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

• Published in: Cogent engineering Vol. 11; no. 1
• Main Authors: Duong, Van Tu, Truong, Cong Toai, Nguyen, Trong Trung, Nguyen, Huy Hung, Nguyen, Tan Tien
• Format: Journal Article
• Language: English
• Published: Abingdon Cogent 31.12.2024; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Adaptation; Adaptive algorithms; Adaptive systems; Algorithms; Automation Control; Case studies; Control Engineering; Controllers; D C motors; Dynamical Control Systems; Dynamical systems; Electric motors; hardware-in-the-loop (HIL); Hardware-in-the-loop simulation; Identification; Mechanical engineering; Mechatronics; MicroPython; model reference adaptive; Parameter estimation; Parameter identification; Platforms; Programming languages; Python; Simulation; Software; Software-in-the-loop (SIL); State space models; state-space model; Steady state models; system identification
• ISSN: 2331-1916; 2331-1916
• DOI: 10.1080/23311916.2024.2434938

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 ( [ 100 100 ] ) 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.