Robot Obstacle Avoidance Simulation Using Fuzzy Controller in MATLAB
Simulating robot obstacle avoidance behavior in MATLAB using a fuzzy logic controller with implementation details for intelligent navigation systems.
Explore MATLAB source code curated for "模糊控制器" with clean implementations, documentation, and examples.
Simulating robot obstacle avoidance behavior in MATLAB using a fuzzy logic controller with implementation details for intelligent navigation systems.
Implementation of a TS model-based fuzzy controller using MATLAB, including fuzzy set definition, rule creation, and inference processes with code examples.
Fuzzy Controller: Establishing complex fuzzy rules using MATLAB. Can be implemented through custom programming or utilizing MATLAB's built-in Fuzzy Logic Toolbox for comprehensive fuzzy inference system design.
This study implements fuzzy logic and fuzzy controllers to regulate tanker ship operations, exploring two primary approaches: fuzzy modeling combined with conventional control methods, and direct fuzzy control implementation.
This major project for my Intelligent Control course required designing a compliant fuzzy controller, accompanied by complete MATLAB programs and simulation diagrams demonstrating system performance under various conditions.
This assignment focuses on fuzzy control system design, covering the development of a fuzzy logic controller (including detailed specifications of universe of discourse, linguistic variables, and rule base in the experimental report) and simulation implementation using MATLAB/Simulink. The project demonstrates practical applications of fuzzy logic algorithms through code-based implementations.
Application Background: This project involves two vehicles (target vehicle and ego vehicle) where the transfer function between speed y and throttle control input u is identical for both. The objective is to design a fuzzy controller to achieve: 1) Control the ego vehicle starting from rest to chase a target vehicle moving at 90 km/h located 200m ahead, maintaining a 30m distance. 2) Maintain 30m distance when target speed changes to 110 km/h 3) Maintain 30m distance when target speed changes to 70 km/h Key Technologies: - Construct system simulation model using Simulink - Design fuzzy logic rules and membership functions - Implement fuzzy inference system using Fuzzy Logic Toolbox
Implementation of vertical handoff mechanism using fuzzy logic controller for intelligent network switching decisions
MATLAB m-file implementation for fitness function optimization using genetic algorithms: weighting matrix optimization for linear quadratic optimal control problems and scaling factor optimization for fuzzy controllers, featuring algorithm parameter tuning and performance evaluation methods.
This control algorithm implements a fuzzy controller for robotic obstacle avoidance systems, designed as a multi-input multi-output control system, with simulation results closely matching real-world performance.