Decentralized adaptive partitioned approximation control of high degrees-of-freedom robotic manipulators considering three actuator control modes

International audiencePartitioned approximation control is avoided in most decentralized control algorithms; however, it is essential to design a feedforward control term for improving the tracking accuracy of the desired references. In addition, consideration of actuator dynamics is important for a robot with high-velocity movement and highly varying loads. As a result, this work is focused on decentralized adaptive partitioned approximation control for complex robotic systems using the orthogonal basis functions as strong approximators. In essence, the partitioned approximation technique is intrinsically decentralized with some modifications. Three actuator control modes are considered in this study: (i) a torque control mode in which the armature current is well controlled by a current servo amplifier and the motor torque/current constant is known, (ii) a current control mode in which the torque/current constant is unknown, and (iii) a voltage control mode with no current servo control being available. The proposed decentralized control law consists of three terms: the partitioned approximation-based feedforward term that is necessary for precise tracking, the high gain-based feedback term, and the adaptive sliding gain-based term for compensation of modeling error. The passivity property is essential to prove the stability of local stability of the individual subsystem with guaranteed global stability. Two case studies are used to prove the validity of the proposed controller: a two-link manipulator and a six-link biped robot

Design of Adaptive Controllers based on Christoffel Symbols of First Kind

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Dynamic movement primitives-based human action prediction and shared control for bilateral robot teleoperation

This article presents a novel shared-control teleoperation framework that integrates imitation learning and bilateral control to achieve system stability based on a new dynamic movement primitives (DMPs) observer. First, a DMPs-based observer is first created to capture human operational skills through offline human demonstrations. The learning results are then used to predict human action intention in teleoperation. Compared with other observers, the DMPs-based observer incorporates human operational features and can predict long-term actions with minor errors. A high-gain observer is established to monitor the robot’s status in real time on the leader side. Subsequently, two controllers on both the follower and leader sides are constructed based on the outputs of the observers. The follower controller shares control authorities to address accidents in real-time and correct prediction errors of the observation using delayed leader commands. The leader controller minimizes position-tracking errors through force feedback. The convergence of the predictions of the DMPs-based observer under the time delays and teleoperation system stability are proved by building two Lyapunov functions. Finally, two groups of comparative experiments are conducted to verify the advantages over other methods and the effectiveness of the proposed framework in motion prediction with time delays and obstacle avoidance