LabVIEW, MatrixX and Modern Control

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LabVIEW Module: Xmath Control Design.
Linear System Representation. Building System Connections. System Analysis: system stability and time-domain analysis, include poles, zeros, and residue. Time - domain stability to time-domain system simulation. Impulse and step responses, system response to arbitrary initial conditions. Classical Feedback Analysis - classical feedback-based control design:  root locus techniques and functions for frequency-domain analysis of closed-loop systems, given open-loop system descriptions.  State-Space Design - modern control: system controllability and observability, general pole placement, linear quadratic control, and system balancing.

LabVIEW Module: Model Reduction Module (MRM). A collection of tools for reducing the order of systems, mainly based on the state-of-the-art algorithms in conjunction with researchers at the Australian National University, the original developers of some of the algorithms. Hankel singular values and balanced realizations. Model reduction with additive errors / Model reduction with multiplicative errors / Model reduction with frequency weighting of an additive error, including controller reduction / Utility functions.

LabVIEW Module: Xmath Optimization Module. Nonlinear Programming, Quadratic Programming, Linear Programming.

 

LabVIEW Module: Robust Control Module. Robustness Analysis - uncertainty, robustness, and performance degradation of closed-loop systems. Modeling Uncertain Systems, Stability Margin and the Worst-Case Performance Degradation.  System Evaluation - singular value Bode plots, performance plots, and the L∞ norm of a linear system. Controller Synthesis: H∞ and H2, LQG/LTR, and frequency shaped LQG design.



LabVIEW Module: Xmath Interactive System Identification Module. Nonparametric Identification Methods: Empirical transfer function estimation,  Spectral density function estimation. / Identification and Model Reduction: Prediction error method for ARMAX models, Prediction error method for Box-Jenkins models, Generalized instrumental variables, Initial model estimation, Estimator of initial state, Identification from impulse response data, Time domain least squares, General maximum likelihood estimation of continuous, discrete, linear, or nonlinear systems, Prediction error method for output error models, Prediction error method, Subspace identification method, Subspace identification method for output-only data, Continuous-time SISO transfer function identification from frequency response data. / State Space Model Transformations:  / Polynomial Model Transformations / Validation Functions / Combining Separate Data Sets / Input Design / General Functions / Preprocessing Functions.