Explore MATLAB source code curated for "二阶系统" with clean implementations, documentation, and examples.
Implementation of sliding mode variable structure control for a second-order system, augmented with fuzzy control to mitigate chattering effects induced by variable structure control. The approach employs fuzzy logic to dynamically adjust control parameters and improve system stability.
Frequency response and phase response analysis, zero-pole plot generation, and cascade implementation of second-order systems with code implementation details
Simulink-based simulation of fuzzy systems with implementation examples for controlling first-order and second-order dynamic systems, including algorithm design and performance analysis.
Implementation of least squares method for second-order system identification to extract damping ratio and natural frequency parameters with code-oriented algorithmic explanations
Programs for generating random sequences, white noise generation techniques, M-sequence generation algorithms, along with least squares identification programs including batch processing for second-order systems, practical pressure system identification, recursive least squares methods, and augmented least squares identification approaches.
Implementation of ordinary least squares and recursive least squares algorithms for parameter identification in second-order dynamic systems with code implementation insights
Key MATLAB implementations covering: 【1】Random sequence generation program 【2】White noise generation with zero mean and constant variance 【3】M-sequence generation with optimal autocorrelation properties 【4】One-step least squares identification for second-order systems 【5】Practical pressure system parameter identification 【6】Recursive least squares algorithm for large datasets 【7】Augmented least squares for underactuated systems 【8】Gradient-corrected least squares for enhanced accuracy 【9】Recursive maximum likelihood estimation 【10】Bayesian parameter estimation methods 【11】Modified neural network MBP algorithm for noisy systems 【12】Multi-dimensional nonlinear function identification 【13】Fuzzy neural network decoupling for multi-time-scale systems 【14】F-test procedures for model validation
Select second-order system models and parameters, design experimental procedures and steps to simulate the effect of natural oscillation frequency and damping coefficient on system time domain response characteristics; study the impact of adding a pole or zero to the system time domain response; summarize response patterns based on experimental results. 1. Select no fewer than six values for each parameter and simulate their step (or impulse) responses. Plot the influence of parameters on time domain responses, with different parameters plotted separately in two graphs. 2. Use graphical methods to obtain time domain response indicators, compare them, and summarize the impact of parameter variations on system response characteristics. 3. Select no fewer than six values for additional zeros and poles, simulate their step (or impulse) responses, and compare with the baseline response without zeros or poles.
There are relatively few programs available for sliding mode control; these are typical examples with significant reference value for researchers working on sliding mode control. Program 3-1: Discrete Sliding Mode Control Based on Reaching Law (for Second-Order Systems)