Here is the green signal our measured signal and the black signal is our estimated signal. According to me - Lyapunov is much better than MIT rule. Model Reference Adaptive Controller - Lyapunov Ruleĭiagram of MRAC system with uning of Lyapunov rule Model Reference Adaptive Controller - MIT Ruleĭiagram of MRAC system with uning of MIT rule Here is the parameter estimation for the ARMAX model. Between 0 to 70 seconds, the MVC self learning the disturbance. Here is green the input signal and black the output signal. I'm using Extended Least Square to estimate a ARMAX model, it's a transfer function with disturbance. On other words, non-minimum phase is much more difficult to handle.ĭiagram of the MVC system. The difference between non-minimum phase and minimum phase is that non-minimum phase has zeros at the right half plane and minimum phase has zeros on the left half plane. Self Tuning Regulator - Non-Minimum phase systemĭiagram of the STR system with non-minimum phase. Here I use Recursive Least Square and STR learning the system between 0 to 15 seconds, then tune the STR controller. Self Tuning Regulator - Minimum phase systemĭiagram of the STR system with minimum phase. Between 0 to 30 seconds, the GPC controller learns the system behaviour. The green line is our reference following line and the black line is the system output. Green is our input signal, black is our noisy output signal and red is our filtered output signal by using a kalman filter. Here we can se that the system has a LQE - Linear Quadratic estimator and a Kalman Filter Between 0 to 50 seconds, the estimator learn the system behaviour.ĭiagram of the LQG system. The green signal is the input signal and the black signal is the output signal. If you want to identify models, then this library is for you - Mataveid. Square Root Uncented Kalman Filter for state estimation and parameter estimation Easy to use and easier than regular kalman filter. I very good filter for you is the Unscented Kalman Filter for nonlinear and linear systems. MATLAB-files and C-code files as examples is available: If you want to try adaptive control with C-code.
#Scilab kalman filter software
The collection is made by the open source software Scilab and Xcox 6.0.1 and the book "Adaptive Control" * Adaptive Constrained MPC (ERAOKIDMPC) - Using Subspace identification methods
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