Tuning of an optimal fuzzy PID controller with stochastic algorithms for networked control systems with random time...

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Networked Systems

Background:

• Networked Control Systems (NCS) present challenges due to unpredictable network-induced delays.
• Tuning Proportional-Integral-Derivative (PID) controllers for higher-order systems with time delays is complex.
• Fuzzy logic offers potential for robust control in uncertain environments.

Purpose of the Study:

• To tune optimal Proportional-Integral-Derivative (PID) and fuzzy PID controllers for a networked control system (NCS).
• To evaluate the performance of controllers tuned using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) variants.
• To compare the effectiveness of conventional PID versus fuzzy logic-based PID controllers in handling network delay.

Main Methods:

• Optimization of PID and fuzzy PID controllers by minimizing Integral of Time multiplied Absolute Error (ITAE) and squared controller output.
• Application of Genetic Algorithm (GA) and two variants of Particle Swarm Optimization (PSO) for controller tuning.
• Performance evaluation of closed-loop systems under varying network conditions.

Main Results:

• Both GA and PSO successfully tuned optimal PID and fuzzy PID controllers.
• Fuzzy logic-based PID controllers exhibited enhanced robustness against random variations in network delay.
• The proposed tuning methods effectively addressed higher-order and time-delay system characteristics.

Conclusions:

• Fuzzy logic-based PID controllers provide a more effective solution for NCS with variable network delays compared to conventional PID controllers.
• Stochastic optimization algorithms like GA and PSO are suitable for tuning complex control systems.
• The ITAE and squared controller output minimization criteria yield well-performing controllers for NCS.