Observer Design for Nonlinear Systems: LMI Approach with Algorithm Implementation
Design of Nonlinear System Observers Using Linear Matrix Inequality (LMI) Methods: Research Paper with MATLAB Code Implementation and Algorithm Details
Design and MATLAB Simulation of Feedback Linearization Controller for Nonlinear Systems
Implementation of feedback linearization controller design for nonlinear systems with detailed MATLAB simulation and code analysis, covering transformation algorithms and control performance evaluation.
Solving Nonlinear System Time Series Prediction Problems Using Statistical Chaos Methods
MATLAB implementation of statistical chaos methods for nonlinear system time series prediction with code-based algorithm explanations
Adaptive Inverse Vibration Control Technology Based on Neural Network Online Identification
Adaptive inverse vibration control technology utilizing neural network online identification for effective application in nonlinear system control, with implementation insights including real-time parameter updates and inverse model compensation.
Extended Kalman Filter (EKF) Algorithm with Motion Model Implementation
An Extended Kalman Filter (EKF) algorithm based on motion models, applicable to any nonlinear system representable in state-space form, achieving near-optimal estimation accuracy through iterative prediction and correction cycles.
Fundamental Principles of Particle Filtering and Its Applications in Nonlinear Systems
This manual primarily introduces the core concepts of particle filtering and its practical implementations in nonlinear systems, focusing on key applications such as target tracking, multi-target tracking, and battery life prediction. The handbook's distinctive advantage lies in providing complete MATLAB code examples alongside theoretical explanations, enabling readers to directly correlate mathematical formulations with practical implementations. It serves as an efficient entry point for researchers entering this field, while also offering a solid foundation for experienced practitioners to further refine algorithms and conduct advanced studies through customizable code structures.
MATLAB Code Implementation of Particle Filter
MATLAB-based particle filter program for maneuvering target tracking, specifically designed for nonlinear systems with comprehensive algorithmic implementation
Unscented Kalman Filter
Unscented Kalman Filter - Primarily applied for tracking in nonlinear systems, implementing sigma point transformation for improved state estimation accuracy.
Compact Form Linearization and Partial Form Linearization Methods
Based on compact form linearization and partial form linearization methods, nonlinear systems can be linearized for subsequent PID controller design, with implementation considerations for control algorithm structure and parameter tuning.
BP Neural Network Modeling Optimized by Genetic Algorithm
Modeling nonlinear systems using 1500 datasets for network training and 500 datasets for testing. Since BP neural networks typically initialize weights and thresholds randomly, they often get trapped in local minima. This method employs genetic algorithm optimization for initial weights and thresholds, with comparative analysis of pre- and post-optimization performance. Implementation includes population initialization, fitness function design based on MSE, and chromosome encoding of network parameters.