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In this paper, a learning-based feedforward term is developed to solve a general control problem in the presence of unknown nonlinear dynamics with a known period. Since the learning-based feedforward term is generated from a straightforward Lyapunov-like stability analysis, the control designer can utilize other Lyapunov-based design techniques to develop hybrid control schemes that utilize learning-based feedforward terms to compensate for periodic dynamics and other Lyapunov-based approaches (e.g., adaptive-based feedforward terms) to compensate for nonperiodic dynamics. To illustrate this point, a hybrid adaptive/learning control scheme is utilized to achieve global asymptotic link position tracking for a robot manipulator. Index Terms--Adaptive control, learning control, repetitive update law, robot manipulator.

This paper presents the design of a variable structure-like tracking controller for a mobile robot system. The controller provides robustness with regard to bounded disturbances in the kinematic model. Through the use of a dynamic oscillator and a Lyapunov-based stability analysis, we demonstrate that the position and orientation tracking errors exponentially converge to a neighborhood about zero that can be made arbitrarily small (i.e., the controller ensures that the tracking error is globally uniformly ultimately bounded (GUUB)). In addition, we illustrate how the proposed tracking controller can also be utilized to achieve GUUB regulation to an arbitrary desired setpoint. An extension is also provided that illustrates how a smooth, time-varying control law can be utilized to achieve setpoint regulation despite parametric uncertainty in the kinematic model. Simulation results are presented to demonstrate the performance of the proposed controllers. [S0022-0434(00)00504-9]

Index Terms--Constrained robots, motion/force control, sliding-mode.

Two important properties of industrial tasks performed by robot manipulators, namely, periodicity (i.e., repetitive nature) of the task and the need for the task to be performed by the end-effector, motivated this work. Not being able to utilize the robot manipulator dynamics due to uncertainties complicated the control design. In a seemingly novel departure from the existing works in the literature, the tracking problem is formulated in the task space and the control input torque is aimed to decrease the task space tracking error directly without making use of inverse kinematics at the position level. A repetitive learning controller is designed which "learns" the overall uncertainties in the robot manipulator dynamics. The stability of the closed-loop system and asymptotic end-effector tracking of a periodic desired trajectory are guaranteed via Lyapunov based analysis methods. Experiments performed on an in-house developed robot manipulator are presented to illustrate the performance and viability of the proposed controller. [ABSTRACT FROM AUTHOR]

In this paper, the position synchronization problem of robotic manipulators, in master–slave form, under a coordinated control scheme is addressed in both joint and task/operation space. The manipulators are considered fully actuated, with all system states measurable. The desired trajectory is only accessible to the master robot and information flow is only from master to slaves as opposed to the usual but restrictive assumption of cooperative synchronization scheme. Despite the parametric uncertainty in the system dynamics the proposed adaptive controllers yield asymptotic synchronization of the network in both joint and task space. Lyapunov-based arguments are applied in the stability proofs and controller design. Simulation studies performed on a two-robot manipulator network are presented to demonstrate the viability and performance of the proposed control methods. [ABSTRACT FROM PUBLISHER]