FAT-based robust adaptive control of electrically driven robots without velocity measurements

Recently, regressor-free control approach has been presented in which uncertainties are estimated using function approximation techniques (FAT) such as the Fourier series expansion or Legendre polynomials. However, FAT-based observer design remains as an open problem. With this in mind, this paper presents a robust adaptive controller for electrically driven robots, without any need for velocity measurements. The mixed observer/control design procedure is based on universal approximation theory and using Stone-Weierstrass theorem. To highlight the contribution of the paper, it should be emphasized that in comparison with previous related FAT-based controllers, the proposed controller is simpler and less computational. In addition, the number of required Fourier series expansions, control laws, and also adaptation rules has been reduced. Moreover, the observer design is free of model. Simulation results of the controller on a 6-DOF industrial robot manipulator have been presented which proves robustness of the proposed controller against various uncertainties. The results are also compared to those obtained from Chebyshev neural network.