An Expert System-Based Automation in Indian Traction System for a One Way Single Platform Station by Introducing PLC

Nowadays, it is very often that some portion of the Indian traction system is still suffering from a single line railway transportation. This in turn creates a havoc disturbance in maintaining the proper sequence of traction control system. Also, passengers are taking risk to catch the train which is already in motion but no such action has been taken to eliminate these consequences. It has been found that more or less various works have been done on Automation in Railway Crossing Gate using Microcontroller and IR Sensor. Thus, it is often decided to develop an idea for the Indian traction system to ensure better controlling action by introducing Limit Switches as Tactile Sensors and by introducing HMI using PLC. The purpose here to take control over various controlling domains, including Railway crossing gate are as follows: Track signal, crossing level signal, alarm notification, and platform edge fence. The proper sequencing needs to be operated via a 128 I/O module with 2 KB memory size small PLC kit.

Actor-Critic Traction Control Based on Reinforcement Learning with Open-Loop Training

The use of actor-critic algorithms can improve the controllers currently implemented in automotive applications. This method combines reinforcement learning (RL) and neural networks to achieve the possibility of controlling nonlinear systems with real-time capabilities. Actor-critic algorithms were already applied with success in different controllers including autonomous driving, antilock braking system (ABS), and electronic stability control (ESC). However, in the current researches, virtual environments are implemented for the training process instead of using real plants to obtain the datasets. This limitation is given by trial and error methods implemented for the training process, which generates considerable risks in case the controller directly acts on the real plant. In this way, the present research proposes and evaluates an open-loop training process, which permits the data acquisition without the control interaction and an open-loop training of the neural networks. The performance of the trained controllers is evaluated by a design of experiments (DOE) to understand how it is affected by the generated dataset. The results present a successful application of open-loop training architecture. The controller can maintain the slip ratio under adequate levels during maneuvers on different floors, including grounds that are not applied during the training process. The actor neural network is also able to identify the different floors and change the acceleration profile according to the characteristics of each ground.

A Study on the Manufacture of Permanent Magnet Traction Control Valve for Electronic Stability Control in Electric Vehicles

Most solenoid valves in use today require a magnetic coil to be continuously energized to maintain the magnetization of the magnetic body in order to operate. The problem is that if the power is still supplied, the power consumption will continue. In addition, problems such as shortening the lifespan of solenoid valve internal parts due to the increase in the internal temperature of the electronic stability control (ESC) due to the continuous heating of the magnetic coil, and malfunction due to instantaneous power failure may occur. In this study, we conducted a study on the permanent magnet traction control valve (TCV) for ESC that can minimize the unnecessary power consumption of electric vehicle batteries. For optimal permanent magnet design, polarity direction setting and permanent magnet specifications were studied through FE simulation. A permanent magnet TCV was fabricated and an electromagnetic force test was conducted to compare and evaluate it with the FE simulation result. By using a permanent magnet, it was possible to lower the initial current value for the TCV to drive, therefore, it was possible to develop a permanent magnet TCV that can minimize the unnecessary power consumption of electric vehicle batteries.

Cars as next generation mobile weather stations: an initial investigation

<p>Modern automobiles are becoming more and more “computers on the wheels” having lots of digital equipment on board. Such equipment is both for the comfort and entertainment of the passengers and for their safety. Sensors play a key role in measuring several parameters of the car performance (e.g., traction control, anti-lock breaking system) and also environmental  parameters are observed directly (e.g., air temperature) or can be somehow inferred (e.g., precipitation via windscreen wipers activity/speed).</p><p>KNMI has been provided air temperature recorded every 10 minutes by thousands of vehicles driving in the Netherlands for the period January-October 2020. We have performed an initial exploratory temporal and spatial analysis to understand the most promising periods of the day and areas where sufficient data is available to perform a more thorough data analysis in the future. Furthermore, we have performed a correlation analysis between the outside temperature measured by cars and air and ground temperature observed by official weather station sensors placed at one location on the Dutch highways. The correlation results for three randomly selected days (with different weather conditions) show a good positive correlation coefficient ranging from 0.93 to 0.76 for car and station air temperature and from 0.91 to 0.67 for car temperature and station ground temperature.</p><p>This initial exploration paves the way to the use of (OEM) car data as (mobile) weather stations. We foresee in the future to use a combination of sensed variables from cars such as air temperature, traction control, windscreen wipers activity for example to improve observations of road slipperiness and related warning systems that are not restricted to Dutch highways only.</p>

Sliding Mode-Based Traction Control for An Autonomous Martian Rover

A traction control system was developed for an autonomous Martian rover using a sliding mode controller. The main inspiration for this project was NASA’s Mars rover, Curiosity, which suffered severe wheel damage due to the lack of an effective traction control system. A control system was sought out to effectively prevent wheel damage, slippage, and soil failure for a Martian rover. It was initially hypothe-sized that a sliding mode controller would be most effective to control the vehicle’s traction. A Simulink model was created with a deformable soil-rigid tire mathematical model in order to simulate the traction control system. The sliding mode controller was tested to be more robust and stable compared to a proportional-integral-derivative (PID) controller for the rover. The results elaborate the possible applica-tions for this project, which spans across commercial and military rovers, rescue robots, and planetary rov-ers in the private and global space industry.