Abstract
AbstractThe paper considers key limitation of the feedback linearisation controller designed for nonlinear systems based on the imperfect nominal dynamics model and sensors. The model-reality differences cause signal leakages in the feedback linearised dynamics. As the leakages are the functions of the process variables, the resulting overall dynamics are again nonlinear with strong additive nonlinearities and the expected decoupling of the system dynamics is missing. In the paper, instead of using advanced control tools, we prove the robustness of the feedback linearisation method can also be significantly enhanced by employing several simple and classical methods cooperatively. For clear description and explanation, the methodology was illustrated based on a two-link manipulator case study, a classical multi-input multi-output coupled nonlinear system. The methods have genetic potential so that they can be applicable to a variety of case study systems and also further developed to become general methodo...
Highlights
Feedback linearisation control [1] has been widely used in controlling nonlinear and coupled multi-input multi-output (MIMO) systems [2]–[6]
As the leakages are the functions of the process variables, the resulting overall dynamics are again nonlinear with strong additive nonlinearities and the expected decoupling of the system dynamics is missing
As the leakages are the functions of process variables, the resulting overall dynamics are again nonlinear with strong additive nonlinearities
Summary
Feedback linearisation control [1] has been widely used in controlling nonlinear and coupled multi-input multi-output (MIMO) systems [2]–[6]. The expected decoupling of the system dynamics is missing and the resulting feedback linearisation controller is not robust enough to reach the desired performance. Such uncertainties are inevitable due to errors and wear in long-term usage. Instead of presenting new tools, we present a general approach, or a methodology, to develop robust feedback linearisation controllers based on accessible basic and popular control methods widely used. For clear description and explanation, the methodology was illustrated based on a two-link manipulator case study, a typical multi-input multi-output (MIMO) coupled nonlinear system, which can be controlled by using various tools including model predictive control [14] and slide mode control [15]–[19]. The structural uncertainty means parts of the real model are missing in the nominal model
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