In this paper, a simple model-free controller for electrically driven robot manipulators is presented using function approximation techniques (FAT) such as Legendre polynomials (LP) and Fourier series (FS). According to the orthogonal functions theorem, LP and FS can approximate nonlinear functions with an arbitrary small approximation error. From this point of view, they are similar to fuzzy systems and can be used as controller to approximate the ideal control law. In comparison with fuzzy systems and neural networks, LP and FS are simpler and less computational. Moreover, there are very few tuning parameters in LP and FS. Consequently, the proposed controller is less computational in comparison with fuzzy and neural controllers. The case study is an articulated robot manipulator driven by permanent magnet direct current (DC) motors. Simulation results verify the effectiveness of the proposed control approach and its superiority over neuro-fuzzy controllers.
Direct Adaptive Function Approximation Techniques Based Control of Robot Manipulators
Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received July 18, 2016; final manuscript received June 10, 2017; published online September 5, 2017. Assoc. Editor: Azim Eskandarian.
Zirkohi, M. M. (September 5, 2017). "Direct Adaptive Function Approximation Techniques Based Control of Robot Manipulators." ASME. J. Dyn. Sys., Meas., Control. January 2018; 140(1): 011006. https://doi.org/10.1115/1.4037269
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