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<p>The latest developments in industrial control applications emphasize the need for incorporating intelligent algorithms for enhanced adaptability and performance. This study addresses the challenge of controlling a nonlinear, multivariable Boiler-Turbine System (BTS), which exhibits strong interactions, non-minimum phase behavior, and instability due to the integrating nature of water level dynamics. Traditional PID tuning methods often fail to manage such complexities effectively. In this work, a reinforcement learning (RL)-based approach is proposed using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm for adaptive PID tuning. Specifically, two novel multi-agent TD3 algorithms are introduced: Shared-Critic Multi-Agent (SCMA-TD3) and Individual-Critic Multi-Agent (ICMA-TD3). These architectures explore the use of shared versus independent critic networks, with varying actor-critic depths, to improve learning efficiency and control accuracy. The BTS control problem is meticulously modelled as an RL task, and the performance of SCMA-TD3, ICMA-TD3, and standard DDPG is compared for PID tuning under standard step signals and different disturbance scenarios. The findings highlight the capability of SCMA-TD3, ICMA-TD3 and DDPG algorithms to minimize oscillations and reduce settling time, while simultaneously enhancing efficiency and stability in BTS both qualitatively and quantitatively for the characteristics namely drum pressure, electric power and drum water level. The stability analysis of the BTS is conducted based on the computation of error metrics such as Integral Time Absolute Error (ITAE), Integral Square Error (ISE) and Integral Absolute Error (IAE). The ICMA-TD3 method demonstrates superior performance in control applications, achieving a 99.33% and 99.76% reduction in ITAE for electric power and drum water level control, respectively, compared to SCMA-TD3 and DDPG in BTS control. Additionally, ICMA-TD3 exhibits a 91.40% faster rise time and an 84.37% reduction in overshoot for electric power control. In the case of drum pressure regulation, while ICMA-TD3 achieves a 99.866% lower ITAE than SCMA-TD3, it experiences greater overshoot compared to SCMA-TD3. Furthermore, DDPG, despite its implementation, incurs a high cost function, along with excessive rise time and overshoot, making it the least effective approach for precise control applications. These results demonstrate that the proposed multi-agent TD3 frameworks offer a robust and adaptive solution for complex industrial control systems like BTS.</p>

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Novel multiagent reinforcement learning framework using twin delayed deep deterministic policy gradient for adaptive PID control in boiler turbine systems

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月3日

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  • U. Kruthika,
  • Surekha Paneerselvam

摘要

The latest developments in industrial control applications emphasize the need for incorporating intelligent algorithms for enhanced adaptability and performance. This study addresses the challenge of controlling a nonlinear, multivariable Boiler-Turbine System (BTS), which exhibits strong interactions, non-minimum phase behavior, and instability due to the integrating nature of water level dynamics. Traditional PID tuning methods often fail to manage such complexities effectively. In this work, a reinforcement learning (RL)-based approach is proposed using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm for adaptive PID tuning. Specifically, two novel multi-agent TD3 algorithms are introduced: Shared-Critic Multi-Agent (SCMA-TD3) and Individual-Critic Multi-Agent (ICMA-TD3). These architectures explore the use of shared versus independent critic networks, with varying actor-critic depths, to improve learning efficiency and control accuracy. The BTS control problem is meticulously modelled as an RL task, and the performance of SCMA-TD3, ICMA-TD3, and standard DDPG is compared for PID tuning under standard step signals and different disturbance scenarios. The findings highlight the capability of SCMA-TD3, ICMA-TD3 and DDPG algorithms to minimize oscillations and reduce settling time, while simultaneously enhancing efficiency and stability in BTS both qualitatively and quantitatively for the characteristics namely drum pressure, electric power and drum water level. The stability analysis of the BTS is conducted based on the computation of error metrics such as Integral Time Absolute Error (ITAE), Integral Square Error (ISE) and Integral Absolute Error (IAE). The ICMA-TD3 method demonstrates superior performance in control applications, achieving a 99.33% and 99.76% reduction in ITAE for electric power and drum water level control, respectively, compared to SCMA-TD3 and DDPG in BTS control. Additionally, ICMA-TD3 exhibits a 91.40% faster rise time and an 84.37% reduction in overshoot for electric power control. In the case of drum pressure regulation, while ICMA-TD3 achieves a 99.866% lower ITAE than SCMA-TD3, it experiences greater overshoot compared to SCMA-TD3. Furthermore, DDPG, despite its implementation, incurs a high cost function, along with excessive rise time and overshoot, making it the least effective approach for precise control applications. These results demonstrate that the proposed multi-agent TD3 frameworks offer a robust and adaptive solution for complex industrial control systems like BTS.