Distributed and Asynchronous Population-Based Optimization Applied to the Optimal Design of Fuzzy Controllers

Abstract

Designing a controller is typically an iterative process during which engineers must assess the performance of a design through time-consuming simulations; this becomes even more burdensome when using a population-based metaheuristic that evaluates every member of the population. Distributed algorithms can mitigate this issue, but these come with their own challenges. This is why, in this work, we propose a distributed and asynchronous bio-inspired algorithm to execute the simulations in parallel, using a multi-population multi-algorithmic approach. Following a cloud-native pattern, isolated populations interact asynchronously using a distributed message queue, which avoids idle cycles when waiting for other nodes to synchronize. The proposed algorithm can mix different metaheuristics, one for each population, first because it is possible and second because it can help keep total diversity high. To validate the speedup benefit of our proposal, we optimize the membership functions of a fuzzy controller for the trajectory tracking of a mobile autonomous robot using distributed versions of genetic algorithms, particle swarm optimization, and a mixed-metaheuristic configuration. We compare sequential versus distributed implementations and demonstrate the benefits of mixing the populations with distinct metaheuristics. We also propose a simple migration strategy that delivers satisfactory results. Moreover, we compare homogeneous and heterogenous configurations for the populations’ parameters. The results show that even when we use random heterogeneous parameter configuration in the distributed populations, we obtain an error similar to that in other work while significantly reducing the execution time.

Design, Simulation, Implementation, and Comparison of Advanced Control Strategies Applied to a 6-DoF Planar Robot

“…where τ ∈ R 6 is the vector for external forces or torques applied by the actuators over the robot's joints; τ 0 ∈ R 6 is the vector for unknown input disturbances; M ∈ R 6×6 is the inertia matrix, with symmetry with respect to the main diagonal; C ∈ R 6×6 is the centrifuge and Coriolis force matrix; g ∈ R 6 is the vector for the gravitational forces, of (6 × 1) dimensions; f ∈ R 6 is the friction forces vector; and q, q, q ∈ R 6 are the joint position, speed, and acceleration vectors, respectively. The results of the motion equations of the robot dynamic model are presented in Appendix A.…”

“…In this sense, Fuzzy-Logic Control (FLC) has been one of the most-successful intelligent control techniques from the earlier Mamdani to more recent applications. The main advantage is that the mentioned controller does not depend on the dynamics of the manipulator robot [6]. Nevertheless, the effectiveness of FLC can be improved if the dynamic modeling is incorporated.…”