Networked Control Time-Delay Compensation Using PSO-FNN Prediction

The control system is segmented into distinct functional modules: PSO algorithm module for optimization, FNN module for prediction, and PIGPC module for compensation. Each module performs a specific function, allowing the complex time-delay compensation task to be divided into manageable components that can be developed and optimized independently while maintaining overall system effectiveness.

The system dynamically adjusts key parameters including prediction horizon, control horizon, forgetting factor, and penalty factors based on system conditions. The PSO algorithm optimizes FNN parameters such as connection weights and membership function parameters, enabling the system to adapt to varying time-delay characteristics and improve prediction accuracy under different operating conditions.

The traditional BP algorithm (gradient-based mechanical optimization) is replaced with the PSO algorithm (swarm intelligence approach). PSO uses a population-based search mechanism that explores the parameter space more efficiently, avoiding local minima and achieving faster convergence with higher prediction accuracy, though requiring more computational resources per iteration.

The PSO algorithm performs preliminary optimization of FNN parameters before actual prediction tasks. By pre-optimizing connection weights and membership function parameters using training data, the system prepares the FNN in advance for accurate predictions, reducing the need for repeated optimizations during real-time operation.

The explicit GPC algorithm's computationally intensive components are extracted and replaced. Specifically, the explicit calculation of future control moves is replaced with an implicit iterative approach that solves for control increments more efficiently. The system extracts only the essential prediction and compensation functions, removing unnecessary computational overhead while maintaining control effectiveness.

The control algorithm transitions from a static explicit GPC approach to a dynamic implicit PIGPC approach. The implicit formulation allows the controller to adaptively adjust control actions based on current system state and predicted time-delay, with iterative refinement that converges to optimal solutions. This dynamic approach improves real-time performance by focusing computational effort only where needed.