We consider the problem of distributed convergence to efficient outcomes in coordination games through dynamics based on aspiration learning. Our first contribution is the characterization of the asymptotic behavior of the induced Markov chain of the iterated process in terms of an equivalent finite-state Markov chain. We then characterize explicitly the behavior of the proposed aspiration learning in a generalized version of coordination games, examples of which include network formation and common-pool games. In particular, we show that in generic coordination games the frequency at which an efficient action profile is played can be made arbitrarily large. Although convergence to efficient outcomes is desirable, in several coordination games, such as common-pool games, attainability of fair outcomes, i.e., sequences of plays at which players experience highly rewarding returns with the same frequency, might also be of special interest. To this end, we demonstrate through analysis and simulations that aspiration learning also establishes fair outcomes in all symmetric coordination games, including common-pool games.

Friction is responsible for several servomechanism limitations, and their elimination is always a challenge for control engineers. In this paper, model-based feedback compensation is studied for servomechanism tracking tasks. Several kinetic friction models are employed and their parameters identified experimentally. The effects of friction compensation on system response are examined using describing function analysis. A number of control laws including classical laws, rigid body motion models, and friction compensation are compared experimentally in large-displacement tasks. Results show that the best response is obtained using a controller that incorporates a rigid body model and a friction model based on an accurate description of identified kinetic friction effects.