Optimization of the Dimensionless Model of an Electrostatic Microswitch Based on AMGA Algorithm

Santana-Cabrera, Jorge; Monzón-Verona, José Miguel; Santana-Martín, Francisco Jorge; García-Alonso, Santiago; Montiel–Nelson, Juan A.

doi:10.1007/978-3-319-11541-2_33

Cited by 1 publication

(1 citation statement)

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“…Archive-based micro genetic algorithm (AMGA), one of the intelligent algorithms, is particularly suitable for solving highly non-linear multi-constraint multi-objective optimizations. [28][29][30] The optimizing process and default setting for Isight are shown in Figure 9 and Table 2, respectively. Based on the above discussion, the optimum parameters can be concluded in Table 3.…”

Section: Optimization Resultsmentioning

confidence: 99%

Design and optimization of an elastic linkage quadruped robot based on workspace and tracking error

Ren

Liu

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part C:

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High acceleration and extreme load are frequently appeared on high-speed locomotion of legged robot’s legs, imposing a challenging trade-off between weight and torque in leg design. This paper proposes a new design paradigm based on cable-drive and elastic linkage to solve the problem. The details of the design procedure are given, including the construction of the single leg. With the optimum design of the linkage mechanism, a combined index of the workspace and tracking error are used as object function, and taking geometrical design parameters of the linkage as optimization parameters. Based on the target workspace and the spring-loaded inverted pendulum model, the best foot trajectory in obstacle climbing and trotting gait are analyzed and illustrated. This paper built linkage cable-drive spring robot based on the legged module integration. Simulations and experiments indicate that linkage cable-drive spring robot performs stable trotting with control of the spring-loaded inverted pendulum model. Linkage cable-drive spring robot prototype experiments results are provided to verify the validity of the new method.

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Section: Optimization Resultsmentioning

confidence: 99%