified hybrid skyhook-groundhook controller is also superior to the counterparts in overcoming the damper constraint by producing the target forces consistently in the same sign with the damper velocity.

Off-road Vehicle Seat Suspension Optimisation, Part I: Derivation of an Artificial Neural Network Model to Predict Seated Human Spine Acceleration in Vertical Vibration

Whole body vibration produces some serious problems for human health in the long term. Low-frequency vibration, generated during vehicle operation, and transmitted to the vehicle operator, plays a major role in the development of low-back pain. Back pain is one of epidemic injuries in heavy duty vehicle drivers. Generally seat suspensions are designed and optimised to remove this unwanted movement. Human body biodynamic model is essential in passive seat suspension optimisation and active control seat suspension design. Lumped parameter models have been used by researchers for this purpose, but they have some limitations such as fixed body weight. With reference to this limitation, in first part of this paper a new artificial neural network (ANN) model is introduced which can predict spine acceleration from excitation signal and human body mass and height. The accuracy of model is 96% and makes it useful in real-time and off-line analysis. In second part of the paper, an off-road seat suspension will be optimised via this achieved ANN model and three Meta-Heuristic algorithms.

A Novel Artificial Neural Network Biodynamic Model for Prediction Seated Human Body Head Acceleration in Vertical Direction

Nowadays, usage of vehicles increases due to modern lifestyles, and many people are exposed to vibrations in vehicles. Vibrations in low frequency range cause some serious long-term diseases in both aspects physically and psychologically. Vibration model helps researchers to have better interpretation of vibrations transmitting to human organs. Lumped models are very popular in this field, and different types of models with various degrees of freedom have been introduced. The main disadvantage of lumped models is that due to its fixed weight, some modifications need to be made to new subjects. Therefore, a new biodynamic model with artificial neural network method was constructed to simulate transmitted vibration to head for seated human body by conducting indoor vertical vibration experiments. Five healthy males participated in the tests. They were subjected to vertical vibration, and their responses were recorded. A neural network model was trained by input-output accelerations. The developed model was able to predict head acceleration from exciting vibration at the pelvic. In addition, weight and height of human body were considered as input factors. The comparison between the model evaluation results and the experimental and other lumped models affirmed high accuracy of the achieved artificial neural network biodynamic model.

Roll Movement Control of a Spray Boom Structure Using Active Force Control with Artificial Neural Network Strategy

Currently, most of modern sprayers are equipped with suspensions for improving the uniformity of spray application in the field. Therefore, this paper represents the possibility of applying active force control (AFC) technique for the control of a spray boom structure undesired roll movement through a simulation analysis. The dynamic model of the spray boom was firstly defined and an AFC-based scheme controller was designed and simulated in MATLAB environment. Artificial neural network (ANN) is incorporated into the AFC scheme to tune the proportional-derivative (PD) controller gains andcompute the spray boom estimated mass moment of inertia. The training of both ANN with multi layer feed forward structure was done using Levenberg-Marquardt (LM) learning algorithm. To evaluate the AFC-ANN control system robustness, various types of disturbances and farmland terrain profileshave been used to excite the spray boom. The results of the study demonstrated that the AFC-based method offers a simple and effective computation compared to the conventional proportional-integral-derivative (PID) control technique in attenuating the unwanted spray boom roll oscillation or vibration. The AFC-ANN scheme is found to exhibit superior performance for different proposed terrain profilesin comparison to the AFC-PD and pure PD counterparts.

Artificial neural network model for monitoring oil film regime in spur gear based on acoustic emission data

The thickness of an oil film lubricant can contribute to less gear tooth wear and surface failure. The purpose of this research is to use artificial neural network (ANN) computational modelling to correlate spur gear data from acoustic emissions, lubricant temperature, and specific film thickness (λ). The approach is using an algorithm to monitor the oil film thickness and to detect which lubrication regime the gearbox is running either hydrodynamic, elastohydrodynamic, or boundary. This monitoring can aid identification of fault development. Feed-forward and recurrent Elman neural network algorithms were used to develop ANN models, which are subjected to training, testing, and validation process. The Levenberg-Marquardt back-propagation algorithm was applied to reduce errors. Log-sigmoid and Purelin were identified as suitable transfer functions for hidden and output nodes. The methods used in this paper shows accurate predictions from ANN and the feed-forward network performance is superior to the Elman neural network.

Semi-active suspension control to improve ride and handling using magnetorheological (MR) damper

This paper presents the simulation study of a semi-active suspension system using non-parametric model of magnetorheological (MR) damper. Hybrid stability augmentation-force control (HSAS-FC) is proposed as the control algorithm. The performance of the proposed control algorithm is analysed using validated full vehicle model and compared with modified hybrid skyhook groundhook control and passive system. The HSAS-FC is used to estimate the desired force needed to simultaneously improve ride dynamics and handling performance. The stability augmentation system (SAS) is to improve ride, meanwhile the force control (FC) is used to control roll rate and pitch rate due to lateral load transfer and longitudinal load transfer. An adaptive inverse current model was developed to produce the required current by the MR damper. The results show the new system is effective in isolating vehicle body from unwanted motions at the body centre of gravity. The amplitude and settling time of unwanted body motions are reduce for ride.

Modelling of magnetorheological semi-active suspension system controlled by semi-active damping force estimator

This paper presents the simulation study of magnetorheological semi-active suspension system controlled by a new proposed algorithm. The control algorithm named as Semi-Active Damping Force Estimator (SADE) is proposed to control the operations of the magnetorheological damper. The simulation of semi-active suspension system was done by considering actual dynamics behaviour of a custom-made magnetorheological damper, where its characteristic testing and modelling are described. The performance of magnetorheological semi-active car suspension system controlled by SADE in term of ride comfort is evaluated in comparison with normal suspension system. The performance of SADE semi-active suspension system is also compared with the skyhook-controlled semi-active suspension system performance. It is shown that magnetorheological semi-active suspension system controlled by SADE is able to improve vehicles ride comfort significantly compared to passive suspension system. The SADE-controlled semi-active suspension system performs more or less the same as skyhook-controlled semi-active suspension system.

Structure-Borne Power Transmission in Thin Naturally Orthotropic Plates: General Case

The structural intensity technique is usually used to estimate vibration power flow in structures. This method is used to determine vibration power flow in thin naturally orthotropic plates. The bending wave is considered to find general vibration power transmission in the frequency domain that is not approximated by far field conditions. This intensity formulation defines power flow per unit width of the plates (W m−1) similar to that of the conventional idea. Power flow estimation is formulated using cross-spectra of field signals, facilitating the use of a fast Fourier transform analyzer.

Vibration suppression of sprayer boom structure using active torque control and iterative learning. Part I: Modelling and control via simulation

Since one of the influential factors that affects the spray distribution pattern is the spray boom movements which are mostly induced by soil unevenness, most of the recent sprayers are equipped with suspensions for improving the uniformity of spray application in the field. This paper investigates the suitability of improving the sprayer suspension dynamics performance by employing a robust intelligent control scheme, namely active torque control (ATC) based method in reducing the undesired vibration through a simulation study. The ATC scheme with a self-tuning fuzzy proportional-integral-derivative (PID) (ATC-STF-PID) controller was first designed and simulated. Then an artificial intelligence (AI) method using iterative learning (IL) was embedded and implemented into the ATC loop to compute the estimated inertial parameter of the system; this scheme is known as ATCAIL. Thereafter, the performance of the ATCAIL scheme is later compared to the ATC with artificial neural network (ATCANN), ATC-STF-PID and STF-PID controllers in time and frequency domains. The results of simulation work affirm that ATC-based schemes can improve the system performance of the active rolling suspension in relation to roll vibration suppression. In other words, both the ATCAIL and ATCANN schemes show better responses in comparison to the ATC-STF-PID controller scheme. The results also imply that the ATCAIL scheme is indeed effective in suppressing the vibration of a sprayer boom structure.

Tyre Force control strategy for semi-active magnetorheological damper suspension system for light-heavy duty truck

A semi-active controller scheme for magnetorheological (MR) damper of a light-heavy vehicle suspension known as Tyre Force Control (TFC) is proposed. The effectiveness of the proposed TFC algorithm is compared with Groundhook (GRD) control. A simulation model was developed and simulated using MATLAB Simulink software. The performance of the semi-active MR damper using TFC is analytically studied. Ride test was conducted at three different speeds and two different bumps, and the simulation results of TFC and GRD are compared and analysed. The results showed that the proposed controller is able to reduce tyre force significantly compared to GRD control strategy.