Toward safe operation of bilateral teleoperation in constrained spaces: An integral barrier Lyapunov function–based adaptive control approach

In practical teleoperation engineering applications, operator negligence can easily lead to collisions of the remote robot in constrained environments. To address this, this research investigates the safety control and path following of teleoperated robotic systems in constrained task spaces, proposing a novel control framework that integrates an integral barrier Lyapunov function with a soft saturation function. The core of this framework is the construction of an adaptive learning controller based on integral barrier Lyapunov function. Unlike traditional approaches, the adopted integral barrier Lyapunov function here enforces constraints directly on the robot’s actual states instead of the tracking error. This ensures that the operation never violates the predefined spatial constraints. Meanwhile, by introducing a second-order filter and a soft saturation function on the remote side to generate a differentiable reference trajectory, it is ensured that any trajectory mapped from the master side remains strictly within the constrained interior. Furthermore, a radial basis function neural network is employed to approximate and offset modeling uncertainties in the system online, enhancing both robustness and tracking accuracy. Utilizing Lyapunov stability criteria, it is analytically demonstrated that both the local and remote controllers of the bilateral teleoperation system remain stable, with all state signals being semi-globally uniformly ultimately bounded. Finally, physical experiments are conducted to verify that the proposed method can ensure the remote robot moves within the prescribed constraints while providing force feedback. Comparative studies with other control methods further demonstrate its effective control performance.