Journal: Uncertain Supply Chain Management

Article Title: Robust simulation-optimization of dynamic-stochastic production/inventory control system under uncertainty using computational intelligence

doi: 10.5267/j.uscm.2020.9.002

Figure Lengend Snippet: Fig. 3. Block diagram representation of the stochastic PID controller in the P/I control system under uncertainty in demand and frustrating rate. This model aims to reduce lead time to zero. The PID gain parameters (𝑲𝒑, 𝑲𝒊, 𝑲𝒅) tuned robustly against source of variability (uncertainty).

Article Snippet: We uniformly produce random numbers for uncertain variables (demand and frustrating rates) and tune the model with MATLAB®/Simulink, PID tuning toolbox.

Techniques: Blocking Assay, Control

Journal: Sensors (Basel, Switzerland)

Article Title: Combined Particle Swarm Optimization and Reinforcement Learning for Water Level Control in a Reservoir

doi: 10.3390/s25165055

Figure Lengend Snippet: MATLAB/Simulink implementation block diagram of the proposed control system using reinforcement learning Twin-Delayed Deep Deterministic agent.

Article Snippet: The PID controller was optimized using the Tune PI Controller approach under reinforcement learning in MATLAB, using a Twin-Delayed Deep Deterministic agent.

Techniques: Blocking Assay, Control

Journal: Sensors (Basel, Switzerland)

Article Title: Combined Particle Swarm Optimization and Reinforcement Learning for Water Level Control in a Reservoir

doi: 10.3390/s25165055

Figure Lengend Snippet: System response in MATLAB/Simulink using reinforcement learning Twin-Delayed Deep Deterministic Agent when the level decreases.

Article Snippet: The PID controller was optimized using the Tune PI Controller approach under reinforcement learning in MATLAB, using a Twin-Delayed Deep Deterministic agent.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Combined Particle Swarm Optimization and Reinforcement Learning for Water Level Control in a Reservoir

doi: 10.3390/s25165055

Figure Lengend Snippet: System response in MATLAB/Simulink using reinforcement learning Twin-Delayed Deep Deterministic Agent when the level increases.

Article Snippet: The PID controller was optimized using the Tune PI Controller approach under reinforcement learning in MATLAB, using a Twin-Delayed Deep Deterministic agent.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Combined Particle Swarm Optimization and Reinforcement Learning for Water Level Control in a Reservoir

doi: 10.3390/s25165055

Figure Lengend Snippet: Response for reinforcement learning + Particle Swarm Optimization and PID System Response when the level increases.

Article Snippet: The PID controller was optimized using the Tune PI Controller approach under reinforcement learning in MATLAB, using a Twin-Delayed Deep Deterministic agent.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Combined Particle Swarm Optimization and Reinforcement Learning for Water Level Control in a Reservoir

doi: 10.3390/s25165055

Figure Lengend Snippet: Response for reinforcement learning + Particle Swarm Optimization and PID System Response when the level decreases.

Article Snippet: The PID controller was optimized using the Tune PI Controller approach under reinforcement learning in MATLAB, using a Twin-Delayed Deep Deterministic agent.

Techniques: