Differential Evolution: A review of more than two decades of research

We present an interactive design system that allows non-expert users to create animated mechanical characters. Given an articulated character as input, the user iteratively creates an animation by sketching motion curves indicating how different parts of the character should move. For each motion curve, our framework creates an optimized mechanism that reproduces it as closely as possible. The resulting mechanisms are attached to the character and then connected to each other using gear trains, which are created in a semi-automated fashion. The mechanical assemblies generated with our system can be driven with a single input driver, such as a hand-operated crank or an electric motor, and they can be fabricated using rapid prototyping devices. We demonstrate the versatility of our approach by designing a wide range of mechanical characters, several of which we manufactured using 3D printing. While our pipeline is designed for characters driven by planar mechanisms, significant parts of it extend directly to non-planar mechanisms, allowing us to create characters with compelling 3D motions.

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Computational design of mechanical characters

A proper tire friction model is essential to model overall vehicle dynamics for simulation, analysis, or control purposes since a ground vehicle's motion is primarily determined by the friction forces transferred from roads via tires. Motivated by the developments of high-performance antilock brake systems (ABSs), traction control, and steering systems, significant research efforts had been put into tire/road friction modeling during the past 40 years. In this paper, a review of recent developments and trends in this area is presented, with attempts to provide a broad perspective of the initiatives and multidisciplinary techniques for related research. Different longitudinal, lateral, and integrated tire/road friction models are examined. The associated friction-situation monitoring and control synthesis are discussed with a special emphasis on ABS design

Integrated longitudinal and lateral tire/road friction modeling and monitoring for vehicle motion control

Performance of EAs for four-bar linkage synthesis

A combined genetic algorithm-fuzzy logic method (GA-FL) in mechanisms synthesis

This work is intended to show the feasibility of the utilisation of a shear-thickening fluid as working fluid in a steering damper for motorcycles. To that end, a prototype of a steering damper has been designed and then tested under different working conditions. Unlike conventional models, this new steering damper bases its performance on the combination of very simple rod geometries and a shear-thickening fluid of different concentrations. The experiments carried out with a test machine demonstrate that, despite its simplicity and reduced cost of manufacturing, the prototype shows similar behaviour to a conventional high-performance racing steering damper. The Bouc–Wen model has been used to reproduce the behaviour of the shear-thickening fluid-based damper prototype. The parameters of the model have been obtained following an optimization process to fit the model’s response to the experimental data when exciting the damper at different speeds. Results show that the damper’s behaviour can be properly modelled with a single combination of parameters.

Design and testing of a steering damper for motorcycles based on a shear-thickening fluid

Advanced intelligent vehicle control systems have evolved in the last few decades thanks to the use of artificial-intelligence-based techniques, the appearance of new sensors, and the development of technology necessary for their implementation [...].

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Advances in Intelligent Vehicle Control

On-Line Learning Applied to Spiking Neural Network for Antilock Braking Systems

This paper presents a vehicle lateral controller based on spiking neural networks capable of replicating the behavior of a model-based controller but with the additional ability to perform online adaptation. By making use of neural plasticity and thanks to reward modulation learning, neural connections are modified to adjust the response according to the committed error. Therefore, the error performs a similar role to dopamine in a biological system, modulating the learning process based on spiking time dependency. The connections are initially set to replicate behavior of the model-based controller. Online adaptation allows tuning connection parameters to improve controller performance. A path controller with a preview is used as a baseline controller to evaluate the performance of the proposed approach. Key performance indicators are obtained from simulation with a step response and a handling track with 20 curves of different radii.

J.A. Cabrera

Papers

Design and testing of a steering damper for motorcycles based on a shear-thickening fluid

Advances in Intelligent Vehicle Control

On-Line Learning Applied to Spiking Neural Network for Antilock Braking Systems

Reward-modulated learning using spiking neural networks for vehicle lateral control