This article focuses on the performance analysis of distributed controllers for general linear multiagent systems in the sense of convergence time and energy consumption. First, the specified-time optimal controller is obtained using the linear operator theory-based method, and then the optimal topology is deduced. Second, to analyze the impact of communication topology on energy consumption, two distributed, suboptimal specified-time controllers are developed for undirected and directed graphs, respectively. By utilizing the inverse optimality method and Lyapunov function scaling, the performance in terms of the bounds of the gaps between the energy consumption of the suboptimal and optimal control laws is derived, which evaluates the effectiveness of the suboptimal controllers. Finally, as the simulation results show, the performance can specify appropriate settling times for applications with different energy budgets and facilitate optimizing the communication topology to reduce the energy gap.

When teleoperating a multirobot system (MRS) in outdoor environments, human operators can often detect obstacles that are not detected by robots and spot emergencies faster than robots do. However, the lack of efficient methods for operators to manipulate an MRS has limited the number of robots in a human-robot team. To handle this problem, a distributed hierarchical shared control scheme is proposed, aiming to provide a safe and flexible control interface for a few human operators to interact with a large MRS. The proposed hierarchical control scheme employs a two-layered structure. In the upper layer, intention field networks are designed to generate virtual human control signals. Two functionalities for human teleoperation, called: 1) group management and 2) motion intervention, are realized using intention fields, allowing the operators to split the robot formation into different groups and steer individual robots away from immediate danger. In parallel, a blending-based shared control algorithm is designed in the lower layer to resolve the conflict between human intervention inputs and autonomous formation control signals. The input-to-output stability (IOS) of the proposed distributed hierarchical shared control scheme is proved by exploiting the properties of weighting functions. Results from a usability testing experiment and a physical experiment are also presented to validate the effectiveness and practicability of the proposed method.

In this paper, we discussed the simultaneous intervention problem that arises from human-robot teams. The problem concerns with the case that one human operator has to intervene with several robots at almost the same time and it cannot be handled properly by existing methods. A model predictive control(MPC) based mixed initiative controller was proposed to solve this problem by embedding an intention model into the optimization problem. This method is in essence suitable for general MPC based mixed initiative controllers. The embedded intention model may cause the robot to deviate from its desired trajectory, for which conditions were developed to guarantee that the task completeness is not be affected. A comparison experiment was conducted and results showed that the proposed controller is more efficient and helpful in shortening the intervention time and allowing more robots to be successfully intervened in a simultaneous intervention scenario.