Cloud-based collaborative learning of optimal feedback controllers

Industrial systems deployed in mass production, such as automobiles, can greatly benefit from sharing selected data among them through the cloud to self-adapt their control laws. The reason is that in mass production systems are clones of each other, designed, constructed, and calibrated by the manufacturer in the same way, and thus they share the same nominal dynamics. Hence, sharing information during closed-loop operations can dramatically help each system to adapt its local control laws so to attain its own goals, in particular when optimal performance is sought. This paper proposes an approach to learn optimal feedback control laws for reference tracking via a policy search technique that exploits the similarities between systems. By using resources available locally and on the cloud, global and local control laws are concurrently synthesized through the combined use of the alternating direction method of multipliers (ADMM) and stochastic gradient descent (SGD). The enhancement of learning performance due to sharing knowledge on the cloud is shown in a simple numerical example.