The traditional leader-follower formation algorithm can realize the formation of multiply robotic fishes, but fails to consider the energy consumption during the formation. In this paper, the energy optimized leader-follower formation algorithm has been investigated to solve this problem. Considering that the acceleration of robotic fish is tightly linked to the motion state and energy consumption, we optimize the corresponding control parameters of the acceleration to reduce energy consumption during the formation via particle swarm algorithm. The whole process has been presented as follows: firstly we realize the formation on the base of the kinematic model with leader-follower formation algorithm; then the energy consumption on the base of dynamical model are derived; finally we seek the optimal control parameters based on the particle swarm optimization (PSO) algorithm. The dynamics simulation of the energy optimization scheme is conducted to verify the functionality of the proposed energy optimized leader-follower formation algorithm via MATLAB. The optimized results demonstrate that the proposed approach, reducing energy consumption during the formation, is superior to the traditional leader-follower formation algorithm and can reduce energy consumption during the formation. The novelty of the work is that we can reduce the energy consumption during the process of formation by considering the energy consumption, which is a gap in the current research field.
This paper addresses finite-time projective synchronization of stochastic complex-valued neural networks (SCVNNs) with probabilistic time-varying delays (PTVs). First, in the complex domain, PTVs are introduced into the studied model and a novel feedback control scheme is constructed. Next, based on inequalities techniques and the Lyapunov stability approach, some novel projective synchronization criteria are established by decomposing SCVNNs into two equivalent real-valued systems. Moreover, a setting time function is created by employing lemma 4. Compared with previous researches, our theory content is an extension and complement to known results. Finally, numerical simulation is presented to validate the effectiveness of theoretical analysis results. INDEX TERMS Projective synchronization; Finite-time; Probabilistic time-varying delays; Stochastic complex-valued neural networks.
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