Performance Comparison of LQR Versus a Novel Hybrid Optimized PID Controller for Inverted Pendulum Stabilization

Authors

  • Aleisawee Alsseid College of Electronic Technology - Bani Walid Author
  • Issa Eldbib College of Electronic Technology, Bani Walid, Author
  • Nehal A. Alsseid College of Electronic Technology, Bani Walid, Author
  • Shefaallah Melad College of Electronic Technology, Bani Walid, Author

DOI:

https://doi.org/10.65568/gujes.2026.020107

Keywords:

PID control, LQR, Particle Swarm Optimization, Genetic Algorithm, Hybrid metaheuristics, Inverted pendulum, Transient performance, Stability analysis

Abstract

This study presents a comparative analysis of control strategies for the stabilization of an inverted pendulum system. It evaluates a traditional Linear-Quadratic Regulator (LQR) against PID controllers optimized using metaheuristic algorithms, including Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). Furthermore, the investigation introduces and assesses novel hybrid controllers that combine the PSO and GA optimization techniques. The performance of all five control strategies was tested and compared through MATLAB/Simulink simulations, which included conditions with external disturbances to evaluate robustness. The results demonstrate that while all controllers successfully stabilized the system, the hybrid metaheuristic approach yielded a notably superior performance in terms of transient response. The study concludes that hybrid metaheuristic algorithms represent a highly promising method for controlling complex, nonlinear systems, particularly in applications where traditional control techniques are limited by their dynamic performance.

References

[1] N. Shiroma, O. Matsumoto, S. Kajita, and K. Tani. Cooperative behavior of a wheeled inverted pendulum for object transportation. In Intelligent Robots and Systems ’96, IROS 96, Proceedings of the 1996 IEEE/RSJ International Conference on, volume 2, pages 396–401 vol.2, Nov 1996.

[2] M. Shivakumar M. Stafford, M. Basavanna. Two wheeled balancing autonomous robot. Int. J. of Advanced Research in Computer and Comm. Eng., 4(11):119–122.

[3]. Ullah S, Mehmood A, Ali K, Javaid U, Hafeez G, Ahmad E. Dynamic Modeling and Stabilization of Surveillance Quadcopter in Space based on Integral Super Twisting Sliding Mode Control Strategy. In2021 International Conference on Artificial Intelligence (ICAI) 2021 Apr 5 (pp. 271-278).

[4 F. Grasser, A. D’Arrigo, S. Colombi, and A. C. Rufer. Joe: a mobile, inverted pendulum. IEEE Transactions on Industrial Electronics, 49(1):107–114, Feb 2002.

[5] Hung, C.C., Fernandez, B.R.: ‘Comparative analysis of control design techniques for a cart-inverted-pendulum in real-time implementation’. Proc. American Control Conf., June 1993

[6]. Munir M, Khan Q, Ullah S, Syeda TM, Algethami AA. Control Design for Uncertain Higher-Order Networked

Nonlinear Systems via an Arbitrary Order Finite-Time Sliding Mode Control Law. Sensors. 2022 Apr 2; 22(7):2748. https://doi.org/10.3390/s22072748 PMID: 35408362

[7]. Singh, G., & Singla, A. Modeling, analysis and control of a single stage linear inverted pendulum. IEEE International Conference on Power, Control, Signals and Instrumentation Engineering, ICPCSI 2017, (pp. 2728–2733).

[8]. Fahmizal, F., Arrofiq, M., Adrian, R., & Mayub, A. (2019). Robot Inverted Pendulum Beroda Dua (IPBD) dengan Kendali Linear Quadratic Regulator (LQR). ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika, 7(2), 224.

[9 Lin, Z., Saberi, A., Gutmann, M., Shamash, Y.A.: ‘Linear controller for an inverted pendulum having restricted travel: a high-and-low gain approach’, Automatica, 1996, 32, pp. 933–937

[10] A. Kharola, P. Patil, PID control of two-stage inverted pendulum. IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), DOI. 10.1109/ICCIC.2016.7919521.

[11] Naidu, D.S.: ‘Optimal control systems’ (CRC Press, FL, 2003)

[12]. Erkol, H. O. Linear Quadratic Regulator Design for Position Control of an Inverted Pendulum by Grey Wolf Optimizer. International Journal of Advanced Computer Science and Applications, 9(4).

[13]. Shuang, L., & Jian, F. Linear quadratic optimal controller design an inverted pendulum. Proceedings International Symposium on Computer, Consumer and Control, IS3C 2014, 416–418.

[14] Krishnakumar, K., Goldberg, D.E.,. Control system optimization using genetic algo-rithms. J. Guid. Control Dyn. 1992, 15, 735–740.

[15] Sreekanth, P., Hari, A., 2016. Genetic algorithm based self tuning regulator for ball and hoop system. In: 2016 Conference on Emerging Devices and Smart Systems. ICEDSS.IEEE, pp. 147–152.

[16] Wang, Q., Spronck, P., Tracht, R., 2003. An overview of genetic algorithms applied tocontrol engineering problems. In: Proceedings of the 2003 Interna.Con.onMachineLearningandCybernetics(IEEECat.No.03EX693).In:IEEE,vol.3, pp.1651–1656.

[17] Kennedy, J., Eberhart, R., 1995. Particle swarm optimization. In: Proceedings of ICNN’95-InternationalConferenceonNeuralNetworks.In:IEEE,vol.4,pp.1942–1948.

[18]deAlmeida,B.S.G.,Leite,V.C.,2019.Particleswarmoptimization:apowerfultechniquefor solving engineering problems. In: Ser, J.D., Villar, E., Osaba, E. (Eds.), SwarmIntelligence. IntechOpen, Rijeka. Chapter 3.

[19] Kim, D.H., Cho,J.H.,2006. A biologically inspired intelligent PID controller tuning forAVR systems. Int. J. Control. Autom. Syst. 4, 624–636.

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Published

2026-03-15

Issue

Vol. 2 No. 1 (2026): Gharyan University Journal of Engineering Sciences (GUJES)

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