Eng: Control Engineering
A system identification framework that turns measurement noise into a structured uncertainty description. Cyclic reformulation with period N is applied to LTI systems to construct polytopes from a single experiment, then used for robust H∞…
A tutorial on kernel-based regularized system identification. Explains stable spline, tuned-correlated, and diagonal-correlated kernels, hyperparameter tuning via empirical Bayes, and MATLAB implementation with impulseest.
Compare IMC, Smith Predictor, Disturbance Observer, 2-DOF control, and Model Error Compensator (MEC). Structural comparison, selection guidelines, and connections to robust control engineering.
A comprehensive tutorial on state feedback control and state-space design for control systems. Covers pole placement, LQR optimal regulators, integral-type servo systems, LMI-based design, observer-based feedback with the separation princi…
The 2-DOF conditional feedback structure is the structural origin of MEC. Setting T = P_M yields MEC, which is equivalent to 2-DOF control for linear systems with feedforward input. MEC extends further to non-minimum-phase and nonlinear sy…
Structural comparison of Internal Model Control (IMC) and Model Error Compensator (MEC). Both use the plant-model output difference, but IMC designs the controller while MEC adds robustness to existing systems.
A survey of Model Error Compensator (MEC) research by independent groups worldwide. Covers applications to quadcopters, teleoperation, underwater robots, power electronics, data-driven tuning with FRIT and Smart MBD, and theoretical analys…
Apply the Model Error Compensator (MEC) to nonlinear systems for robust feedback linearization. Unlike standard feedback linearization, MEC does not require exact model knowledge or full state measurement. The output-feedback structure ach…
How to apply the Model Error Compensator (MEC) to non-minimum phase systems using a parallel feedforward compensator. Non-minimum phase plants have unstable zeros that prevent standard high-gain compensation. The PFC approach resolves this…
Learn how to add robustness to existing PID control systems using the Model Error Compensator (MEC). MEC is a simple add-on compensator that suppresses the effect of model uncertainty and parameter variations without modifying the PID cont…
A tutorial on classical parametric system identification. Explains ARX, ARMAX, Output-Error, and Box-Jenkins model structures, the prediction error method (PEM) for parameter estimation, model order selection, and MATLAB implementation. In…
A tutorial on subspace identification methods for control systems. Explains N4SID, MOESP, and CVA algorithms, model order selection via SVD, and MATLAB implementation. Includes connections to multirate and LPTV system identification resear…
A comprehensive guide to system identification in control engineering. Covers parametric methods, subspace identification (N4SID), kernel-based estimation, multirate systems, and data-driven control. With MATLAB code and links to research …
A detailed comparison between Model Error Compensator (MEC) and Disturbance Observer (DOB) for robust control. Covers structural differences, inverse model requirements, applicability to non-minimum phase and nonlinear systems, and practic…
A comprehensive guide to the H-infinity filter for robust state estimation. Covers the worst-case optimization formulation, LMI-based design with Bounded Real Lemma, comparison with Kalman filter, pole placement constraints, and extensions…
A comprehensive guide to the Kalman filter for state estimation. Covers the prediction-update algorithm, steady-state Kalman filter, Kalman-Bucy filter, tuning of Q and R, Extended and Unscented Kalman filters, and multi-rate Kalman filter…
A comprehensive guide to state observers and state estimation in control systems. Covers Luenberger observers, Kalman filters, H-infinity filters with LMI design, multi-rate state estimation, and outlier-robust MCV observers. Includes link…
Comprehensive guide to the Model Error Compensator (MEC), a general-purpose method for adding robustness to control systems against model errors and disturbances. Compatible with PID, MPC, nonlinear, and non-minimum phase systems. Includes…
Stability Analysis of Discrete-Time Systems In control system design, system stability is the most fundamental and important characteristic. While in continuous-time systems, stability is determined by whether the roots (poles) of the char…
Linear matrix inequalities (LMIs) and controller design The method using Linear Matrix Inequality (LMI) is one of the most powerful controller design methods in the field of control engineering. The usefulness of controller design using LM…
Discretization of Continuous-Time Control Systems When expressing the characteristics of a control target based on physical laws, it is often represented in the form of differential equations, which are treated within the framework of cont…
In this article, we summarize system identification and dynamic system modeling. Links to related articles and explanatory articles are placed at the bottom. Overview of System Identification Model Representation Example of System Identifi…
This article summarizes the stability of systems represented by state equations. Videos related to stability are placed at the bottom. Poles of the Controlled System and Stability When the Order of the Controlled System is 1 Lyapunov's Sta…
This article summarizes state feedback control for systems expressed in state equation form. It particularly touches on gain design using pole placement methods and the relationship between pole placement and performance. At the end, you c…
This article explains controller design based on optimal regulators for linear systems represented by state equations. Optimal regulators are a type of optimal control, also known as LQ control or LQR (Linear Quadratic Regulator). For an o…
This article summarizes state estimators (state observers) for systems represented by state equations. A video explaining the state observer for systems expressed by state equations is provided at the bottom. Observers are also called soft…
This article summarizes the equivalent transformation (coordinate transformation, equivalence transformation) of a system expressed by a state equation. A video explaining the equivalent transformation of state equations is provided at the…
This article summarizes the controllability and observability of systems. First, I would like to discuss controllability. Links to videos explaining controllability and observability are provided at the bottom. The overall picture of state…
This article summarizes the state-space realization of system state equations. In control based on state equations, deriving a mathematical model is the first step. A video explaining state equation representation is placed at the bottom. …
This article explains control using MATLAB with images and videos, focusing particularly on state feedback control. The videos and related article links explaining MATLAB simulations are provided at the bottom. The following article summar…
