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Related Experiment Videos

Secondary Cooling Water System Control Method Based on Deep Reinforcement Learning.

Jin Xu1, Yu Cheng1, Cheng Shen1

  • 1School of Artificial Intelligence, Shenyang Aerospace University, Shenyang 110136, China.

Sensors (Basel, Switzerland)
|May 13, 2026
PubMed
Summary
This summary is machine-generated.

A new Beta-policy and PID-inspired augmented-state proximal policy optimization (BPAS-PPO) framework improves control of secondary cooling water systems. BPAS-PPO offers smoother control actions and better performance compared to conventional methods.

Keywords:
beta distributiondeep reinforcement learningindustrial process controlintelligent controlproximal policy optimization (PPO)secondary cooling water system

Related Experiment Videos

Area of Science:

  • Engineering
  • Control Systems
  • Artificial Intelligence

Background:

  • Secondary cooling water systems present control challenges due to loop coupling, thermal inertia, and actuator constraints.
  • Conventional proximal policy optimization (PPO) with Gaussian action sampling can suffer from degraded performance near actuator limits.

Purpose of the Study:

  • To develop an advanced control framework, BPAS-PPO, for enhanced secondary cooling water system management.
  • To address limitations of conventional PPO in systems with strict actuator constraints and complex dynamics.

Main Methods:

  • The proposed Beta-policy and PID-inspired augmented-state proximal policy optimization (BPAS-PPO) framework.
  • Augmenting the state with proportional, integral, and derivative (PID) error features.
  • Utilizing a Beta-distribution policy for bounded continuous-action generation and a piecewise dense reward.

Main Results:

  • Simulation studies on a two-input two-output (TITO) model demonstrated the effectiveness of the PID-augmented state.
  • BPAS-PPO exhibited lower overshoot, reduced settling time, and superior setpoint tracking and disturbance rejection.
  • Smoother control actions were observed near actuator limits compared to PID, Fuzzy-PID, and Gauss-PPO.

Conclusions:

  • The BPAS-PPO framework is effective for controlling secondary cooling water systems within the studied operating region.
  • The enhanced state representation and Beta-policy contribute to improved control performance and smoothness.
  • Further validation is needed to assess performance beyond the selected operating region.