Target capturing is an essential and key mission for tethered space robot (TSR) in future on-orbit servicing, and it is quite meaningful to investigate the stabilization method for TSR during capture impact with target. In this paper, the stabilization control of TSR during target capturing is studied. The space tether is described by the lumped mass model, and the impact dynamic model for target capturing is derived using the Lagrange method with the consideration of space tether length, in/out-plane angles, and gripper attitude. Given the structure of the TSR's gripper, a position-based impedance control method is proposed for target capturing operation. The neural network is used to estimate and compensate the uncertainties in the dynamic model of TSR, and an adaptive robust controller is designed to overcome the influence of the space tether and track the desired position generated by impedance controller. Numerical simulations suggest that the proposed controller can realize the stabilization of TSR during target capturing; besides, the uncertainties of the TSR can effectively be compensated via adaptive law and the influence of the space tether can be suppressed via the robust control strategy, which lead to smaller overshoot, less convergence time, and higher control accuracy during capturing operation.

This article addresses the design problem of distributed event-triggered average tracking (DETAT) algorithms for homogeneous and heterogeneous multiagent systems. The objective of the DETAT problem is to develop a group of distributed cooperative control algorithms with event-triggered strategies for agents to track the average of multiple time-varying reference signals. First, for homogeneous linear multiagent systems, based on sampling measurements and model-relied holding techniques, a class of static-gain DETAT algorithms is proposed with a couple of local event-triggered functions for estimators and controllers, respectively. Compared with the existing distributed average tracking (DAT) algorithms, the static-gain DETAT algorithms greatly reduce the cost over communication networks and the frequency of control protocol updates. Second, to reduce the chattering phenomenon caused by nonsmooth items in static-gain algorithms and requirements of the global information of networks, smooth dynamic-gain DETAT algorithms are introduced based on boundary layer approximation methods and self-adaptive principles. Third, for heterogeneous linear multiagent systems, a new algorithm is established by using the output regulation techniques for the heterogeneous DETAT problem. The outputs of heterogeneous agents can ultimately track the average of multiple time-varying reference signals. To the best of our knowledge, it is the first time to study the DETAT problem for heterogeneous multiagent systems. Finally, some examples are presented to show the validity of theoretical results.

In this brief, we focus on the study of formation tracking problem for a class of multiagent systems with nonlinear dynamics and external disturbances in the presence of relative distance constraints. A novel distributed formation control strategy is proposed based on an integration of radial basis function neural network (NN) with artificial potential field method. The relative distance constraints between arbitrary adjacent agents can be ensured by the artificial potential function. Based on the NN approximation property, it has been proposed to neutralize the nonlinear dynamics in agents. To account for the negative influence of the approximation error and external disturbances, a robustness term is employed. Finally, based on algebraic graph theory, matrix theory, and Barbalat's lemma, some sufficient conditions are established to accomplish the asymptotical stability of the systems for a given communication graph. The study is with application to tethered space net robot. The simulation results are performed to illustrate the performance of the proposed strategy.

This article proposes a variable structure control with neural network and optimized fractional-order selection policy for the sensorless telerobotic system with uncertain time delay, model uncertainty, fractional calculus numerical approximation bias, and external disturbance. Expanded from the integer-order calculus based on the definition of Caputo and the comparison principle, the Riemann–Liouville-based uniformly ultimately bounded stability is introduced to synthesize the sliding mode control with varying order, and the corresponding neural network based force estimation, which is optimized by the specified order based on the greed algorithm. Combined with the mixed-type error, the proposed hybrid integer-order and fractional-order analysis methodology is capable of guaranteeing the Riemann–Liouville-based uniformly ultimately bounded stability of tracking and synchronization errors under time delay. According to Euler difference, the proposed synthesis method can suppress the combined error in the boundedness of approximating double Euler difference errors. Numerical simulation and experiment of the telerobotic system under the control of the proposed method verify the stability analyses.