State Feedback Control Simulation with MATLAB
MATLAB simulation program for state feedback control with detailed comments, including PID controller implementation with comprehensive algorithm explanations
Comparison of Neural Network PID Control Performance vs Traditional PID Control
Implementation of neural network PID control method compared with traditional PID control, demonstrating superior precision through adaptive system modeling
Fuzzy Algorithm-Based PID Control for First-Order Inverted Pendulum
Implementation of PID control for first-order inverted pendulum using fuzzy algorithm via T-S model approach in fuzzy control
PID Controller Parameter Optimization Using Ant Colony Algorithm
MATLAB source code for PID controller parameter optimization based on Ant Colony Algorithm, featuring detailed implementation steps with comprehensive documentation
PID Control for Electric Motors with MATLAB Implementation
MATLAB-based PID control implementation for electric motors - download and run in MATLAB environment with detailed code explanation
DC Motor Mathematical Model
DC Motor Mathematical Model including three distinct model files with corresponding MATLAB demonstration files for PID speed control and position control
Simulation Model of PID Control in Control Systems
This simulation model for PID control in control systems is developed using Simulink, designed to help students understand and apply PID control principles through visual implementation and parameter tuning.
Boost Converter Model with PID Control Implementation
This model implements a boost converter with PID control algorithm. The system employs proportional-integral-derivative (PID) control logic where adjusting the P, I, and D coefficients enables optimized dynamic response. The implementation includes parameter tuning methods for achieving desired performance characteristics.
Fuzzy Control vs. PID Control: A Comparative Performance Analysis
Performance comparison between two control methodologies: fuzzy logic control and PID control, including implementation approaches and algorithm characteristics.
PID Control Tuned by BP Neural Network
PID Control with BP Neural Network Tuning for adjusting proportional, integral, and derivative control actions, including implementation approaches and algorithm descriptions