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PTTIPTTI

Journal Journal of Fuzzy Systems and ControlJournal of Fuzzy Systems and Control Abstract

Rotary Inverted Pendulum (RIP) is a classical but effective model in testing control algorithms. Besides designing controllers, it can also be a model for testing the evolution algorithms (EAs) in optimizing control parameters. In this paper, we apply particle swarm optimization (PSO), which is an EA, to optimize the parameters of the LQR controller for this model. In the study, an experimental model in which system parameters are already measured and identified in former studies is used. The LQR control method is inherited from former results, and the weighing matrices (Q and R) are optimized by the PSO method. In each case, the control matrix K is obtained from Q and R to apply for RIP. Through both simulation and experiment, LQR control parameters are found better through generations by using PSO. The responses of RIP, in which controllers are designed under optimized Q and R in later generations, are better in quality, and values of the fitness function also supports that opinion. Thence, through this study, beside genetic algorithm (GA), this study proves that PSO is a suitable searching algorithm that can be applied for balancing this single input- multi output (SIMO) system. Also, the experimental platform of RIP in this research confirms its ability to control tests.

Conclusion

Through the paper, optimized-LQR controllers based on PSO improve the response of a Rotary Inverted Pendulum (RIP) compared to a standard LQR controller, demonstrating shorter settling times and reduced oscillation.These improvements are quantifiable through a lower fitness function value.The study confirms PSOs effectiveness in optimizing control parameters, specifically the Q and R matrices, and demonstrates the ability to adjust optimization focus using a weighting constant to prioritize specific variables.Ultimately, this research validates PSO as a successful method for enhancing control quality in RIP systems.

Future Research

Penelitian lebih lanjut dapat dilakukan untuk mengeksplorasi penggunaan algoritma optimasi lain, seperti algoritma genetika atau optimasi koloni semut, untuk membandingkan efektivitasnya dengan PSO dalam mengoptimalkan kontrol LQR pada RIP. Selain itu, studi dapat diperluas untuk menguji ketahanan kontrol yang dioptimalkan terhadap variasi parameter sistem dan gangguan eksternal, sehingga menghasilkan sistem kontrol yang lebih robust dan adaptif. Terakhir, penelitian dapat difokuskan pada pengembangan platform RIP yang lebih canggih dengan sensor dan aktuator yang lebih presisi, untuk memungkinkan pengujian kontrol yang lebih akurat dan realistis, serta membuka peluang untuk aplikasi kontrol yang lebih kompleks.

References

1. Modelling of the rotary inverted pendulum system | IEEE Conference Publication | IEEE Xplore. modelling... ieeexplore.ieee.org/document/4588810Modelling of the rotary inverted pendulum system IEEE Conference Publication IEEE Xplore modelling ieeexplore ieee document 4588810
2. Ranging in a Single-Input Multiple-Output (SIMO) System | IEEE Journals & Magazine | IEEE Xplore.... ieeexplore.ieee.org/document/4471960Ranging in a Single Input Multiple Output SIMO System IEEE Journals Magazine IEEE Xplore ieeexplore ieee document 4471960
3. Control of DC Motor Using Genetic Algorithm Based PID Controller | IEEE Conference Publication | IEEE... doi.org/10.23919/ICUE-GESD.2018.8635662Control of DC Motor Using Genetic Algorithm Based PID Controller IEEE Conference Publication IEEE doi 10 23919 ICUE GESD 2018 8635662
4. Particle Swarm Optimization: A Comprehensive Survey | IEEE Journals & Magazine | IEEE Xplore. particle... doi.org/10.1109/ACCESS.2022.3142859Particle Swarm Optimization A Comprehensive Survey IEEE Journals Magazine IEEE Xplore particle doi 10 1109 ACCESS 2022 3142859

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