Visual servoing systems provide higher safety and autonomy when dealing with unstructured environments. Visual and force sensing are essential for accurate and reliable robotic manipulation. In this article, a novel image-based visual-admittance control with prescribed performance of manipulator is presented. The method couples visual and force information in feature space, which avoids control limitations due to inconsistent driver layers and improves system flexibility. Thanks to the prescribed performance and the tan-type barrier Lyapunov function, the image feature error is satisfied under the finite field-of-view constraint, which improves the transient and steady-state performance of the visual servoing system. The proposed image-based visual-admittance control with prescribed performance is experimentally validated. The results show that the control framework improves the transient/steady-state response and task success of the system.

This paper studies the target‐tracking problem of underactuated surface vessels with model uncertainties and external unknown disturbances. A composite robust adaptive self‐structuring neural‐network‐bounded controller is proposed to improve system performance and avoid input saturation. An extended state observer is proposed to estimate the uncertain nonlinear term, including the unknown velocity of the tracking target, when only the measurement values of the line‐of‐sight range and angle can be obtained. An adaptive self‐structuring neural network is developed to approximate model uncertainties and external unknown disturbances, which can effectively optimize the structure of the neural network to reduce the computational burden by adjusting the number of neurons online. The input‐to‐state stability of the total closed‐loop system is analyzed by the cascade stability theorem. The simulation results verify the effectiveness of the proposed method.

Visual features are often lost during fast servoing due to the limited field of view (FOV) of the camera. In this article, a method for robust fixed-time image-based visual servoing (IBVS) with prescribed performance is proposed to tackle the aforementioned problems. First, a performance function is introduced to preset performance specifications of the image feature errors. This way, the image feature points are kept in the FOV of the camera during the visual servoing process. Next, the implementation of performance constraints on image feature point errors is accomplished using a barrier Lyapunov function (BLF) to improve the steady-state accuracy. Finally, fast stabilization of the system is ensured by the fixed-time Lyapunov stability theorem. The proposed method is implemented for visual servoing of a robotic arm. The experimental results show that the convergence rate is significantly improved compared to the existing methods. The image feature points remain in the FOV of the camera during the fast servoing process, too. Additionally, a bolt visual servoing alignment example study is carried out to demonstrate the applicability of the proposed method in practice.

With the rapid progression of industry, robotic assembly technologies have garnered significant attention. To enable robots to perform assembly tasks autonomously and intelligently in unstructured environments without relying on precise environmental modeling, many robotic systems incorporate external perception sensors. Among these, the integration of vision and force sensors proves to be both cost-effective and complementary, enhancing system safety and task success by providing real-time sensory feedback. This article proposes a general framework for image-based variable admittance control, which couples force and visual information within the image feature space, thereby avoiding control limitations arising from inconsistent actuation layers. The variable admittance control adjusts stiffness parameters dynamically across different task phases, enhancing flexibility and interaction with the environment. To enhance the transient and steady-state performances of the system, the control design utilizes an asymmetric barrier Lyapunov function with a specified error bound as a time-varying constraint. The asymmetric barrier Lyapunov function enforces a strict predefined range for feature coordinate errors, thereby improving both the transient and steady-state performances of the system. The proposed image-based variable admittance control is experimentally validated. The results show that the proposed framework improves the system’s transient/steady-state response, safety, and environmental interaction.