In this paper a framework is proposed for the adaptive control of robotic manipulators which combines parametric adaptive control with Artificial Neural Network (ANN)-based compensation of dynamic uncertainties like friction. The proposed method utilizes a passivity-based parametric adaptive control approach and makes use of the ANN models as generic identifiers to compensate for unmodelled friction effects. Unlike many approaches for ANN based control in the literature, parameter update equations for the ANN model and for the parametric adaptive model are driven by both the tracking error and the system identification error. A stability analysis is given based on the passivity properties of the manipulator dynamics. The methodology is successfully tested for the control of a Direct Drive SCARA arm and performance is compared with standard adaptive control schemes.
A new adaptation mechanism is proposed for the tuning functions based adaptive backstepping control for a class of nonlinear systems. The new scheme combines direct and indirect parameter update regimes in an effort to improve the speed and accuracy of the adaptation which in turn leads to increased tracking performance. The parameter update dynamics is driven both by the identification error and the tracking error defined in the backstepping control. The closed loop error dynamics is shown to be globally stable by using a recursive mechanism based on Lyapunov analysis with tracking error convergence along with identification error convergence. The proposed scheme is tested on two benchmark examples in order to demonstrate its performance increase compared to the tuning functions based adaptive backstepping scheme.
Transient performance improvement of adaptive control of a class of single-input single-output (SISO) non-linear systems is considered in this study. The system under study is assumed to be minimum-phase and input-output linearisable. The non-linear dynamics is also assumed to be linearly parameterised in terms of the unknown parameters. The improvement in the transient performance under large parametric uncertainties is obtained with the use of multiple identification models and switching, and the closed loop system is shown to be stable with the switching mechanism. A new methodology is proposed for the quantitative evaluation of the transient performance. The study is verified by simulation of a non-linear physical system.
We consider a class of minimum-phase non-linear systems with large parametric uncertainties. The non-linear dynamics is assumed to be linearly parameterized in terms of the unknown parameters. A novel scheme which utilizes multiple models in a model reference adaptive control (MRAC) framework is proposed to improve the transient performance of the adaptive scheme. The proposed approach makes use of fixed models from a compact and partitioned parameter space and resets the parameter update dynamics to the model which gives a negative jump to the control Lyapunov function. The overall stability of closed loop system under the switching is preserved based on the Lyapunov approach. A simulation study is given in order to demonstrate the efficient use of the algorithm.
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