Antilock braking control using robust control approach

The aims of this study are to establish the mathematical model and the robust control technique for an Antilock Braking System (ABS). The ABS have been developed to reduce tendency of wheel lock up and to improve vehicle control during sudden braking. The ABS work by maintaining the wheel slip to a desired level so that maximum tractive force and maximum vehicle deceleration is obtained, thus reducing the vehicle stopping distance. A quarter vehicle model undergoing straightline braking maneuver, tire dynamics and hydraulic brake dynamics mathematical model are developed to represent the ABS model. The established mathematical model shows the ABS dynamics exhibits strong nonlinear characteristics. Thus, Sliding Mode Control which is a robust control technique is proposed in this study to regulate the wheel slip at the desired value depending on the road surface. The mathematical derivations proved the designed controller satisfy the stability requirement. Extensive simulation study is performed to verify the effectiveness of the designed controller and the result shows the designed controller able to maintain the wheel slip at the desired value and reducing the stopping distanc

Predictive intelligence to the edge through approximate collaborative context reasoning

We focus on Internet of Things (IoT) environments where a network of sensing and computing devices are responsible to locally process contextual data, reason and collaboratively infer the appearance of a specific phenomenon (event). Pushing processing and knowledge inference to the edge of the IoT network allows the complexity of the event reasoning process to be distributed into many manageable pieces and to be physically located at the source of the contextual information. This enables a huge amount of rich data streams to be processed in real time that would be prohibitively complex and costly to deliver on a traditional centralized Cloud system. We propose a lightweight, energy-efficient, distributed, adaptive, multiple-context perspective event reasoning model under uncertainty on each IoT device (sensor/actuator). Each device senses and processes context data and infers events based on different local context perspectives: (i) expert knowledge on event representation, (ii) outliers inference, and (iii) deviation from locally predicted context. Such novel approximate reasoning paradigm is achieved through a contextualized, collaborative belief-driven clustering process, where clusters of devices are formed according to their belief on the presence of events. Our distributed and federated intelligence model efficiently identifies any localized abnormality on the contextual data in light of event reasoning through aggregating local degrees of belief, updates, and adjusts its knowledge to contextual data outliers and novelty detection. We provide comprehensive experimental and comparison assessment of our model over real contextual data with other localized and centralized event detection models and show the benefits stemmed from its adoption by achieving up to three orders of magnitude less energy consumption and high quality of inference

Self Tuning PID Control Of Antilock Braking System Using Electronic Wedge Brake

This paper describes the design of an antilock braking system (ABS) control for a passenger vehicle that employs an electronic wedge brake (EWB). The system is based on a two-degree-of-freedom (2-DOF) vehicle dynamic traction model, with the EWB acting as the brake actuator. The developed control structure, known as the Self-Tuning PID controller, is made up of a proportional-integral-derivative (PID) controller that serves as the main feedback loop control and a fuzzy supervisory system that serves as a tuner for the PID controller gains. This control structure is generated through two structures, namely FPID and SFPID, where the difference between these two structures is based on the fuzzy input used. An ABS-based PI D controller and a fuzzy fractional PID controller developed in previous works were used as the benchmark, as well as the testing method, to evaluate the effectiveness of the controller structure. According to the results of the tests, the performance of the SFPID controller is better than that of other PID and FPID controllers, being 10% and 1% faster in terms of stopping time, 8% and 1% shorter in terms of stopping distance, 9% and 1% faster in terms of settling time, and 40% and 5% more efficient in reaching the target slip, respectively

Artificial Immune Systems - Models, algorithms and applications

Copyright © 2010 Academic Research Publishing Agency.This article has been made available through the Brunel Open Access Publishing Fund.Artificial Immune Systems (AIS) are computational paradigms that belong to the computational intelligence family and are inspired by the biological immune system. During the past decade, they have attracted a lot of interest from researchers aiming to develop immune-based models and techniques to solve complex computational or engineering problems. This work presents a survey of existing AIS models and algorithms with a focus on the last five years.This article is available through the Brunel Open Access Publishing Fun