Adaptive tracking control of robot manipulators with input saturation and time‐varying output constraints

Abstract

This paper investigates adaptive tracking control in task space for robot manipulators with uncertain system dynamics, input saturation, and time‐varying output constraints simultaneously. An auxiliary system is constructed to compensate the effect of the input saturation, and an asymmetric barrier Lyapunov function (BLF) is applied to tackle time‐varying output constraints, while radial basis function (RBF) neural networks (NN) are used to approximate the unknown closed‐loop dynamics. By introducing a disturbance observer (DO), unknown external disturbances from humans and environment are estimated, and NN approximation errors are compensated. A novel adaptive NN tracking controller is designed to guarantee all signals in the closed‐loop system are semi‐globally uniformly ultimately bounded (UUB), while the tracking errors and observer errors converge to a small neighborhood of zero, and the time‐varying output constraints are not violated. Moreover, the adaptive tracking control of redundant robot manipulators is studied, and the subtask and task space tracking of redundant robot manipulators are realized simultaneously, while the stability of the system is proved by Lyapunov stability theory. Finally, some simulation results are presented to verify the effectiveness and superiority of the proposed control scheme.

UDE-based task space tracking control of uncertain robot manipulator with input saturation and output constraint

“…Let u = U(τ ) − τ and J +T u = J +T U(τ ) − J +T τ , there exists a positive constant δ 2 which satisfies J +T u ≤ δ 2 according to ref. [41].…”

“…Then system (1) can be converted to (5) To further evaluate the performance of the proposed controller, we select two comparative controllers from the literature. The adaptive NN tracking controller [41] is designed as…”

Neuroadaptive Control for Uncertain Euler-Lagrange Systems With Input and Output Constraints

“…As a result, the computational load is greatly reduced. 3) Compared with the works [38]- [45], which can only guarantee the boundedness of the output tracking error of plants, the proposed direct/indirect neuroadaptive control schemes guarantee that the tracking error converge to zero as t → ∞.…”

“…Meanwhile, this method was extended to solve the formation control for waterjet unmanned surface vessels under constraints both in input and output in [44]. The work in [45] paid attention to the control issue of the uncertain robot manipulators (RMs) under input saturation and time-varying output constraints, where ADS and ABLF were used to deal with the constraint issues. Although the works in [38]- [45] had presented many meaningful solutions to the plants described by ELSs, these control solutions can only guarantee that the tracking error of the plants is bounded, i.e., the zero tracking error can not be obtained.…”

“…The work in [45] paid attention to the control issue of the uncertain robot manipulators (RMs) under input saturation and time-varying output constraints, where ADS and ABLF were used to deal with the constraint issues. Although the works in [38]- [45] had presented many meaningful solutions to the plants described by ELSs, these control solutions can only guarantee that the tracking error of the plants is bounded, i.e., the zero tracking error can not be obtained. Further research on the zero tracking error of the uncertain ELSs simultaneously with input and output constraints brings forward the challenge.…”