Mechatronic Design Evolution Using Bond Graphs and Hybrid Genetic Algorithm With Genetic Programming

Abstract

A typical mechatronic problem (modeling, identification, and design) entails finding the best system topology as well as the associated parameter values. The solution requires concurrent and integrated methodologies and tools based on the latest theories. The experience on natural evolution of an engineering system indicates that the system topology evolves at a much slower rate than the parametric values. This paper proposes a two-loop evolutionary tool, using a hybrid of genetic algorithm (GA) and genetic programming (GP) for design optimization of a mechatronic system. Specifically, GP is used for topology optimization, while GA is responsible for finding the elite solution within each topology proposed by GP. A memory feature is incorporated with the GP process to avoid the generation of repeated topologies, a common drawback of GP topology exploration. The synergic integration of GA with GP, along with the memory feature, provides a powerful search ability, which has been integrated with bond graphs (BG) for mechatronic model exploration. The software developed using this approach provides a unified tool for concurrent, integrated, and autonomous topological realization of a mechatronic problem. It finds the best solution (topology and parameters) starting from an abstract statement of the problem. It is able to carry out the process of system configuration realization, which is normally performed by human experts. The performance of the software tool is validated by applying it to mechatronic design problems.

Adaptive Bi-objective Genetic Programming for Data-Driven System Modeling

“…whereô k = f (i k ) is the estimated output calculated by that tree when fed with the k-th sample. The fitness of the tree is then its RMSE, and conventional GP algorithms simply try to find the minimum-RMSE tree [1].…”

Velocity control of a washing machine: A mechatronic approach

“…In this region (see 4 Though the model presented in Section 3 and the theoretical derivation make always reference to the motor velocity, as it is the actual measured and controlled variable, the curve of the critical gains is always shown as a function of the drum velocity, as this quantity conveys the idea of the machine operating condition.…”

“…In fact, a mechatronic model is a powerful design tool that allows to predict the effect of design choices made at component level on the machine performance [1][2][3][4][5].…”

Concurrent optimization on the powertrain of robot manipulators for optimal motor selection and control in a point-to-point trajectory planning

“…The constrained multi-objective optimization problem with a concurrent approach is solved by a Non-dominated Sorting Genetic Algorithm-II (NSGA-II algorithm 15 ), since it is an evolutionary algorithm widely used to deal with multi-objective optimization problems, 16 and has been successfully used for path planning 4,10,[17][18][19] and concurrent design. [20][21][22] This article is organized as follows. Section ''Pointto-point trajectory planning strategy'' describes the applied point-to-point planning method.…”