This paper examines the boundary disturbance observer-based control for a vibrating single-link flexible manipulator system possessing external disturbances. Two new boundary anti-disturbance control strategies are presented to eliminate vibration, track disturbance, and determine angle position for the flexible manipulator system. Achieving rigorous analysis with no model reduction, the derived control can ensure the angle positioning and bounded stability in the controlled system. By appropriately designing parameters, the resulting simulation results can demonstrate the control performance.

This article develops an adaptive neural-network (NN) boundary control scheme for a flexible manipulator subject to input constraints, model uncertainties, and external disturbances. First, a radial basis function NN method is utilized to tackle the unknown input saturations, dead zones, and model uncertainties. Then, based on the backstepping approach, two adaptive NN boundary controllers with update laws are employed to stabilize the like-position loop subsystem and like-posture loop subsystem, respectively. With the introduced control laws, the uniform ultimate boundedness of the deflection and angle tracking errors for the flexible manipulator are guaranteed. Finally, the control performance of the developed control technique is examined by a numerical example.

This paper focuses on adaptive robust vibration control for flexible riser systems affected by input nonlinearities and unknown external disturbances. An auxiliary system is constructed and adjusted to develop a boundary adaptive robust control for restraining the vibrational offset and eliminating the effect of input nonlinearities. Besides, an adaptive law of boundary disturbance upper-bound is constructed together with the vibration control strategy to estimate the magnitude of unknown boundary disturbance. Further, the convergence of states and stability of the system are ensured with the developed control scheme. By choosing the proper control parameters, the control performance is verified with the obtained simulation results.

This study provides a framework of deadzone compensation and robust adaptive vibration control of uncertain spatial flexible riser systems. First, the input deadzone nonlinearity is represented in the form of a desired control input with the addition of an extra nonlinear input error. Second, by visualizing this input error and extraneous disturbance as an unknown “disturbance-like” term, a new adaptive robust vibration control strategy with online updating laws is presented for stabilizing the riser's offset and compensating for system uncertainties. Third, the constructed control ensures and achieves the bounded Lyapunov stability in the controlled system. Finally, the derived simulation results demonstrate the control performance with appropriate design parameters.

The aim of this study is to establish a novel boundary anti-disturbance control to dampen the oscillation of a spatially nonlinear flexible string system influenced by unknown disturbances. To realize this, new disturbance rejection controllers are constructed to effectively suppress the external disturbances for the vibration reduction with the help of a newly designed disturbance observer. Adopting Lyapunov direct method, we ensure that the derived control system is uniformly bounded stable. Simulations are also performed to demonstrate the feasibility and validity of the suggested strategy.

This article presents an adaptive control method for dual-arm robot systems to perform bimanual tasks under modeling uncertainties. Different from the traditional symmetric bimanual robot control, we study the dual-arm robot control with relative motions between robotic arms and a grasped object. The robot system is first divided into two subsystems: a settled manipulator system and a tool-used manipulator system. Then, a command filtered control technique is developed for trajectory tracking and contact force control. In addition, to deal with the inevitable dynamic uncertainties, a radial basis function neural network (RBFNN) is employed for the robot, with a novel composite learning law to update the NN weights. The composite learning is mainly based on an integration of the historic data of NN regression such that information of the estimate error can be utilized to improve the convergence. Moreover, a partial persistent excitation condition is employed to ensure estimation convergence. The stability analysis is performed by using the Lyapunov theorem. Numerical simulation results demonstrate the validity of the proposed control and learning algorithm.