Sistem Kontrol Swarm untuk Flocking Wahana NR-Awak Quadrotor dengan Optimasi Algoritma Genetik

Penulis

  • Endra Joelianto Institut Teknologi Bandung, Bandung
  • Winarendra Satya Rajasa Institut Teknologi Bandung, Bandung
  • Agus Samsi Institut Teknologi Bandung, Bandung

DOI:

https://doi.org/10.25126/jtiik.2021863467

Abstrak

Quadrotor merupakan wahana udara nir-awak jenis lepas landas atau pendaratan vertikal berbentuk silang dan memiliki sebuah rotor pada setiap ujung lengannya dengan kemampuan manuver yang tinggi. Swarm quadrotor yang terdiri dari sekumpulan quadrotor akan menjadi suatu swarm yang baik, sesuai dengan kriteria swarm oleh Reynold yaitu dapat menghindari tumbukan, menyamakan kecepatan, dan pemusatan swarm. Pengontrolan swarm quadrotor memiliki tingkat kerumitan yang tinggi karena melibatkan banyak agen. Riset pengembangan swarm quadrotor masih belum banyak dilakukan dan masih membuka peluang untuk meneliti dengan metoda lain yang lebih baik dalam menghasilkan swarm. Makalah ini mengusulkan pengontrolan swarm quadrotor yang terdiri dari dua tingkat lup kontrol. Lup pertama adalah pengontrol sistem model swarm untuk membangkitkan lintasan swarm dan lup kedua merupakan pengontrol pada quadrotor untuk melakukan penjejakan lintasan swarm. Pengontrol pertama menggunakan pengontrol proporsional derivatif (PD), sedangkan pengontrol kedua menggunakan regulator linier kuadratik (RLK). Pengontrol yang dirancang memiliki parameter yang banyak, sehingga pemilihan parameter yang optimal sangat sulit. Pencarian parameter optimal pada pengontrol model swarm quadrotor membutuhkan teknik optimasi seperti algoritma genetik (AG) untuk mengarahkan pencarian menuju solusi yang menghasilkan kinerja terbaik. Pada makalah ini, penalaan dengan optimasi AG hanya dilakukan pada pengontrol PD untuk menghasilkan lintasan swarm terbaik, sedangkan matrik bobot RLK dilakukan secara uji coba. Hasil simulasi swarm pada model quadrotor menunjukkan parameter , . , dan  yang diperoleh menggunakan AG menghasilkan pergerakan swarm yang baik dengan kesalahan RMS pelacakan 0,0094 m terhadap fungsi obyektif. Sedangkan ketika parameter ,  dan  dicari menggunakan AG, tidak berpengaruh banyak dalam memperbaiki hasil simulasi swarm quadrotor.

 

Abstract

The quadrotor is a type of take-off or vertical landing unmanned aerial vehicles with a cross shape and has one rotor at each end of its arm with high maneuverability. A quadrotor swarm consisting of a group of quadrotors leads to a good swarm, according to Reynold's swarm criteria, which accomplishes collision avoidance, velocity matching, and flock centering. Quadrotor swarm control has a high level of complexity because it involves many agents. Research on the development of quadrotor swarm has received insignificant attention and it still opens opportunities to research other methods that are better at producing swarm. The paper proposes the control of a quadrotor swarm consisted of two levels of control loops. The first loop controls the swarm model system to generate the swarm trajectory and the second loop is the controller on the quadrotor to track the swarm path. The first controller uses a proportional derivative controller (PD), while the second controller uses the linear quadratic regulator (LQR). The controller that is designed has many parameters, so the optimal parameter selection is very difficult. The search for optimal parameters in the swarm model controller requires optimization techniques such as the genetic algorithm (GA) to direct the search for solutions that produce the best performance. In this paper, tuning with the optimization of GA is only done for the PD controller in order to produce the best swarm trajectory, while the weight matrices of the LQR are done on a trial error basis. Swarm simulation results of a quadrotor model system show the parameters , . , and  obtained using GA produce a good swarm movement with RMS error 0.0094 m of the objective function. Whereas when parameters ,  and  are searched using GA, it does not have much effect in improving the quadrotor swarm simulation results.


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Referensi

BLUM, C. & MERKLE, D., 2008. Swarm intelligence in optimization. In: Swarm intelligence, pp. 43-85, Berlin, Heidelberg: Springer.

BUROHMAN, A.M., WIDYOTRIATMO, A. & JOELIANTO, E., 2016. Flocking for nonholonomic robots with obstacle avoidance. In 2016 International Electronics Symposium (IES), IEEE, pp. 345-350, September.

CALVEZ, B. & HUTZLER, G., 2005. Automatic tuning of agent-based models using genetic algorithms. In International Workshop on Multi-Agent Systems and Agent-Based Simulation Springer, Berlin, Heidelberg, pp. 41-57, July.

CAO, L., TANG, S. & ZHANG, D., 2017. Flight control for air-breathing hypersonic vehicles using linear quadratic regulator design based on stochastic robustness analysis. Frontiers of Information Technology & Electronic Engineering, 18(7), pp. 882-897.

CHAKRABORTY, R.C., 2010. Fundamentals of genetic algorithms. Reproduction, 22, p.35.

CHOUTRI, K., LAGHA, M., DALA, L. & LIPATOV, M., 2018. Quadrotors UAVs swarming control under Leader-Followers formation. In 2018 22nd International Conference on System Theory, Control and Computing (ICSTCC), IEEE, pp. 794-799.

DAS, D., GURRALA, G. & SHENOY, U. J., 2016. Linear quadratic regulator-based bumpless transfer in microgrids. IEEE Transactions on Smart Grid, 9(1), pp. 416-425.

DE LELLIS COSTA DE OLIVEIRA, M., 2011. M.S. Thesis: Modeling, Identification, and Control of a Quadrotor Helicopter, Czech Technical University in Prague, Prague, Ukraine.

FAELDEN, G.E.U., VICERRA, R.R.P., LIM, L.A.G., SYBINGCO, E., DADIOS, E.P. & BANDALA, A.A., 2017. Implementation of Swarm Social Foraging Behavior in Unmanned Aerial Vehicle (UAV) Quadrotor Swarm. Journal of Advanced Computational Intelligence and Intelligent Informatics, 21(2), pp. 197-204.

FERRANTE, E., TURGUT, A.E., HUEPE, C., STRANIER, A., PINCIROLI, C. & DORIGO, M., 2008. Self-Organized Flocking with a Mobile Robot Swarm. 7th ed. Padgham, Parkes, Müller and Parsons. Portugal, pp. 39-46.

FERRANTE, E., TURGUT, A.E., HUEPE, C., STRANIERI, A., PINCIROLI, C. & DORIGO, M., 2012. Self-organized flocking with a mobile robot swarm: a novel motion control method. Adaptive Behavior, 20(6), pp. 460-477.

GAZI, V. & PASSINO, K.M., 2003. Stability Analysis of Swarms. IEEE Transactions on Automatic Control, 48(4), pp. 692-697.

GAZI, V. & PASSINO, K.M., 2004a. Stability Analysis of Social Foraging Swarms. IEEE Transactions on System, Man and Cybernetics, 34(1), pp. 539-557.

GAZI, V. & PASSINO, K.M., 2004b. A Class of Attractions-Repulsion Functions for Stable Swarm Aggregations. International Journal of Control, 77(18), pp.1567-1579.

GE, Z., WANG, Y. & LV, M., 2018. Three-dimensional Trajectory Tracking Guidance Law Based on Linear Quadratic Regulator. Journal of Physics: Conference Series. 1039(1), p. 012042. IOP Publishing.

HAKIIM, K., DHARMAWAN, A. & FAIZAH, F., 2017. Optimasi Kendali PID menggunakan Algoritma Genetika untuk Penerbangan Quadrotor. IJEIS (Indonesian Journal of Electronics and Instrumentation Systems), 7(2), pp. 173-184.

HALCI, B., GAZI, V. & CIHAN, O., 2019. Modelling and Coordination of a Swarm of Quadrotors Using Lagrange Dynamics and Potential Functions. In 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), IEEE, pp. 963-970.

HÖNIG, W., PREISS, J.A., KUMAR, T.K. S., SUKHATME, G.S. & AYANIAN, N., 2018. Trajectory planning for quadrotor swarms. IEEE Transactions on Robotics, 34(4), pp. 856-869.

İÇEN, M., ATEŞ, A. & YEROĞLU, C., 2017. Optimization of LQR weight matrix to control three degree of freedom quadcopter. In 2017 International Artificial Intelligence and Data Processing Symposium (IDAP), IEEE, pp. 1-6.

JOELIANTO, E., 2017. Linear Quadratic Control: A State Space Approach. Bandung: ITB Press.

JOELIANTO, E. & QURTHOBI, A., 2011. Optimal Control Design for A Formation Tracking with Leader-Follower Concept of Multi-Agent Autonomous Helicopter Model. In Proceedings of International Conference on Intelligent Unmanned Systems (Vol. 7).

JOELIANTO, E. & SAGALA, A., 2012. Swarm tracking control for flocking of a multi-agent system. In 2012 IEEE Conference on Control, Systems & Industrial Informatics. IEEE, pp. 75-80, September.

KRAMER, O., 2017. Genetic algorithm essentials (Vol. 679). Springer.

KUSHLEYEV, A., MELLINGER, D., POWERS, C. & KUMAR, V., 2013. Towards a swarm of agile micro quadrotors. Autonomous Robots, 35(4), pp. 287-300.

LAZIM, I.M., HUSAIN, A.R., MOHD SUBHA, N.A., MOHAMED, Z. & MOHD BASRI, M. A., 2017. Optimal formation control of multiple quadrotors based on particle swarm optimization. In Asian Simulation Conference, pp. 121-135. Springer, Singapore.

LEONARD, J., SAVVARIS AL. & TSOURDOS, A., 2012. Towards a fully autonomous swarm of unmanned aerial vehicles. In Proceedings of 2012 UKACC International Conference on Control, IEEE. pp. 286-291.

MARADA, T., MATOUSEK, R. & ZUTH, D., 2017. Design of linear quadratic regulator (LQR) based on genetic algorithm for inverted pendulum. Mendel, 23(1), pp. 149-156.

MIRJALILI, S., 2019. Genetic algorithm. In Evolutionary algorithms and neural networks (pp. 43-55). Springer, Cham.

MOHAMMED, I.K. & ABDULLA, A.I., 2018. Design of optimised linear quadratic regulator for capsule endoscopes based on artificial bee colony tuning algorithm. International Journal for Engineering Modelling, 31(1-2), pp. 77-98.

OKYERE, E., BOUSBAINE, A., POYI, G.T., JOSEPH, A.K. & ANDRADE, J.M., 2019. LQR controller design for quad-rotor helicopters. The Journal of Engineering, 17, pp. 4003-4007.

PASSINO, K.M., 2005. Biomimicry for optimization, control, and automation. London: Springer-Verlag.

POSSIERI, C., SASSANO, M., GALEANI, S. & TEEL, A.R., 2020. The linear quadratic regulator for periodic hybrid systems. Automatica, 113, 108772.

REYNOLDS, C.W., 1987, August. Flocks, herds and schools: A distributed behavioral model. In Proceedings of the 14th annual conference on Computer graphics and interactive techniques, pp. 25-34, August.

SUDIYANTO, T., MULJOWIDODO, M., & BUDIYONO, A., 2009. First principle approach to modeling of primitive quad rotor. International Journal of Aeronautical and Space Sciences, 10(2), pp.148-160.

TRIZULJAK, A., DUCHOŇ, F., RODINA, J., BABINEC, A., DEKAN, M. & MYKHAILYSHYN, R., 2019. Control of a small quadrotor for swarm operation. Journal of Electrical Engineering, 70(1), pp. 3-15.

TURGUT, A.E., ÇELIKKANAT, H., GÖKÇE, F., & ŞAHIN, E., 2008. Self-organized flocking in mobile robot swarms. Swarm Intelligence, 2(2-4), pp. 97-120.

WIDYOTRIATMO, A., JOELIANTO, E., PRASDIANTO, A., BAHTIAR, H. & NAZARUDDIN, Y.Y., 2017. Implementation of Leader-Follower Formation Control of a Team of Nonholonomic Mobile Robots. International Journal of Computers Communications & Control, 12(6), pp.871-885.

Diterbitkan

24-11-2021

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Sistem Kontrol Swarm untuk Flocking Wahana NR-Awak Quadrotor dengan Optimasi Algoritma Genetik. (2021). Jurnal Teknologi Informasi Dan Ilmu Komputer, 8(6), 1089-1098. https://doi.org/10.25126/jtiik.2021863467