State space model identification using model reference adaptive approach: software and hardware-in-the-loop simulation

This paper presents a comprehensive tutorial on the identification process for a class of continuously dynamic systems expressed in state-space form using the model reference adaptive approach. The proposed algorithm does not require prior knowledge of the systems but needs all state variables to adapt the estimated parameters. Through simulations using m-script code, software-in-the-loop (SIL), and hardware-in-the-loop (HIL) simulations, the effectiveness of the proposed method in identifying the system model of a DC motor is evaluated. Simulation results demonstrate consistency across various platforms. Steady-state estimated models can be achieved using the proposed estimation algorithm with adaptation gains of diag([100100]) after 5 s. Furthermore, this paper demonstrates the implementation of the proposed method on both SIL and HIL platforms, using Python and MicroPython programming languages, respectively. This approach leverages the Numpy library for efficient matrix computations. It is evident that the proposed estimation algorithm is readily applicable in real-world scenarios.