Tag Archives: System Identification

A remote Matlab laboratory for LTI system identification

Z. Lei, H. Zhou, W. Hu and G. -P. Liu, Teaching and Comprehensive Learning With Remote Laboratories and MATLAB for an Undergraduate System Identification Course, EEE Transactions on Education, vol. 65, no. 3, pp. 402-408, Aug. 2022 DOI: 10.1109/TE.2021.3123302.

Contribution: This article introduces the teaching and learning with remote laboratories and MATLAB for an undergraduate system identification (SI) course, which can be employed for students at the advanced level with a control background. Background: SI has been widely used in all engineering fields; thus, the SI course that includes complex theories, concepts, and formulas is crucial for engineering education. Constraints, such as time, space, cost, and maintenance work, pose limitations for conventional laboratories, and current remote laboratories may not offer experiences to enhance control-oriented practical skills. Intended Outcomes: The proposed teaching and learning using remote laboratories is intended to facilitate the understanding of theories and concepts, and enhance the ability of design and implementation of control algorithms, the conducting of experiments, data collecting, data analysis, and the conducting of SI with MATLAB. Application Design: In the classroom teaching, theoretical lectures regarding SI are delivered to students by the teacher, along with the classroom demonstration with the networked control system laboratory for online experimentation. Then, the laboratory work is required to be completed by the students using the remote laboratory. A tailored laboratory report is supposed to be handed in by each student after the experimentation. Findings: The effectiveness of the proposed method was evaluated through the analysis of student performance and student responses to surveys. The student performance analysis indicates that the application of the remote laboratories is effective, and the feedback from students shows that they can benefit from the application of remote laboratories, and they would like the remote laboratories to be expanded to other courses.

Identifying state-space-models of systems with autoencoders

Daniele Masti, Alberto Bemporad, Learning nonlinear state–space models using autoencoders, . Automatica, Volume 129, 2021 DOI: 10.1016/j.automatica.2021.109666.

We propose a methodology for the identification of nonlinear state–space models from input/output data using machine-learning techniques based on autoencoders and neural networks. Our framework simultaneously identifies the nonlinear output and state-update maps of the model. After formulating the approach and providing guidelines for tuning the related hyper-parameters (including the model order), we show its capability in fitting nonlinear models on different nonlinear system identification benchmarks. Performance is assessed in terms of open-loop prediction on test data and of controlling the system via nonlinear model predictive control (MPC) based on the identified nonlinear state–space model.