This paper concerns a distributed control strategy to deal with possible actuator outages of multi-agent systems (MASs). With the designed strategy, the control process is monitored by a fault detection system (FDS). Once the actuator of one agent in use partially or completely fails, the FDS will detect the fault timely and a controller activator will produce its effect. By this means, the healthy actuator can replace the faulty one for control at the delicately designed instants, and the prescribed performance of the MAS can always be satisfied in the presence of possible actuator outages with the help of prescribed performance functions. Moreover, to achieve more diversified constraints to meet wider requirements in practice, the proposed strategy is further extended to combine with a time-varying barrier Lyapunov function so that the time-varying output constraint can be achieved, which makes the designed fault-tolerant control more flexible and sensitive in dealing with failures. With the proposed distributed control method, uncertain actuator failures, especially the actuator outage, can be addressed in the presence of disturbances and uncertain dynamics. Since the failure is allowed to occur multiple times with only one actuator operating for control at any time, the designed approach can be more economical in energy saving compared with the existing methods. Numerical simulations are provided for multiple agents to verify the effectiveness of the proposed algorithm. Note to Practitioners—In this paper, a control strategy is proposed to handle the actuator failure of a class of MASs. To deal with possible actuator outages, which pose a threat to system safety, actuator redundancy is introduced in the proposed strategy. By developing a controller activator to specify the activation time of healthy actuators with the help of a designed FDS, the prescribed performance of the agents can always be guaranteed and the tracking error can be constrained within specified time-varying function curves as expected. Since the proposed strategy enables the successive triggering of actuators, only one actuator works at any instant, which contributes to its energy-saving efficiency. Since the proposed strategy can accommodate possible actuator outages in an economical way with guaranteed prescribed performance, it can be used in various industrial scenarios where high safety of agents is required, for instance, multiple aircrafts, multiple high-speed trains, and multiple industrial robot systems.

In human-robot collaboration (HRC) tasks, the role of the robot should be naturally and smoothly transitioned between the leader and the follower to guarantee task performance. To realize this, the arbitration of the shared control between the human and the robot needs to be properly designed to assign a degree of leadership to the robot. In this paper, we propose a fuzzy logic-based arbitration rule with the help of Kalman filter (KF). Based on this rule, the arbitration can be continuously regulated between zero and one according to the interaction force and the velocity of the human-robot collaboration system with human intention taken into account. Besides, the distance between the system and the obstacle and more generally the environment is also served as a fuzzy input, so that the possible interaction with the environment, e.g., obstacle avoidance, can be considered to ensure system safety. Since our proposed arbitration rule is based on a fuzzy logic, it endows the robot with the capability of continuous reasoning without an explicit form. The effectiveness of the proposed algorithm is evaluated by experiments.

One promising function of interactive robots is to provide a specific interaction force to human users. For example, rehabilitation robots are expected to promote patients' recovery by interacting with them with a prescribed force. However, motion uncertainties of different individuals, which are hard to predict due to the varying motion speed and noises during motion, degrade the performance of existing control methods. This article proposes a method to learn a desired reference trajectory for a robot based on dynamic motion primitives (DMPs) and iterative learning (IL). By controlling the robot to follow the generated desired reference trajectory, the interaction force can achieve a desired value. In our proposed approach, DMPs are first employed to parameterize the demonstration trajectories of the human user. Then, a recursive least square (RLS)-based estimator is developed and combined with the Adam optimization method to update the trajectory parameters so that the desired reference trajectory of the robot is iteratively obtained by resolving the DMPs. Since the proposed method parameterizes the trajectories depending on the phase variable, it removes the essential assumption of traditional IL methods that the iteration period should be invariant, and thus, has improved robustness compared with the existing methods. Experiments are performed using an interactive robot to validate the effectiveness of our proposed scheme.

A popular path learning method is to use off-line programming by demonstration (PbD) to plan a rough path, but it is subjected to uncertainties in the environment so needs to be updated during the task execution. For this purpose, a spatial iterative learning control (sILC) is developed to learn an accurate path through intuitive online correction based on human-robot interaction (HRI). To improve the efficiency and accuracy of the path learning, a visual assistance system is added to HRI, which helps the robot to find the initial path point and complement the correction of the learning error. This method mitigates the requirement on classic ILC that the time period should be consistent in the repetitive interaction task and utilizes the complementary advantages of vision and force sensing, thus addressing the limitations of the vision-based or HRI methods. The rigorous proof of learning convergence and the results of the simulation and experiments on a 7-degree-of-freedom (DoF) Sawyer robot platform illustrate the significance and advantages of the proposed method.Note to Practitioners—The problem of accurate path learning of robotic manipulators is addressed in this paper, which is found in ample applications such as welding and laser cutting. When the required path is irregular, it is difficult to define it based on offline programming and calibration. This paper presents a new human-robot interactive learning framework, in which the interaction force and machine vision are combined with sILC to achieve online detection and correction for learning and tracking an unknown path. This framework leads to an intuitive human-robot collaboration system where the human operator can fine tune the robot’s motion through direct physical interaction, and at the same time the robot improves its tracking performance automatically based on visual servoing.

Previous works have developed impedance control to increase safety and improve performance in contact tasks, where the robot is in physical interaction with either an environment or a human user. This article investigates impedance learning for a robot guided by a human user while interacting with an unknown environment. We develop automatic adaptation of robot impedance parameters to reduce the effort required to guide the robot through the environment, while guaranteeing interaction stability. For nonrepetitive tasks, this novel adaptive controller can attenuate disturbances by learning appropriate robot impedance. Implemented as an iterative learning controller, it can compensate for position dependent disturbances in repeated movements. Experiments demonstrate that the robot controller can, in both repetitive and nonrepetitive tasks: first, identify and compensate for the interaction, second, ensure both contact stability (with reduced tracking error) and maneuverability (with less driving effort of the human user) in contact with real environments, and third, is superior to previous velocity-based impedance adaptation control methods.

In this article, an iterative-learning-based robotic controller is developed, which aims at providing a prescribed assistance or resistance force to the human user. In the proposed controller, the characteristic parameter of the human upper limb movement is first learned by the robot using the measurable interaction force, a recursive least square (RLS)-based estimator, and the Adam optimization method. Then, the desired trajectory of the robot can be obtained, tracking which the robot can supply the human’s upper limb with a prescribed interaction force. Using this controller, the robot automatically adjusts its reference trajectory to embrace the differences between different human users with diverse degrees of upper limb movement characteristics. By designing a performance index in the form of interaction force integral, potential adverse effects caused by the time-related uncertainty during the learning process can be addressed. The experimental results demonstrate the effectiveness of the proposed method in supplying the prescribed interaction force to the human user. Note to Practitioners—This article concentrates on developing a novel control technique to make the robot supply a prescribed interaction force to the human user in the presence of time-related uncertainties. The proposed control method is applicable to various scenarios of the human–robot interaction, e.g., it can be used for rehabilitation robots to provide assistive or resistive force to stroke patients or for exoskeleton robots to provide assistive force to human users for completing heavy-load tasks. Moreover, the desired interaction force can be tailored for different human users according to their needs and different task objectives. Consequently, the proposed controller can serve diverse users and has a promising perspective in automation.

This article aims at proposing an adaptive neural control strategy for a class of nonlinear time-delay systems with input delays and unknown control directions. Different from previous researches that investigated delays and constraints separately, the novelty of this article lies in that it simultaneously considers delays (state and input delays) and input constraints (magnitude and rate constraints) for a class of uncertain nonlinear systems. In this article, the uncertain states and input delays are handled by integrating a constructed auxiliary system that functions as an observer with neural networks (NNs), with which the adverse effects caused by the uncertain states and input delays can be approximated and compensated. By involving smooth hyperbolic tangent functions in the designed auxiliary system, the problem of magnitude and rate constraints of the control input is fully addressed. Then, the backstepping technique runs through the entire control designing process, which allows the designed adaptive neural control strategy to handle the input constraints and delays at the same time. Furthermore, Nussbaum functions are employed to resolve the problem of unknown control directions. Due to the introduction of an input-driven filter, only the output of the system is required to be measured as the control feedback, which promotes the applicability of the designed controller. Under the proposed control scheme, semiglobal, uniform, and ultimate boundedness of all signals of the closed-loop system is realized with uncertain control directions, input and state delays, and guaranteed magnitude and rate constraints of control inputs. Finally, simulation results are illustrated to verify the effectiveness of the presented control method.

This paper presents a novel method for learning and tracking of the desired path of the human partner in physical human-robot interaction. Combining the Adam optimization algorithm with iteration learning control (ILC), a path learning method is designed to generate and update reference waypoints according to the human partner’s desired path. This method firstly uses the Adam optimization algorithm to update the robot’s reference waypoints in an online manner. Then, an ILC is developed to further modify the waypoints and reduce the difference between the robot’s actual path and the human partner’s desired path in an iterative manner. Simulations and experiments on a 7-DOF Sawyer robot are carried out to show the effectiveness of our proposed method.

In this paper, the modeling and control problem of an overhead crane equipped with a three-dimensional (3-D) variable length flexible cable is discussed. In order to achieve high control performance, a partial differential equation (PDE) model is deduced with exactly preserving high frequency modes of the original system. For the sake of dealing with the effect of input amplitude and rate constraints, a boundary control scheme is carried out to drive the payload to a desired position with the 3-D vibration reduction of the variable length cable by applying the backstepping technology. The exponential stability of the closed-loop system is demonstrated based on the Lyapunov's direct method. The simulation results verify the effectiveness of the proposed control strategy.