RBF Neural Network Implementation Using S-Function
RBF neural network programmed with S-function, primarily designed for controller applications with code-level implementation details
Simulation Design of Automotive Electronic Throttle Controller
Automotive Electronic Throttle Controller Simulation Design using MATLAB Simulink for modeling and validation
Compressed Sensing Algorithm for Radar Imaging
This program implements compressed sensing algorithms for radar imaging, enabling efficient radar detection zone imaging through advanced signal processing techniques.
Fuzzy Adaptive Control for Inverted Pendulum System Using MATLAB
This implementation provides MATLAB S-functions for both the inverted pendulum plant and fuzzy adaptive controller, along with complete Simulink module files. The fully functional inverted pendulum program demonstrates practical implementation of fuzzy adaptive control algorithms, making it valuable for academic research and publications.
Fuzzy PID Control Program with Adaptive Parameter Tuning
This program implements an adaptive fuzzy PID controller using error (e) and error rate of change (e_dot) as inputs to dynamically adjust PID parameters. The system employs fuzzy logic rules to modify PID coefficients online, creating a self-tuning controller that maintains optimal performance under varying conditions through real-time parameter optimization.
Response Surface Methodology for PD Controller Design in Tanker Ships
Application of Response Surface Methodology for PD Controller Optimization in Tanker Vessel Control Systems
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.
S-functions for Fuzzy Adaptive Control of Inverted Pendulum System: Plant and Controller Implementation
This project implements MATLAB S-functions for both the plant model and fuzzy adaptive controller of an inverted pendulum system, complete with Simulink module files. The fully functional program demonstrates fuzzy adaptive control implementation using MATLAB's S-function architecture, making it valuable for research publications and practical control theory applications.
PSO-Based MPPT Controller for Solar Panels
Implementation of a Particle Swarm Optimization (PSO) based Maximum Power Point Tracking (MPPT) controller for photovoltaic systems with code-level algorithm explanation.
Optimized Design of PID Controller Based on Particle Swarm Optimization
Implementation of PSO-optimized PID controller design using Simulink environment, where PID_Model represents the control system model, PSO module handles particle swarm optimization algorithms, and PSO_PID implements the parameter optimization process for PID controllers through iterative swarm intelligence techniques.