Rapid learning in sequential composition control

Sequential composition is an effective approach to address the control of complex dynamical systems. However, it is not designed to cope with unforeseen situations that might occur during runtime. This paper extends sequential composition control via learning new policies. A learning module based on reinforcement learning is added to the traditional sequential composition that allows for the online creation of new control policies in a short amount of time, on a need basis. During learning, the domain of attraction (DOA) of the new control policy is continuously monitored. Hence, the learning process only executes until the supervisor is able to compose the new control policy with designed controllers via the overlap of DOAs. Estimating the DOAs of the learned controllers is achieved by solving an optimization problem. The proposed strategy has been simulated on a nonlinear system. The results show that the learning module can rapidly augment the designed sequential composition by new control policies such that the supervisor could handle unpredicted situations online.