In this paper, a robust formulation for the structure-control design of mechatronic systems is developed. The proposed robust approach aims at minimization of the sensitivity of the nominal design objectives with respect to uncertain parameters. The robust integrated design problem is established as a nonlinear multiobjective dynamic optimization one, which in order to consider synergetic interactions uses mechanical and control nominal design objectives. A planar parallel robot and its controller are simultaneously designed with the proposed approach when the nominal design objectives are the tracking error and the manipulability measure. The payload at the end-effector is considered as the uncertain parameter. Experimental results show that a robustly designed parallel robot presents lower sensitivity of the nominal design objectives under the effects of changes at the payload than a nonrobustly designed one.

The present work deals with the use of a constraint-handling differential evolution algorithm to solve a nonlinear dynamic optimization problem (NLDOP) with 51 decision variables. A novel mechatronic design approach is proposed as an NLDOP, where both the structural parameters of a non-redundant parallel robot and the control parameters are simultaneously designed with respect to a performance criterion. Additionally, the dynamic model of the parallel robot is included in the NLDOP as an equality constraint. The obtained solution will be a set of optimal geometric parameters and optimal PID control gains. The optimal geometric parameters adjust the dynamic and the kinematic parameters, optimizing then, the link shapes of the robot. The proposed mechatronic design approach is applied to design simultaneously both the mechanical structure of a five-bar parallel robot and the PID controller.

In this paper, an improved harmony search (ImHS) algorithm is presented. HS is a simple but efficient metaheuristic method explored in recent literature, that simulates the process of musical improvisation. Two modifications for parameter tuning are proposed to enhance the algorithm performance in the solution of constrained numerical optimization problems, maintaining the simplicity of its original design. Metaheuristics are methods for solving optimization problems, and are based in two processes: exploration (diversification) and exploitation (intensification). The proposed modifications improve both processes in HS, without breaking their balance. A well-known ideal problem set was used as a reference to compare the efficiency of the developed algorithm ImHS with HS and three of its most successful variants, and also with two other metaheuristics of different nature, artificial bee colony (ABC) and modified ABC (MABC). Various techniques were applied to evaluate the algorithm performance with the proposed modifications, in order to validate the reliability of the comparison. In most case studies, ImHS far surpassed the results of HS and ABC, also improving the performance of the selected variants. Additionally, its results reached a similar quality than the obtained with MABC but with a significantly lower computational cost, suggesting that it can be a useful tool for solving real-world optimization problems if they are modeled as constrained numerical cases.

This paper presents the mechanical synthesis of a four-bar mechanism, its definition as a constrained optimisation problem in the presence of one dynamic constraint and its solution with a swarm intelligence algorithm based on the bacteria foraging process. The algorithm is adapted to solve the optimisation problem by adding a suitable constraint-handling technique that is able to incorporate a selection criterion for the two objectives stated by the kinematic analysis of the problem. Moreover, a diversity mechanism, coupled with the attractor operator used by bacteria, is designed to favour the exploration of the search space. Four experiments are designed to validate the proposed model and to test the performance of the algorithm regarding constraint-satisfaction, sub-optimal solutions obtained, performance metrics and an analysis of the solutions based on the simulation of the four-bar mechanism. The results are compared with those provided by four algorithms found in the specialised literature used to solve mechanical design problems. On the basis of the simulation analysis, the solutions obtained by the proposed algorithm lead to a more suitable design based on motion generation and operation quality.

An Isolated Microgrid (IMG) is an electrical distribution network combined with modern information technologies aiming at reducing costs and pollution to the environment. In this article, we implement the Bacterial Foraging Optimization Algorithm (BFOA) to optimize an IMG model, which includes renewable energy sources, such as wind and solar, as well as a conventional generation unit based on diesel fuel. Two novel versions of the BFOA were implemented and tested: Two-Swim Modified BFOA (TS-MBFOA), and Normalized TS-MBFOA (NTS-MBFOA). In a first experiment, the TS-MBFOA parameters were calibrated through a set of 87 independent runs. In a second experiment, 30 independent runs of both TS-MBFOA and NTS-MBFOA were conducted to compare their performance on minimizing the IMG using the best parameter tuning. Results showed that TS-MBFOA obtained better numerical solutions compared to NTS-MBFOA and LSHADE-CV, an Evolutionary Algorithm, found in the literature. However, the best solution found by NTS-MBFOA is better from a mechatronic point of view because it favors the lifetime of the IMG, resulting in economic savings in the long term.