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A comparison between gain-scheduling linear quadratic regulator and model predictive control for a manipulator with flexible components

引用

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING 2022年第7期236卷 1285-1293页

作者： Colombo, Fernanda Thais da Silva, Maira Martins Univ Sao Paulo Sao Carlos Sch Engn Dept Mech Engn Trabalhador Sao Carlense 400 BR-13566590 Sao Carlos Brazil

Dynamic performance and energy efficiency can be improved by reducing the inertia of mechanical systems. However, the components of such a system may be flexible and, inevitably, subject to vibrations. Moreover, the relative motion between flexible components leads to time-varying boundary conditions characterizing configuration-dependent dynamics. Since robust controllers may be conservative for this problem, adaptive controllers should be considered since they can cope with performance design requirements. In this work, we present a comparison between a linear quadratic regulator gain-scheduling control and an adaptive model predictive control for a flexible industrial Cartesian manipulator with configuration-dependent dynamics. This study takes the system stability and the use of bounded actuation into account. Numerical results demonstrate that the adaptive model predictive control strategy achieved better performance than the linear quadratic regulator gain scheduled. Also, in applications with tighter actuation bounds, the difference between the performance of both approaches is even higher, reinforcing the model predictive control capability of dealing with limited actuation.

关键词： Adaptive model predictive control linear quadratic regulator gain-scheduled control configuration-dependent dynamics linear parameter-varying systems adaptive control systems

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