This article presents an adaptive control method for dual-arm robot systems to perform bimanual tasks under modeling uncertainties. Different from the traditional symmetric bimanual robot control, we study the dual-arm robot control with relative motions between robotic arms and a grasped object. The robot system is first divided into two subsystems: a settled manipulator system and a tool-used manipulator system. Then, a command filtered control technique is developed for trajectory tracking and contact force control. In addition, to deal with the inevitable dynamic uncertainties, a radial basis function neural network (RBFNN) is employed for the robot, with a novel composite learning law to update the NN weights. The composite learning is mainly based on an integration of the historic data of NN regression such that information of the estimate error can be utilized to improve the convergence. Moreover, a partial persistent excitation condition is employed to ensure estimation convergence. The stability analysis is performed by using the Lyapunov theorem. Numerical simulation results demonstrate the validity of the proposed control and learning algorithm.

Nowadays, due to many limitations in reality, the optimization of tracking precision and convergence time has attracted the attention of researchers in robotics community. In this article, a fixed-time adaptive neural network (NN) controller is proposed for unknown robot manipulators. A switching mechanism is integrated into the control design such that the semiglobal stability of the conventional NN control systems can be extended to global stability. The time-varying barrier Lyapunov function and the fixed-time control technique are incorporated into the controller design to guarantee the prescribed motion constraints and the fixed-time convergence simultaneously. Compared with some existing fixed-time NN control algorithms, the assumption that NN weight should be upper bounded can be relaxed in our work. Finally, the simulation and experiment studies are respectively carried out based on an unknown 2-degree of freedom robot and a Baxter robot to demonstrate the effectiveness and superiority of the proposed control scheme.

In this paper, an adaptive fixed-time fuzzy control scheme is proposed for an uncertain robot manipulator with user-defined performance. A novel symmetrical barrier Lyapunov function (BLF) is designed based on the error conversion mechanism and the performance function such that the tracking errors will not violate the prescribed output constraints. A novel adaptive law is constructed and incorporated into the fixed-time controller design such that all the closed-loop signals can be bounded and achieve practical fixed-time convergence regardless of the initial conditions. Finally, the feasibility and superiority of the proposed scheme are demonstrated based on simulation and experiment studies using a Baxter robot.

Physical human-robot interaction (pHRI) plays an important role in robotic. In order for a human operator to be able to easily adapt to interact with a robot, a minimal interaction force in pHRI should be achieved. In this paper, a pHRI framework is proposed to allow the robot to regulate its trajectory adaptively for minimizing the interaction force with small position-tracking errors. The trajectory of the robot is first adjusted by the interaction force which is updated by the performance evaluation index. Then, the human hand motion is predicted based on the autoregressive (AR) model to further adapt the trajectory. Thirdly, an adaptive impedance control method is developed to update the stiffness in the robot impedance controller using surface electromyography (sEMG) signals for robot compliant interaction with the environment. This method allows the human operator to interact with the robot by the interaction force, the hand motion and muscle contraction. By investigating the performance of the proposed method, the interaction force is decreased and a good position tracking accuracy is achieved. Comparative experiments demonstrate the enhanced performance of the proposed method. Note to Practitioners —This paper focuses on developing a novel method that can allow the robot to compliantly interact with the human operator while simultaneously taking into account the trajectory-tracking accuracy and the interaction force in pHRI scenarios. The proposed method has a large application potential in a variety of pHRI tasks, such as human-robot collaborative transporting, curing, assembly, cutting, and so on. In addition, the proposed method can allow the human operator to physically interact with the robot in an easier and more intuitive manner, by taking advantage of human motion prediction and adaptive impedance control. Therefore, it is also potentially utilized for rehabilitation and assistive robots, and robot learning skills from human physical demonstration.

The integration of advanced sensor technologies has significantly propelled the dynamic development of robotics, thus inaugurating a new era in automation and artificial intelligence. Given the rapid advancements in robotics technology, its core area—robot control technology—has attracted increasing attention. Notably, sensors and sensor fusion technologies, which are considered essential for enhancing robot control technologies, have been widely and successfully applied in the field of robotics. Therefore, the integration of sensors and sensor fusion techniques with robot control technologies, which enables adaptation to various tasks in new situations, is emerging as a promising approach. This review seeks to delineate how sensors and sensor fusion technologies are combined with robot control technologies. It presents nine types of sensors used in robot control, discusses representative control methods, and summarizes their applications across various domains. Finally, this survey discusses existing challenges and potential future directions.