Research On Cluster Formation Control Algorithm For Biomimetic Robot Fish

The cluster formation control of biomimetic robotic fish is one of the main contents in the field of multi robot collaborative control,which can effectively improve the efficiency and stability of task execution.It plays an important role in underwater resource exploration,marine organism tracking and observation,sea safety inspection,collaborative encirclement,and other fields.However,in complex scenarios,the formation of robotic fish faces problems such as easy interference,path obstacles,and limited communication resources.Therefore,this paper studies a multi biomimetic robotic fish formation control method based on consistency and event driven mechanisms,based on the path tracking control of single robotic fish.The main content is as follows:(1)Research on path tracking control of bionic robotic fishTo address the difficulties in modeling and susceptibility to external environmental interference in the path tracking process of biomimetic robotic fish,a robotic fish path tracking control method based on an auto-disturbance rejection controller is adopted.Establish a mathematical model of the robotic fish,derive the robotic fish path tracking guidance law by introducing the Serret-Frenet equation and line of sight angle function,and implement the tuning of the guidance law based on an active disturbance rejection controller.The path tracking simulation experiment was conducted using straight lines and curves as reference paths.The results showed that the proposed controller can converge to the reference path with an error of0.1m in about 3 seconds,indicating that the proposed method has fast convergence speed,good control accuracy,and high stability.(2)Consistency-based formation control method for bionic robotic fishAiming at the formation,maintenance,and obstacle avoidance problems of bionic robotic fish formations,an improved consistency-based formation control algorithm with navigators is proposed.By introducing a weight term that varies with the position,the convergence speed of the follower robotic fish to the leader robotic fish is accelerated,achieving the formation and maintenance of multi-robotic fish clusters.To enable the robotic fish cluster to reach the target point in accordance with the preset formation in the presence of obstacles,this paper incorporates the improved artificial potential field method and dynamic formation transformation strategy into the consistency algorithm to achieve overall obstacle avoidance for the formation of robotic fish clusters.(3)Event-driven control method for formation of bionic robotic fishIn response to the problem of limited communication resources in the system of robotic fish formation,an event driven mechanism based biomimetic robotic fish formation control method is adopted.Design a distributed observer to estimate the relative state of robotic fish,then provide an event driven formation control algorithm,and prove that the event triggering function does not exhibit Zeno behavior.Numerical simulation results show that the state observer in this paper can effectively reconstruct the state of agents.Compared with the situation without event triggering,the update frequency of the system controller based on event driven strategy is about 0.15 s,compared with the update frequency of fixed time of 0.02 s,which greatly reduces the system’s occupation of communication resources.(4)Experimental verification of bionic robotic fish control systemTo verify the effectiveness of the above method,experimental verification of the bionic robotic fish control system is conducted.In the path tracking experiment verification,linear and curved path tracking experiments are conducted under interference conditions respectively.The results show that the path tracking control system based on self-adaptive controller has good anti-interference ability.In the formation control experiment verification,robotic fish formation experiments with and without obstacles and formation transformation are conducted.The experimental results show that the proposed algorithm can adapt to different environments and achieve the formation,maintenance,and obstacle avoidance of bionic robotic fish formations.