PID Controlled DC Motor Source Code (Simulink)
PID-controlled DC motor source code developed in Simulink, ready to run immediately. This implementation demonstrates practical PID controller design with motor control applications.
Adaptive Fuzzy PID Control Implementation
Adaptive fuzzy PID control program requiring FIS file import for configuration and operation
Designing Adaptive PID Control Using Grey System Theory
Implementation of online PID parameter adjustment through grey system theory, enabling real-time adaptation to environmental changes with enhanced control algorithms and system intelligence.
PID Control Tuned with BG-PSO Algorithm
Implementation of PID control using the BG-PSO (Binary Grid Particle Swarm Optimization) tuning algorithm, providing a reference for learners with detailed code examples and parameter optimization techniques.
Neural Network Discrete PID Control Implementation Example
MATLAB neural network discrete PID control example - easily implemented by integrating simulation modules in Simulink environment, producing excellent result visualization with comprehensive algorithm implementation details.
Newtonian Dynamics Approach for Single Inverted Pendulum System with Control Method Comparison
This study employs Newtonian dynamics methodology to establish a mathematical model of a single inverted pendulum system, followed by comparative simulation analysis of PID control (based on classical control theory), LQR control (based on optimal control theory), and fuzzy logic control methods. The simulation implementation includes system linearization techniques, state-space formulations for LQR design, and membership function configuration for fuzzy controllers. The analytical results provide theoretical guidance for research in this field, with practical insights into controller tuning parameters and stability margin comparisons.
PID Control Widely Adopted in Industrial Production with Genetic Algorithm Implementation
While PID control is extensively utilized in industrial processes, determining optimal PID parameters remains a key challenge. This example provides a reference implementation using genetic algorithms for PID enthusiasts, demonstrating how evolutionary computation techniques can optimize controller tuning through population-based search and fitness evaluation.
Neural Network-Based PID Control: Neural Network as Controller Rather Than PID Parameter Tuner
Neural network-based PID control does not use neural networks to tune PID parameters; instead, it employs a neural network directly as the controller, adjusting PID parameters indirectly by training the neural network's weight coefficients through backpropagation and optimization algorithms.
MATLAB Simulation for PID Control
Comprehensive MATLAB PID control simulation examples with ready-to-use implementations including M-files and SIMULINK models, featuring detailed algorithm explanations and practical implementation approaches.
PID Control Simulation
Simulation of PID Control, coursework for the "PID Control and Applications" course. SIMULINK-based implementation featuring controller design and system response analysis.