In this paper, we propose a decentralized robust control algorithm for modular and reconfigurable robots (MRRs) based on Lyapunov's stability analysis and backstepping techniques. In using decentralized control schemes with robot manipulators, each joint is considered as an independent subsystem, and the dynamical effects from the other links and joints are treated as disturbance. However, there exist many uncertainties due to unmodeled dynamics, varying payloads, harmonic drive (HD) compliance, HD complex gear meshing mechanisms, etc. Also, while the reconfigurability of MRRs is advantageous, modifying the configuration will result in changes to the robot dynamics parameters, thereby making it challenging to tune the control system. All of the above mentioned disturbances in addition to reconfigurability present a challenge in controlling MRRs. The proposed controller is well suited for MRR applications because of its simple structure that does not require the exact knowledge of the dynamic parameters of the configurations. Desired tracking performance can be achieved via tuning a limited set of parameters of the robust controller. If the numbers of degrees of freedom are held constant, these parameters are shown to be relatively independent of the configuration, and can be held constant between changes in configuration. This strategy is novel compared to existing MRR control methods. In order to validate the controller performance, experimental setup and results are also presented.

A major technical challenge in controlling modular and reconfigurable robots is associated with the kinematics and dynamic model uncertainties caused by reconfiguration. In parallel, conventional model uncertainties such as uncompensated joint friction still persist. This paper presents a modular distributed control technique for modular and reconfigurable robots that can instantly adapt to robot reconfigurations. Under the proposed control method that is based on joint torque sensing, a modular and reconfigurable robot is stabilized joint by joint, and modules can be added or removed without the need to adjust control parameters of the other modules of the robot. Model uncertainties associated with link and payload masses are compensated using joint torque sensor measurement. The remaining model uncertainties, including uncompensated dynamic coupling and joint friction, are compensated by a decomposition-based robust controller. Simulation results have confirmed the effectiveness of the proposed method.