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1

A study of dynamic hysteresis model for a magnetorheological damper

Chen Kun-Yung, Kung Ming-Lung

Journal of Theoretical and Applied Mechanics

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2023

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Vol. 61 nr 2

259--274

EN

The paper proposes a new dynamic model based on the LuGre model and an electrical equation to describe the hysteresis phenomenon for a magnetorheological (MR) damper. In addition, a sliding mode observer (SMO) is proposed to estimate unmeasurable states of the MR damper. The parameters of the MR damper are successfully identified by using the self-learning particle swarm optimization (SLPSO) algorithm. The contributions of this paper are: i) a new dynamic model based on the LuGre model and an electrical equation for an MR damper is successfully formulated to fit for the hysteresis behavior, ii) the exerted damping force can be practically adjusted by using input voltage for the dynamic model, iii) the SMO is proposed to estimate the internal states and current, and iv) the unknown parameters of the MR damper are successfully identified by using the SLPSO algorithm with a numerical experiment.

2

An Ant Algorithm for the Sudoku Problem

Schiff K.

Journal of Automation Mobile Robotics and Intelligent Systems

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2015

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Vol. 9, No. 2

24--27

EN

In this paper an ant algorithm for the Sudoku problem is presented. This is the first ant algorithm enabling discovery of an optimal solution to the Sudoku puzzle for 100% of investigated cases. The Sudoku is a one of many combinatorial optimisation problems, as well as an NPcomplete problem, hence an ant algorithm which constructs an optimal solution as a meta-heuristic method is important for this problem.

3

Application of the Bee Swarm Optimization BSO to the Knapsack Problem

Sotelo-Figueroa M. A., Baltazar B., Carpio J. M.

Journal of Automation Mobile Robotics and Intelligent Systems

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2011

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Vol. 5, No. 1

101-114

EN

Swarm Intelligence is the part of Artificial Intelligence based on study of actions of individuals in various decentralized systems. The optimization algorithms which are inspired from intelligent behavior of honey bees are among the most recently introduced population based techniques. In this paper, a novel hybrid algorithm based in Bees Algorithm and Particle Swarm Optimization is applied to the Knapsack Problem. The Bee Algorithm is a new population-based search algorithm inspired by the natural foraging behavior of honey bees, it performs a kind of exploitative neighborhood search combined with random explorative search to scan the solution, but the results obtained with this algorithm in the Knapsack Problem are not very good. Although the combination of BA and PSO is given by BSO, Bee Swarm Optimization, this algorithm uses the velocity vector and the collective memories of PSO and the search based on the BA and the results are much better. We use the Greedy Algorithm, which it's an approximate algorithm, to compare the results from these metaheuristics and thus be able to tell which is which gives better results.

4

Tracking Exercise Motions of Older Adults Using Contours

Havens T C, Alexanderson G L, Abbott C C, Keller J M, Skubic M, Rantz M

Journal of Applied Computer Science Methods

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2009

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Vol. 1 No. 2

21--42

EN

In this paper we describe the development of a novel markerless motion capture system and explore its use in documenting elder exercise routines in a health club. This system uses image contour tracking and swarm intelligence methods to track the location of the spine and shoulders during three exercises — treadmill, exercise bike, and overhead lateral pull-down. Validation results show that our method has a mean error of approximately 2 degrees when measuring the angle of the spine or shoulders relative to the horizontal. Qualitative study results demonstrate that our system is capable of providing important feedback about the posture and stability of elders while they are performing exercises. Study participants indicated that feedback from our system would add value to their exercise routines.