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"Sistem coupled tank merupakan salah contoh penerapan sistem kontrol level industri yang memiliki karakteristik yang kompleks dengan non linieritas yang tinggi. Pemilihan metode pengendalian yang tepat perlu dilakukan untuk dapat diterapkan dalam sistem coupled tank agar dapat memberikan kinerja dengan presisi tinggi. Sejak awal kemunculannya, Reinforcement Learning (RL) telah menarik minat dan perhatian yang besar dari para peneliti dalam beberapa tahun terakhir. Akan tetapi teknologi ini masih belum banyak diterapkan secara praktis dalam kontrol proses industri. Pada penelitian ini, akan dibuat sebuah sistem pengendalian level pada sistem coupled tank dengan menggunakan Reinforcement Learning dengan menggunakan algoritma Twin Delayed Deep Deterministic Policy Gradient (TD3). Reinforcement Learning memiliki fungsi reward yang dirancang dengan sempurna yang diperlukan untuk proses training agent dan fungsi reward tersebut perlu diuji terlebih dahulu melalui trial and error. Performa hasil pengendalian ketinggian air pada sistem coupled tank dengan algoritma TD3 mampu menghasilkan pengendalian yang memiliki keunggulan pada rise time, settling time, dan peak time yang cepat serta nilai steady state eror sangat kecil dan mendekati 0%.

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The coupled tank system is an example of the application of an industrial level control system that has complex characteristics with high non-linearity. It is necessary to select an appropriate control method to be applied in coupled tank systems in order to provide high-precision performance. Since its inception, Reinforcement Learning (RL) has attracted great interest and attention from researchers in recent years. However, this technology is still not widely applied practically in industrial process control. In this research, a level control system in a coupled tank system will be made using Reinforcement Learning using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. Reinforcement Learning has a perfectly designed reward function that is required for the agent training process and the reward function needs to be tested first through trial and error. The performance of the results of controlling the water level in the coupled tank system with the TD3 algorithm is able to produce controls that have advantages in rise time, settling time, and peak time which are fast and the steady state error value is very small and close to 0%."

"Coupled-tank system merupakan salah satu model plant yang umum digunakan dalam industri untuk pengendalian sistem multivariabel. Meskipun metode PID banyak digunakan dalam industri, pendekatan ini memiliki keterbatasan saat diterapkan pada sistem nonlinear seperti coupled-tank MIMO system. Oleh karena itu, metode RL DDPG hadir sebagai pendekatan inovatif untuk sistem kendali. Algoritma DDPG dipilih karena kemampuannya menangani state dan action yang kontinu seperti coupled-tank MIMO system tanpa memerlukan model matematis eksplisit. Perancangan dan pengujian dilakukan menggunakan software MATLAB dan Simulink. Kinerja metode RL DDPG dibandingkan dengan metode PID berdasarkan parameter performa sistem kendali, yaitu overshoot, rise time, settling time, dan steady-state error. Hasil penelitian menunjukkan bahwa metode RL DDPG memberikan performa pengendalian yang lebih baik. Rata-rata overshoot yang dihasilkan adalah 1,48% untuk tangki 1 dan 0,58% untuk tangki 2, jauh lebih rendah dibandingkan metode PID yang mencapai 20,86% dan 21,73%. RL DDPG juga menunjukkan rata-rata settling time yang lebih cepat, yaitu 119,23 detik untuk tangki 1 dan 111,66 detik untuk tangki 2, dibandingkan PID yang mencapai 196,79 detik (tangki 1) dan 177,54 detik (tangki 2). Parameter rise time dan steady-state error juga menunjukkan performa yang baik pada metode RL, masing-masing sebesar 35,64 detik dan 35,18 detik untuk rise time, serta 0,18% dan 0,25% untuk steady-state error. Berdasarkan hasil penelitian ini, metode RL DDPG cukup efektif sebagai solusi alternatif, terutama dalam hal kestabilan sistem.

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The coupled-tank system is a widely used plant model in industry for multivariable control applications. Although the PID method is commonly applied due to its simplicity and effectiveness, it has limitations when applied to nonlinear systems such as the coupled-tank MIMO system. Therefore, the Reinforcement Learning (RL) method with the Deep Deterministic Policy Gradient (DDPG) algorithm is introduced as an innovative control approach. The DDPG algorithm was chosen for its ability to handle continuous state and action spaces, such as those in the coupled-tank MIMO system, without requiring an explicit mathematical model. The system was designed and tested using MATLAB and Simulink software. The performance of the RL-DDPG method was compared with the PID method based on standard control performance parameters: overshoot, rise time, settling time, and steady-state error. The results show that the RL-DDPG method provided better control performance. The average overshoot was 1.48% for tank 1 and 0.58% for tank 2, significantly lower than the PID method, which reached 20.86% and 21.73%, respectively. The RL-DDPG method also achieved faster average settling times, at 119.23 seconds for tank 1 and 111.66 seconds for tank 2, compared to 196.79 seconds (tank 1) and 177.54 seconds (tank 2) with the PID method. The rise time and steady-state error parameters also indicated strong performance with the RL-DDPG method, at 35.64 and 35.18 seconds for rise time, and 0.18% and 0.25% for steady-state error. Based on these findings, the RL-DDPG method is considered an effective alternative solution, particularly in terms of system stability."