Sequential composition is a supervisory control architecture for addressing control problems in complex dynamical systems. Although sequential composition works properly for a single system, it is not designed for cooperative systems. This paper extends the standard sequential composition by introducing a novel approach to compose multiple sequential composition controllers towards cooperative control. Given two or more systems, cooperation is achieved by composing each of the systems' control automaton, together with estimation for the domains of attraction of the resulting composed controllers. This typically results in new events for the original sequential composition controllers. Applying these events, the cooperative control system can fulfill the tasks which are not possible to satisfy with the original controllers individually. The simulation results of an inverted pendulum system collaborating with two second-order DC motors are presented for cooperative swing-up maneuvers.

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This paper proposes a new approach to robotic manipulation planning based on the contact between a set of objects, robots and surfaces. We consider making or breaking contact as the most abstract, yet representative element of a manipulation task. Using this paradigm, a robotic manipulation planner has been developed. Given an environment with robots and objects, a manipulation graph is generated by a set of rules and the available geometrical information. Next, the object manipulation planning is formulated as a graph search problem. Paths on this graph divide a complex manipulation task into sub-tasks, followed by low-level path planning and controller assignment for each sub-task. By sequentially executing these controllers in a hybrid fashion, one achieves the overall manipulation task.

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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.@en

Most stabilizing controllers designed for nonlinear systems are valid only within a specific region of the state space, called the domain of attraction (DoA). Computation of the DoA is usually costly and time-consuming. This paper proposes a computationally effective sampling approach to estimate the DoAs of nonlinear systems in real time. This method is validated to approximate the DoAs of stable equilibria in several nonlinear systems. In addition, it is implemented for the passivity-based learning controller designed for a second-order dynamical system. Simulation and experimental results show that, in all cases studied, the proposed sampling technique quickly estimates the DoAs, corroborating its suitability for real-time applications.

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