Hanlei Wang

In this paper, the distributed time-varying optimization problem is investigated for networked Lagrangian systems with parametric uncertainties. Due to the usage of the signum function in the control torque design, there might exist chattering while implementing the distributed time-varying optimization algorithms for networked Lagrangian agents in the existing works. To this end, we design a distributed optimization algorithm that is capable of generating continuous control torques and achieving exact optimum tracking. A simulation is presented to validate the effectiveness of the proposed algorithm.

This paper investigates adaptive control of nonlinear robot manipulators with parametric uncertainty. Motivated by generating closed-loop robot dynamics with enhanced transmission capability of a reference torque and with connection to linear dynamics, we develop a new adaptive approach by exploiting forwardstepping design and inertia invariance, yielding differential-cascaded closed-loop dynamics. With this approach, we propose a new class of adaptive controllers for nonlinear robot manipulators. Our particular study concerning adaptive control of robots exhibits a design methodology towards establishing the connection between adaptive control of highly nonlinear uncertain systems (e.g., with a variable inertia matrix) and linear dynamics (typically with the same or increased order), which is a long-standing intractable issue in the literature.

This brief investigates the image-space regulation problem for visually servoed robotic systems by considering the uncertainties in the camera model and the manipulator kinematics and dynamics. By exploiting the depth-related passivity of the overall system kinematics, we first propose an adaptive dynamic controller that avoids the image-space velocity measurement by introducing a filter. In addition, we further show how to utilize the passivity property to design an adaptive kinematic control scheme, which simplifies the controller structure of the visually servoed robotic systems with the assumption that the low-level control loop can guarantee the square-integrability and boundedness of the joint velocity tracking error. We prove that both the proposed adaptive controllers can ensure the asymptotic convergence of the image-space position error. The results from a numerical simulation and extensive experimental studies illustrate the performance of the proposed adaptive image-space regulation control schemes.

Modern applications of robotics typically involve a robot control system with an inner proportional-integral (PI) or proportional-integral-derivative (PID) control loop and an outer user-specified control loop for specifying the velocity (or position) command. Most existing outer loop controllers, however, do not take into consideration the dynamic effects of robots and their effectiveness relies on the ad hoc assumption that the inner PI or PID control loop is fast enough, and other torque-based controllers cannot be implemented in robots with closed architecture. This paper investigates the adaptive control of robotic systems with an inner/outer loop structure, taking into account the effects of the dynamics and uncertainties, and both the task-space control and joint-space control are considered. We propose a dynamic modularity approach to resolve this issue, and a class of adaptive outer loop control schemes is proposed and their role is to dynamically generate the joint velocity (or position) command for the low-level joint servoing loop. Without relying on the ad hoc assumption that the joint servoing loop is fast enough or modification of the low-level joint controller structure, we show that the proposed outer loop controllers can ensure stability and convergence of the closed-loop system. The formulated dynamic modularity approach is validated by the experimental results using different generations of the UR10 robots.

This paper investigates manipulability of interactive Lagrangian systems with parametric uncertainty and communication/sensing constraints. Two standard examples are teleoperation with a master-slave system and teaching operation of robots. We here systematically formulate the concept of infinite manipulability for general dynamical systems, and investigate how such a unified motivation yields a design paradigm towards guaranteeing the infinite manipulability of interactive dynamical systems and in particular facilitates the design and analysis of nonlinear adaptive controllers for interactive Lagrangian systems. Specifically, based on a new class of dynamic feedback, we propose adaptive controllers that achieve both the infinite manipulability of the controlled Lagrangian systems and the robustness with respect to the communication/sensing constraints, mainly owing to the resultant dynamic-cascade framework. The proposed paradigm yields the desirable balance between network coupling requirements and controlled dynamics of human-system interaction. We also show that a special case of our main result resolves the longstanding nonlinear bilateral teleoperation problem with arbitrary unknown time-varying delay. Simulation results show the performance of the interactive robotic systems under the proposed adaptive controllers.

Separation of the kinematic and dynamic loops is important for industrial/commercial robotic applications (i.e., for designing kinematic control schemes) and also for simplifying the controller structure, but most visual servoing algorithms in the literature, due to the lack of such separation, are hard to be justified as applied to most industrial/commercial robotic systems with a hidden inner control loop. In this brief, we investigate how passivity and nonlinear feedback are used to realize the objective of separation for visual tracking of robotic systems with parametric uncertainty and with time-varying depth, in the case of no image-space velocity measurement. We propose two new passive image-space observers that rely on the joint reference velocity rather than joint velocity, and based on these observers, we develop two adaptive controllers that do not require the image-space velocity measurement and more importantly achieve the separation of the kinematic and dynamic loops. The loop separation is achieved by resorting to the adaptive inverse-Jacobian-like control and nonlinear feedback in the controller and observer, yielding two adaptive kinematic schemes applicable to robots with a closed architecture yet admitting the design of the joint velocity (or position) command. In addition, the proposed second adaptive controller does not require inversion of the estimated depth at the expense of using a potentially-high-gain feedback. The performance of the proposed controllers is shown by numerical simulations and the implementation issues concerning the application to industrial/commercial robots are also discussed.

In this paper, we investigate the task-space consensus problem for multiple robotic systems with both the uncertain kinematics and dynamics and address two main issues, i.e., the separation of the kinematic and dynamic loops in the case of no task-space velocity measurement and the quantification of the manipulability of the system. We propose an observer-based adaptive controller to achieve the manipulable consensus without relying on the measurement of task-space velocities, and also formalize the concept of manipulability to quantify the degree of adjustability of the consensus value. The proposed adaptive controller employs a new distributed observer that does not rely on the joint velocity and a new kinematic parameter adaptation law with a distributed adaptive kinematic regressor matrix that is driven by both the observation and consensus errors. In addition, it is shown that the proposed controller has the separation property, which yields an adaptive kinematic controller that is applicable to most industrial/commercial robots. The performance of the proposed observer-based adaptive schemes is shown by numerical simulations.

This paper investigates the consensus problem of multiple uncertain Lagrangian systems. Due to the discontinuity resulted from the switching topology, achieving consensus in the context of uncertain Lagrangian systems is challenging. We propose a new adaptive controller based on dynamic feedback to resolve this problem and additionally propose a new analysis tool for rigorously demonstrating the stability and convergence of the networked systems. The new introduced analysis tool is referred to as uniform integral-L_p stability, which is motivated for addressing integral-input-output properties of linear time-varying systems. It is then shown that the consensus errors between the systems converge to zero so long as the union of the graphs contains a directed spanning tree. It is also shown that the proposed controller enjoys the robustness with respect to constant communication delays. The performance of the proposed adaptive controllers is shown by numerical simulations.

Modern applications of robotics typically involve a robot control system with an inner PI (proportional-integral) or PID (proportional-integral-derivative) control loop and an outer user-specified control loop. The existing outer loop controllers, however, do not take into consideration the dynamic effects of robots and their effectiveness relies on the ad hoc assumption that the inner PI or PID control loop is fast enough, and other torque-based control algorithms cannot be implemented in robotics with closed architecture. This paper investigates the adaptive control of robotic systems with an inner/outer loop structure, taking into full account the effects of the dynamics and the system uncertainties, and both the task-space control and joint-space control are considered. We propose a dynamic modularity approach to resolve this issue, and a class of adaptive outer loop control schemes is proposed and their role is to dynamically generate the joint velocity (or position) command for the low-level joint servoing loop. Without relying on the ad hoc assumption that the joint servoing is fast enough or the modification of the low-level joint controller structure, we rigorously show that the proposed outer loop controllers can ensure the stability and convergence of the closed-loop system. We also propose the outer loop versions of several standard joint-space direct/composite adaptive controllers for rigid or flexible-joint robots, and a promising conclusion may be that most torque-based adaptive controllers for robots can be designed to fit the inner/outer loop structure by using the new definition of the joint velocity (or position) command. Simulation results are provided to show the performance of various adaptive outer loop controllers, using a three-DOF (degree-of-freedom) manipulator, and experiment results using the UR10 robotic system are also presented.