Sequential composition is an effective supervisory control method for addressing control problems in nonlinear dynamical systems. It executes a set of controllers sequentially to achieve a control specification that cannot be realized by a single controller. Sequential composition focuses on the interaction between a collection of pre-designed controllers, where each of them is associated with a domain of attraction (DoA) and a goal set. By design, if the goal set of one controller lies in the DoA of another controller (this is called the prepare relation), the supervisor can instantly switch from the first controller to the second without affecting the stability of the system. As these controllers are designed offline, sequential composition cannot address unmodeled situations that might occur during runtime. Moreover, sequential composition has not been developed for cooperative settings where the collaboration of multiple systems is required in order to fulfill the control specifications.

This thesis studies automatic synthesis of supervisory control systems using the framework of sequential composition. First, a learning sequential composition control algorithm is developed so as to learn new controllers on demand, by means of reinforcement learning. Once learning is completed, the supervisory control structure is augmented with the learned controllers. As a consequence, the supervisor is able to cope with unforeseen situations for which new controllers are required. Second, a cooperative sequential composition control algorithm is proposed to enable the coordination between a set of sequential composition controllers, without any change in their low-level structures. Finally, a robot contact language is designed for the manipulation of multiple objects by multiple robots.