The visual servoing strategy of a continuum robot has wide-ranging applications and significant engineering value. However, limited by the accuracy of its motion control, research on visual servoing of continuum robots is currently limited to close-range conditions (distance < 1m). Therefore, a novel visual servoing strategy for continuum robots is proposed in this paper that can track external targets in real-time and across distance from centimeters to tens of meters. Firstly, a continuous mapping relationship was proposed by integrating the visual coordinate transformation process with the mechanical model of the continuum robot, establishing a mapping relationship between pixel error and drive values. Secondly, a closed-loop control system for trajectory tracking of moving targets is designed by combining the continuous mapping relationship. Finally, trajectory tracking experiments are conducted on a moving target (i.e., human-shaped target) at different distance scales. Compared with the eye-in-hand strategy at different distances (0.2m, 2m and 3m), the experimental results amply demonstrate the tracking accuracy and stability of the proposed visual servoing strategy. Furthermore, under the experimental condition of long distance (20.5m), the proposed visual servoing strategy can still achieve accurate tracking of the moving target, with an average tracking error of 27.97 pixels (about 2.31cm).

Trajectory tracking control of flexible continuum robots is challenging due to their inherent compliance and high nonlinearity. Many related works exclude the control of the end's orientation, i.e., only the end's position is considered. In this article, a differential-algebraic equations (DAEs) model-based instantaneous optimal control (IOC) framework for the end's position and orientation cooperative tracking of a cable-driven tensegrity continuum robot (TCR) is developed. Based on the tensegrity concept, a TCR is designed first as the control object, which can achieve multimode deformations such as bending, scoliosis, contraction, and the S- or J-shape. Then, the actuation of cables is introduced as the system kinematic constraints from the view of multibody dynamics so that a control-oriented model of the TCR can be built by DAEs. Subsequently, the original continuous trajectory tracking problem is approximated for a series of IOC problems at each discrete time slot. Finally, considering the constraints of control input saturation, a linear complementarity problem was derived for solving these IOC problems. The method provides an easy-to-implement and unified framework for addressing the trajectory tracking control issues of cable-driven continuum robots, which can improve the control performance of the position-only tracking controllers and exploit the TCR's advantages to handle more application scenarios. The advanced performance and potential applications of the proposed controller have been evaluated via several numerical simulations and experiments on the TCR prototype.

Active shape control for an antenna reflector is a significant procedure used to compensate for the impacts of a complicated space environment. In this article, a physics-guided distributed model predictive control (DMPC) framework for reflector shape control with input saturation is proposed. First, guided by the actual physical characteristics, an overall structural system is decomposed into multilevel subsystems with the help of a so-called substructuring technique. For each subsystem, a prediction model with information interaction is discretized by an explicit Newmark- β method. Then, to improve the system-wide control performance, a coordinator among all the subsystems is designed in an iterative fashion. The input saturation constraints are addressed by transforming the original problem into a linear complementarity problem (LCP). Finally, by solving the LCP, the input trajectory can be obtained. The performance of the proposed DMPC algorithm is validated through an experiment on the shape control of an antenna reflector structure.