Mona Tahmasebi

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.

Active Force Control Applied to Spray Boom Structure

Distribution pattern of spray boom in fields is affected by several parameters which one of the important reasons is horizontal and vertical vibrations because of unevenness surfaces. Spray boom movements lead to decrease of spread efficiency and crop yield. Generally, active suspension is employed to control and attenuate the vibration of sprayer booms because these suspensions reduce the high frequency vibration of spray booms thanks to irregularities soil. In this research, a proportional-integral-derivative controller with active force control is used to remove undesired rolling of spray boom. Simulation results depict that the proposed scheme is more effective and accurate than PID control only scheme. The AFC based scheme shows the robustness and accuracy compared to the PID controller.

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.

Off-road Vehicle Seat Suspension Optimisation, Part II: Comparative Study between Meta-Heuristic Optimisation Algorithms

Clinical observations show that a significant number of low back pain, spine disorders and head injuries are related to exposure to vibration of vehicles. Implementation of seat to suspensions reduces the effects of vibration in vehicle operators in long terms. This paper discusses three evolutionary algorithms in off-road seat suspension design. Genetic algorithm (GA), particle swarm optimisation (PSO) and harmony search (HS) algorithm were used to minimise transmitted vibration to the drivers spine which was modelled by artificial neural network biodynamic model based on experimental data, as was discussed in part I. The reliability of results was studied by simulation of vibration in vertical direction. The suspension was tuned based on harmony search depicting lower values of seat to spine vibration transmissibility and mobility. Thus, the transmitted vibration to spine and risk of spine problem may be alleviated by using this tuned seat suspension.

Prediction Head Acceleration from Hand and Seat Vibration via Artificial Neural Network Model

Transmitted vibration from hand to the human body is one of the oscillation sources which are a reason of pain in shoulders, neck and arms. Design vibration isolators or seat suspensions require an accurate biodynamic model which can consider both vibration sources; hand and seat. For this purpose, an artificial neural network model was established which is able predict head acceleration from hand and seat excitation. Average of error in signal prediction in this model is around 3.5%, and this issue made appropriate this biodynamic model to use in the next studies especially for suspension designs.

Application of machine learning for NonHolonomic mobile robot trajectory controlling

Mobile robots are very interested by researchers over the last few years because of their applications and physical characteristics. The workspace of mobile robots is not always ideal, but typically filled with disturbances (known or unknown) such as uneven surface terrain, natural friction, uncertainties, and parametric changes. In this study, a new approach namely active force control (AFC) scheme integrating artificial neural network (ANN) has been suggested to cope on the disturbances and thus improve the trajectory tracking characteristic of the system. Therefore, a two wheeled mobile robot has been simulated, and ANN technique is explicitly employed for the estimation of the inertia matrix that is needed in the inner feedback control loop of the AFC scheme. The robustness and efficiency of the identified control scheme are studied considering various forms of loading and operating conditions. For the purpose of benchmarking, the AFC scheme performance has been compared to PID controller.

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.

Vibration suppression of sprayer boom structure using active torque control and iterative learning. Part II: Experimental implementation:

The most common technique of protecting crops from diseases is by applying a chemical process whereby a mixture of chemicals and water are sprayed onto the crops via a sprayer. Nowadays, modern sprayers are generally implemented to suspension systems for reducing the unwanted vibration of the spray boom structure to improve the uniformity of spray distribution in the agricultural field environment. This paper serves to present a new alternative to address and resolve the vibration control problem of the moving sprayer structures. The application of an active torque control (ATC) method to cancel the undesired vibration of the sprayer boom is thus proposed. As a continuation from Part I that deals with the modeling and simulation aspect, Part II explains the practical facet of the study through the implementation of ATC and iterative learning (ATCAIL) control scheme to an experimental spray boom structure as a basis to validate the effectiveness and robustness of the scheme as simulated and described at length in Part I. A sprayer boom suspension system test rig was specifically designed and developed to verify this control scheme that was principally chosen due to its ease of implementation through the exploitation of simple proportional–integral–derivative (PID), ATC and iterative learning algorithms. The system performance was evaluated and compared to the PID and ATC–PID control schemes for benchmarking. The results demonstrate the capability of the practical ATCAIL scheme to improve the vibration suppression in both time and frequency domains, thereafter guaranteeing a more uniform spray distribution of chemicals on a bumpy terrain. The experimental outcomes are in good agreements with the simulation counterpart.

Intelligent active force control of a helicopter seat suspension using iterative learning algorithm

The high level of noise and vibrations in helicopters is not preventable and happens through flight operations. This high level of vibrations can produce uneasiness and may affect aircrew performance and their health. Correspondingly, their concentration on flight operation and decision making is strongly depended to comfort ability. Therefore, vibration attenuation can improve flight control, and aircrews feel better conditions. In this study, the helicopter structure was modeled in ANSYS software and natural frequencies have been obtained. The seat suspension and pilot body were modeled by Lumped modeling method. The active force control (AFC) scheme hybridized by Iterative learning (IL) to determine the estimated mass called AFCIL was used in helicopter seat suspension system to reduce the vibrations transmitted to the pilot body. The simulation was performed with sinusoidal and random disturbance signals and results demonstrated in both the time and frequency domains. Attained results were compared with the passive system, PID controller and AFCANN schemes. The AFCIL scheme had superior performance in pilot head displacement reduction compared to the classical PID controller. The results of the AFCIL and the AFCANN were similar together while AFCIL results were marginally superior to AFCANN.