Publications - Conference Paper

In this paper, the problem of the stabilization of a discrete-time linear system subject to a fixed and uniformly quantized control set is considered. It is well known that, working with quantized inputs, the states of the system (except for a negligible set of initial conditions) cannot reach asymptotically the equilibrium point. Our aim is then to find an invariant and attractive neighborhood of the equilibrium and provide with a controller which steers the system into it. We construct a continuous and increasing family of invariant sets including one which is, in a specific sense, minimal. The invariance and attractivity properties of such sets are revised in the finite control set case: we propose a family of controllers taking on a finite number of values and ensuring the system convergence to the minimal invariant set. Some consequences of our technique are underlined with particular regard to the usage of Model Predictive Control tools. In the last section an example which shows the effectiveness of our results is presented.

In this paper, an independent joint position and stiffness adaptive control for a robot arm actuated by McKibbenartificial muscles is reported. In particular, {\em muscular} nd {\em dynamic} parameters of the system are supposed unknown. Adaptive control performance is tested in a one degree of freedom experimental setup and compared with PID control performance. The adaptive control scheme is then applied to a robot arm that is conceived to perform tasks in an anthropic environment. The adaptive control developed is such that performance of the robot arm is very similar to this of human arm. Experimental results are reported.