Fuzzy model for braking force maximization

This paper shows the process of braking force realization by air brakes with brake shoes accompanied by a suitable mechanical model. The complexity of adhesion nature as a physical phenomenon as well as the limited factors on which the braking force value depends are pointed out. According to this, the model of braking force realization based on the fuzzy set theory is explained. The procedure of fuzzy controller projecting with a task to regulate the value of kidding and by that the value of braking torque through the air pressure in the braking cylinder by maximizing the braking force that can be realized according to adhesion conditions is described. The testing of the optimization model under concrete adhesion conditions of the wheels on the rails is done at the end of the paper

Smart Traction Control Systems for Electric Vehicles Using Acoustic Road-type Estimation

The application of traction control systems (TCS) for electric vehicles (EV) has great potential due to easy implementation of torque control with direct-drive motors. However, the control system usually requires road-tire friction and slip-ratio values, which must be estimated. While it is not possible to obtain the first one directly, the estimation of latter value requires accurate measurements of chassis and wheel velocity. In addition, existing TCS structures are often designed without considering the robustness and energy efficiency of torque control. In this work, both problems are addressed with a smart TCS design having an integrated acoustic road-type estimation (ARTE) unit. This unit enables the road-type recognition and this information is used to retrieve the correct look-up table between friction coefficient and slip-ratio. The estimation of the friction coefficient helps the system to update the necessary input torque. The ARTE unit utilizes machine learning, mapping the acoustic feature inputs to road-type as output. In this study, three existing TCS for EVs are examined with and without the integrated ARTE unit. The results show significant performance improvement with ARTE, reducing the slip ratio by 75% while saving energy via reduction of applied torque and increasing the robustness of the TCS.Comment: Accepted to be published by IEEE Trans. on Intelligent Vehicles, 22 Jan 201

A fuzzy model for an increase in locomotive traction force

This paper deals with the process of traction force realization described by a suitable mechanical model and is pointed to the adhesion phenomenon as a physical one, i.e. is a suitable factor that the value of traction force depends on. The model for the process of optimizing locomotive traction force based on using the fuzzy set theory is explained. The projecting process of a fuzzy controller regulating the value of skidding and the value of traction torque by increasing the value of traction force that can be realized according to adhesion conditions is described. Finally, testing the optimization model in several numerical examples under specific conditions of wheel-rail adhesion is done. First published online: 27 Oct 201

Reliability of Training Data Sets for ML Classifiers: a Lesson Learned from Mechanical Engineering

The popularity of learning and predictive technologies, across many problem domains, is unprecedented and it is often underpinned with the fact that we efficiently compute with vast amounts of data and data types, and thus should be able to resolve problems, which we could not in the past. This view is particularly common among scientists who believe that the excessive amount of data, we generate in real life, is ideal for performing predictions and training algorithms. However, the truth might be quite different. The paper illustrates the process of preparing a training data set for an ML classifier, which should predict certain conditions in mechanical engineering. It was not the case that it was difficult to define and choose classifiers, in order to secure safe predictions. It was our inability to create a safe, reliable and trustworthy training data set, from scientifically proven experiments, which created the problem. This places serious doubts on the way we use learning and predictive technologies today. It remains debatable what the next step should be. However, if in ML algorithms, and classifiers in particular, the semantic which is built-in data sets, influences classifier’s definition, it would be very difficult to evaluate and rely on them, before we understand data semantics fully. In other words, we still do not know how the semantic, sometimes hidden in a data set, can adversely affect algorithms trained by them

A map-based model predictive control approach for train operation

Trains are a corner stone of public transport and play an important role in daily life. A challenging task in train operation is to avoid skidding and sliding during fast changes of traction conditions, which can, for example, occur due to changing weather conditions, crossings, tunnels or forest entries. The latter depends on local track conditions and can be recorded in a map together with other location-dependent information like speed limits and inclination. In this paper, a model predictive control (MPC) approach is developed. Thanks to the knowledge of future changes of traction conditions, the approach is able to avoid short-term skidding and sliding even under fast changes of traction conditions. In a first step, an optimal reference trajectory is determined by a multiple-shooting approach. In a second step, the reference trajectory is tracked by an MPC setup. The developed method is simulated along a track with fast-changing traction conditions for different scenarios, like changing weather conditions and unexpected delays. In all cases, skidding and sliding is avoided.Comment: 6 pages, 7 figures, accepted at ECC 202

Investigation on electric motor braking control system for electric powered wheelchair

In recent years, research on Electric Powered Wheelchair (EPW) has been widely studied due to its high importance of mobility for disabled people. During descent on a slope, the manual braking system is commonly used to control the speed by gripping the brake lever. However, the task becomes difficult if the user is an elderly or paralyzed due to their body’s deficiencies. As a result, the possibilities of collision and injuries to occur are high. In this study, the automatic electric motor braking control that is known as Hill Descent Control (HDC) is proposed to increase the safety of EPW during descending on slopes. Since the electric motor has an advantage which can generate the torque during braking, the plugging braking is integrated with the HDC system to control the speed of the EPW according to the desired speed from the user. The analysis of this study is divided into three phases; investigation of braking performance using electrical braking, development of active braking control system in the embedded system as well as the simulation environment and analysis on active braking control system in experimental and simulation work. From the experimental results, the plugging brake is most suitable to integrate with the active brake control system compared to the regenerative and dynamic brake. In the plugging brake, by changing the plugging voltage from 0.5 V to 4.5 V, a variety of dynamic behaviour effects such as braking distance, tire speed and slip ratio can be achieved. Meanwhile, from the analysis of active braking control system that was integrated with plugging braking, both of the experimental and simulation analysis results show the speed of EPW can be maintained at the desired speed o