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This paper considers the formation control problem for a group of unmanned aerial vehicles( UAVs)employing consensus with different optimizers. A group of UAVs can never accomplish difficult tasks without formation because if disordered they do not work any better than a single vehicle,and a single vehicle is limited by its undeveloped intelligence and insufficient load. Among the many formation methods,consensus has attracted much attention because of its effectiveness and simplicity. However,at the beginning of convergence,overshoot and oscillation are universal because of the limitation of communication and a lack of forecasting,which are inborn shortcomings of consensus. It is natural to modify this method with lots of optimizers. In order to reduce overshoot and smooth trajectories, this paper first adopted particle swarm optimization( PSO), then pigeon-inspired optimization( PIO) to modify the consensus. PSO is a very popular optimizer,while PIO is a new method,both work but still retain disadvantages such as residual oscillation. As a result,it was necessary to modify PIO,and a pigeon-inspired optimization with a slow diving strategy( SD-PIO) is proposed. Convergence analysis was performed on the SD-PIO based on the Banach fixed-point theorem and conditions sufficient for stability were achieved.Finally,a series of comparative simulations were conducted to verify the feasibility and effectiveness of the proposed approach.
This paper considers the formation control problem for a group of unmanned aerial vehicles (UAVs) employing consensus with different optimizers. A group of UAVs can not accomplish difficult tasks without formation because if disordered they do not work any better than a single vehicle, and a single the vehicle is limited by its undeveloped intelligence and insufficient load. Among the many formation methods, consensus has attracted much attention because of its effectiveness and simplicity. However, at the beginning of convergence, overshoot and oscillation are universal because of the limitation of communication and a lack of forecasting, which are inborn shortcomings of consensus. This is natural to modify this method with lots of optimizers. This is the first to modify the method with lots of optimizers. This paper first adopted particle swarm optimization (PSO), then pigeon-inspired optimization PIO) to modify the consensus. PSO is a very popular optimizer, while PIO is a new method, both work but stil As a result, it was necessary to modify PIO, and a pigeon-inspired optimization with a slow diving strategy (SD-PIO) is proposed. Convergence analysis was performed on the SD-PIO based on the Banach fixed-point theorem and conditions sufficient for stability were achieved. Finaally, a series of comparative simulations were conducted to verify the feasibility and effectiveness of the proposed approach.