IEEE Transactions on Neural Networks and Learning Systems   2016 年 27 卷 9 期

2016.09.01

In this brief, we investigate the parameter optimization issue of a central pattern generator (CPG) network governed forward and backward swimming for a fully untethered, multijoint biomimetic robotic fish. Considering that the CPG parameters are tightly linked to the propulsive performance of the robotic fish, we propose a method for determination of relatively optimized control parameters. Within the framework of evolutionary computation, we use a combination of dynamic model and particle swarm optimization (PSO) algorithm to seek the CPG characteristic parameters for an enhanced performance. The PSO-based optimization scheme is validated with extensive experiments conducted on the actual robotic fish. Noticeably, the optimized results are shown to be superior to previously reported forward and backward swimming speeds.