Trajectory Following and Improved Differential Evolution Solution for Rapid Forming of UAV Formation

Abstract

In this paper we proposed the circle trajectory assembly algorithm to control the multi-UAVs circular assembly formation. CTFAP solution provides rapid formation of UAVs on a circular orbit and solve the problem of large scattering distance. Proposed Distributed model prediction control framework improves the optimization ability and reduces the computation consumption with the better convergence ability of the UAV formation. Firstly, a circular trajectory following algorithm with an adaptive parameter is proposed to complete the rapid formation of UAVs on a circular orbit and solve the problem of large scattering distance during formation forming. Then, in the stage of formation reconfiguration, with distributed model prediction control framework (DMPC), the proposed method gets the prediction information of DMPC to optimize the population of classical differential evolution (DE) algorithm and improve the iterative optimization ability of DE algorithm. Experiments show that the proposed differential evolution algorithm greatly improves the efficiency of solving the formation reconfiguration problem under the DMPC framework and overcomes the disadvantages of random population of classical DE. For the proposed rapid forming method, assembly range is reduced by 41% compared with direct linearly formation assembly, and the formation forming time is reduced by approximately 21%. Compared with other optimization algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and DE, the proposed differential evolution algorithm reduces instruction response time of single-drones by 16%-30%.

A Quadral-Fuzzy Control Approach to Flight Formation by a Fleet of Unmanned Aerial Vehicles

“…Potential field and leader-follower are the two main approaches for formation control [12]. Hybrid approaches, combining both theories, are often used to build and move formations because they are effective, robust, and easy to handle [28]- [31]. In this paper, besides the use of fuzzy theory to develop intelligent controllers for drones motion and obstacle avoidance as usual in literature [15], [20], we combine potential field and leader-follower approaches to develop a fuzzy system to avoid the collisions in formation.…”

HDP Algorithms for Trajectory Tracking and Formation Control of Multi-Agent Systems

“…To conduct large-scale tasks, such as inspection of electric power transmission lines, monitoring leaks in gas and oil pipelines, country border patrolling, agriculture, and transportation, the use of multiple robots or agents is more feasible and economical than using a single agent [1], [2]. The control tasks are carried out by multiple agents simultaneously, and new control algorithms must support such framework [3].…”

Real-time tuning of PID controller based on optimization algorithms for a quadrotor

“…Particle Swarm Optimization (PSO) and Differential Evaluation (DE) algorithms have been used to optimize and improve the control systems of the quadrotor (Bian et al, 2019;Salamat and Tonello, 2019;Wufan Wang et al, 2016b). The present paper deals with improving a proportional, integral and derivative (PID) controller to increase the trajectory tracking performance for a quadrotor.…”

“…Quadrotors are widely used in aviation and engineering because of their simple structure, straightforward implementation, easy and flexible design(Liu et al, 2016). Particle Swarm Optimization (PSO) and Differential Evaluation (DE) algorithms have been used to optimize and improve the control systems of the quadrotor(Bian et al, 2019; Salamat and Tonello, 2019; Wufan Wang et al, 2016b). The present paper deals with improving a proportional, integral and derivative (PID) controller to increase the trajectory tracking performance for a quadrotor.In the past decades, trajectory tracking of the quadrotor has been a popular research topic in UAV control.…”