Distributed Robust Adaptive Control of Cooperative Robotic Agents With Input Allocation

In this work, a distributed indirect adaptive controller is designed for a group of robotic agents cooperatively manipulating a common payload. Uncertainty on the model of the manipulated object and the limited actuation capabilities of the single agents can significantly impact the overall behavior of the control system. An indirect adaptive control scheme is proposed in this article to address these shortcomings. In particular, model uncertainty and loss of effectiveness of the actuators are handled in a unifying fashion by an adaptive control architecture that preserves physical consistency of the estimated inertial parameters of the manipulated object, while simultaneously providing an antiwindup mechanism for the estimated inertial parameters in case of actuator saturation. In addition, a dynamic input allocation strategy is proposed to distribute the control effort among the agents in such a way that the intrinsic input redundancy of the overall setup is exploited for dynamic optimization of additional performance criteria, including optimization of the control efforts on each agent. The stability of the closed-loop system is proven theoretically, and the performance and robustness of the control system are validated by means of comparative simulations with respect to a baseline state-of-the-art controller.