A fuzzy logic controller to line starting performance synchronous motor for a crane system using vector control

This paper presents the design process of a synchronous motor of crane system using vector control of line starting [1]. The preliminary design is d-q model armature rotor line start synchronous motor with vector control for decreasing a starting current and torque. The design allows the synchronous motor to operate at both starting and synchronous speed. The basic equations for park transformation of the rotor-stator for proposed vector control to synchronous motor are presented [2]. The starting performance of synchronous motor, for example in crane application, requires rapid dynamics and precise regulation; hence the need of direct control is becoming an urgent demand. This type of control providesanindependent vector control of torqueand current, whichis similar to a separatelyexcited synchronous motor and offersa number ofattractivefeatures. Synchronous motorhasahighstartingtorquewhileseparately synchronous motorcanoperate abovethebase low speedinthe line starting current [3]. This paper designs study and highlights the effectiveness of the proposed vector control methods for a line starting performance of synchronous motor model parameter, using a fuzzy logic controller methods both simulation and manufacturers measured experimental data. Asteady state and transient analysis of the synchronous motor is performed belowand abovebase line starting current

Modeling of occupant's head movement behavior in motion sickness study via time delay neural network

Passengers are more susceptible to experiencing motion sickness (MS) than drivers. The difference in the severity of MS is due to their different head movement behavior during curve driving. When negotiating a curve, the passengers tilt their heads towards the lateral acceleration direction while the drivers tilt their heads against it. Thus, to reduce the passengers’ level of MS, they need to reduce their head’s tilting angle towards the lateral acceleration direction. Designing MS minimization strategies is easier if the correlation between the head movement and lateral acceleration is known mathematically. Therefore, this paper proposes the utilization of a time delay neural network (TDNN) to model the correlation of the occupant’s head movement and lateral acceleration. An experiment was conducted to gather real-time data for the modeling process. The results show that TDNN manages to model the correlation by producing a similar output response to the actual response. Thus, it is expected that the correlation model could be used as an occupant’s head movement predictor tool in future studies of MS

Path Tracking on Autonomous Vehicle for Severe Maneuvre

Autonomous vehicle consists self-learning process consists recognizing environment, real time localization, path planning and motion tracking control. Path tracking is an important aspect on autonomous vehicle. The main purpose path tracking is the autonomous vehicle have an ability to follow the predefined path with zero steady state error. The non-linearity of the vehicle dynamic cause some difficulties in path tracking problems. This paper proposes a path tracking control for autonomous vehicle. The controller consists of a relationship between lateral error, longitudinal velocity, the heading error and the reference yaw rate. In addition, the yaw rate controller developed based on the vehicle and tyre model. The effectiveness of the proposed controller is demonstrated by a simulation

Cartographer slam method for optimization with an adaptive multi-distance scan scheduler

This paper presents the use of Google's simultaneous localization and mapping (SLAM) technique, namely Cartographer, and adaptive multistage distance scheduler (AMDS) to improve the processing speed. This approach optimizes the processing speed of SLAM which is known to have performance degradation as the map grows due to a larger scan matcher. In this proposed work, the adaptive method was successfully tested in an actual vehicle to map roads in real time. The AMDS performs a local pose correction by controlling the LiDAR sensor scan range and scan matcher search window with the help ofscheduling algorithms. The scheduling algorithms manage the SLAM that swaps between short and long distances during map data collection. As a result, the algorithms efficiently improved performance speed similar to short distance LiDAR scanswhile maintaining the accuracy of the full distance of LiDAR. By swapping the scan distance of the sensor, and adaptively limiting the search size of the scan matcher to handle difference scan sizes, the pose's generation performance time is improved by approximately 16% as compared with a fixed scan distance, while maintaining similar accuracy

Robust hovering controller for uncertain multirotor micro aerial vehicles (MAVS) in gps-denied environments: IMAGE-BASED

This paper proposes an image-based robust hovering controller for multirotor micro aerial vehicles (MAVs) in GPS-denied environments. The proposed controller is robust against the effects of multiple uncertainties in angular dynamics of vehicle which contain external disturbances, nonlinear dynamics, coupling, and parametric uncertainties. Based on visual features extracted from the image, the proposed controller is capable of controlling the pose (position and orientation) of the multirotor relative to the fixed-target. The proposed controller scheme consists of two parts: a spherical image-based visual servoing (IBVS) and a robust flight controller for velocity and attitude control loops. A robust compensator based on a second order robust filter is utilized in the robust flight control design to improve the robustness of the multirotor when subject to multiple uncertainties. Compared to other methods, the proposed method is robust against multiple uncertainties and does not need to keep the features in the field of view. The simulation results prove the effectiveness and robustness of the proposed controller

Fully convolutional neural network for Malaysian road lane detection

Recently, a deep learning, Fully Convolutional Neural Network (FCN) has been widely studied because it can demonstrate promising results in the application of detection of objects in an image or video. Hence, the FCN approach has been proposed as one of the solution methods in mitigating the issues pertinent to Malaysia’s road lane detection. Previously, FCN model for lane detection has not been tested in Malaysian road conditions. Therefore, this study investigates the further performance of this model in the Malaysia. The network model is trained and validated using the datasets obtained from Machine Learning NanoDegree. In addition, the real-time data collection has been conducted to collect the data sets for the testing at the highway and urban areas in Malaysia. Then, the collected data is used to test the performance of the FCN network in detecting the lane markings on Malaysia road. The results demonstrated that the FCN method is achieving 99% of the training and validation accuracy

Potential field based motion planning with steering control and DYC for ADAS

In this study, the development of motion planning and control for collision avoidance driver assistance systems is presented. A potential field approach has been used in formulating the collision avoidance algorithm based on predicted vehicle motion. Then, to realize the advanced driver assistance systems (ADAS) for collision avoidance, steering control system and direct yaw moment control (DYC) is designed to follow the desired vehicle motion. Performance evaluation is conducted in simulation environment in term of its performance in avoiding the obstacles. Simulation results show that the vehicle collision avoidance assistance systems can successfully complete the avoidance behavior without colliding

Threat assessment algorithm for Active Blind Spot Assist system using short range radar sensor

Road safety has become more concern due to the number of accidents that keeps increasing every year. The safety systems include from simple installation such as seat belt, airbag, and rear camera to more complicated and intelligent systems such as braking assist, lane change assist, steering control and blind spot monitoring. This paper proposes another intelligent safety system to be implemented in passenger vehicle by monitoring the blind-spot region by using automotive short range radar as sensor to assess its surrounding. This system is called Active Blind-Spot Assist (ABSA) system and this system will collaborate with a Steering Intervention system for autonomous steering maneuvers. The objective of ABSA system is to deploy safety interventions by giving warning to the driver whenever other vehicle is detected within the blind-spot region. Furthermore, this active system also triggers autonomous steering control when the potential of collision with the detected vehicle increases greatly. Consequently, a threat assessment algorithm is developed to evaluate the right moment to give safety interventions to the driver and the conditions for autonomous steering maneuvers. The process of developing the threat assessment algorithm explained in this paper

Optimised combinatorial control strategy for active anti-roll bar system for ground vehicle

The objective of this paper is to optimise the proposed control strategy for an active anti-roll bar system using non-dominated sorting genetic algorithm (NSGA-II) tuning method. By using an active anti-roll control strategy, the controller can adapt to current road conditions and manoeuvres unlike a passive anti-roll bar. The optimisation solution offers a rather noticeable improvement results compared to the manually-tuned method. From the application point of view, both tuning process can be used. However, using optimisation method gives a multiple choice of solutions and provides the optimal parameters compared to manual tuning method

Steering intervention strategy for side lane collision Avoidance

Advance Driver Assistance Systems (ADAS) have successfully been integrated in many vehicles; however, the research on its improvement is still on-going. Some of the features of ADAS include Lane Departure warning System, Blind Spot detection, Lane Change Assistance and etc. However, with such systems available, accidents still occurred due to the driver's lack of awareness and negligence towards the given indication and warning, especially situation related to side lane collision. Thus, this paper aims to propose a simple steering intervention control. If the driver still proceed for the lane change when there are other object appearing in the blind spot area, the proposed solution will automatically trigger vehicle evasion mode to avoid side lane collision. The system does not take into account comfort in order to warn the driver. The system was tested and validated using a test vehicle. The results show that the steering intervention provides good vehicle evasion results and hypothetically it may act as the final warning towards the person behind the wheel