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Iterative learning control (ILC) is applied to a robot arm with joint flexibility. The ILC algorithm uses an estimate of the arm angle, where the estimate is computed using measurements of the motor angle and the arm angular acceleration. The design of the ILC algorithm is evaluated experimentally on a laboratory scale robot arm with good results. [ABSTRACT FROM AUTHOR]

The idea of the multi-controller approach is to design local controllers, each of them combined with a special environment. One problem of this approach is to select the best local controller or even several controllers in order to design the final control law, which will be applied to the plant. In this paper, a 'switching' algorithm based on a symbolic fuzzy system is used. The main goal is basically to blend the control signals provided by several controllers, which are modelreference output feedback controllers (RST controllers) in our method. Nonetheless, an interesting issue is the stability of the global structure with a reference signal. In this paper, we present research studies on the stability analysis of the proposed multi-controller approach by using Lyapunov theory and linear matrix inequalities (LMIs). The derivative of a candidate quadratic Lyapunov function is calculated thanks to a weighted sum of local derivatives. Finally, a real-time experimentation on a robot wrist shows the effectiveness of the approach. [ABSTRACT FROM AUTHOR]