Swarm control of an unmanned quadrotor model with LQR weighting matrix optimization using genetic algorithm

Endra Joelianto; Daniel Christian; Agus Samsi; Endra Joelianto; Daniel Christian; Agus Samsi

doi:10.14203/j.mev.2020.v11.1-10

Swarm control of an unmanned quadrotor model with LQR weighting matrix optimization using genetic algorithm

Endra Joelianto, Daniel Christian, Agus Samsi

Abstract

Unmanned aerial vehicle (UAV) quadrotors have developed rapidly and continue to advance together with the development of new supporting technologies. However, the use of one quadrotor has many obstacles and compromises the ability of a UAV to complete complex missions that require the cooperation of more than one quadrotor. In nature, one interesting phenomenon is the behaviour of several organisms to always move in flocks (swarm), which allows them to find food more quickly and sustain life compared with when they move independently. In this paper, the swarm behaviour is applied to drive a system consisting of six UAV quadrotors as agents for flocking while tracking a swarm trajectory. The swarm control system is expected to minimize the objective function of the energy used and tracking errors. The considered swarm control system consists of two levels. The first higher level is a proportional – derivative type controller that produces the swarm trajectory to be followed by UAV quadrotor agents in swarming. In the second lower level, a linear quadratic regulator (LQR) is used by each UAV quadrotor agent to follow a tracking path well with the minimal objective function. A genetic algorithm is applied to find the optimal LQR weighting matrices as it is able to solve complex optimization problems. Simulation results indicate that the quadrotors' tracking performance improved by 36.00 %, whereas their swarming performance improved by 17.17 %.

10.14203/j.mev.2020.v11.1-10

Keywords

unmanned aerial vehicle (UAV); quadrotor model; swarm model; proportional – derivative (PD) controller; linear quadratic regulator (LQR); optimization problems; genetic algorithm.

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