Experimental control and model validation : a helicopter case study

electrical engineering

Morris, John Christopher ; Doyle, John Comstock (advisor)

University:California Institute of Technology

Department:Engineering and Applied Science

关键词: electrical engineering;   

Others  :  https://thesis.library.caltech.edu/5089/1/Morris_jc_1996.pdf

美国|英语

来源: Caltech THESIS

【 摘 要 】

Robust control has not been used as widely as it could because modelling tools have not advanced as far as analysis and synthesis tools. This becomes readily apparent when applying robust control theory to real problems. With this in mind, an experimental platform was designed and built to study the application of robust control. This platform consists of a real-time computer and a radio-controlled model helicopter mounted on a six degree-of-freedom stand. Experimental systems provide the opportunity not only to verify the applicability of new control theory but also to highlight potential deficiencies.

Traditional system identification and control techniques were used to construct hover controllers for the model helicopter. These techniques are not suitable for the construction of robust models for a system of this complexity. In particular, there was no systematic way to augment nominal identified models with uncertainty suitable for the construction of robust controllers.

To address this issue, frequency-domain model validation algorithms and software were developed. These algorithms provide a methodology for verifying the applicability and consistency between experimental data and robust models. Additionally, they provide a method whereby nominal model parameters can be tuned in a robust setting. This is the first set of software tools which provide this capability for general linear uncertain systems.

Using these new software tools, a systematic design process was developed which incorporated frequency-domain model validation analysis, µ-analysis and µ-synthesis, simulation, and implementation. This design process proved to be a valuable new tool for constructing robust models and designing robust control systems. In particular, by applying this design process to the helicopter, the size of uncertainty in the robust model was substantially reduced without sacrificing the ability of the model to "cover" experimental data and the first controller implemented performed well. This was strikingly different from the results obtained when using standard robust control techniques, where several controllers destabilized the helicopter when implemented, even though they performed well under simulation.

The model validation software and design process provide a consistent methodology and systematic framework which connects system identification, the construction of robust models, and controller synthesis with experimental data. For the first time the control engineer can compute measures on the validity of a robust model, with respect to all observed data on the actual physical system, which are directly related to the robustness measures resulting from µ-analysis and µ-synthesis.