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This paper focuses on the construction of differential-cascaded structures for control of nonlinear robot manipulators subjected to disturbances and unavailability of partial information of the desired trajectory. The proposed differential-cascaded structures rely on infinite differential series to handle the robustness with respect to time-varying disturbances and the partial knowledge of the desired trajectories for nonlinear robot manipulators. The long-standing problem of reliable adaptation in the presence of sustaining disturbances is solved by the proposed forwardstepping control with forwardstepping adaptation, and stacked reference dynamics yielding adaptive differential-cascaded structures have been proposed to facilitate the forwardstepping adaptation to both the uncertainty of robot dynamics and that of the frequencies of disturbances. A distinctive point of the proposed differential-cascaded approach is that the reference dynamics for design and analysis involve high-order quantities, but via degree-reduction implementation of the reference dynamics, the control typically involves only the low-order quantities, thus facilitating its applications to control of most physical systems. Our result relies on neither the explicit estimation of the disturbances or derivative and second derivative of the desired position nor the solutions to linear/nonlinear regulator equations, and the employed essential element is a differential-cascaded structure governing robot dynamics.
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 dynamic
This paper focuses on developing a new paradigm motivated by investigating the consensus problem of networked Lagrangian systems with time-varying delay and switching topologies. We present adaptive controllers with piecewise continuous or arbitrary
In this paper, we investigate the adaptive control problem for robot manipulators with both the uncertain kinematics and dynamics. We propose two adaptive control schemes to realize the objective of task-space trajectory tracking irrespective of the
A structured preconditioned conjugate gradient (PCG) solver is developed for the Newton steps in second-order methods for a class of constrained network optimal control problems. Of specific interest are problems with discrete-time dynamics arising f
This paper presents a control reconfiguration approach to improve the performance of two classes of dynamical systems. Motivated by recent research on re-engineering cyber-physical systems, we propose a three-step control retrofit procedure. First, w