## 1 Introduction  
Robotic manipulators have been extensively applied in assisting or even replacing humans to perform the various tasks such as assembling [1], machine operation [2], debarring [3], drilling [4], transportation [5] and manufacturing [6, 7]. In order to successfully perform such industrial tasks, it is essential to well control the motion of robotic manipulators. However, the highly nonlinear and uncertain dynamics of robotic manipulators result in the inapplicability of many traditional controllers designed for linear systems such as linear quadratic control (LQC) [8, 9] and linear Ho control [10, 11]. Hence, many researchers have been motivated to place efforts on designing advanced controllers for robotic manipulators. State feedback linearization requiring a known dynamic model can transform the nonlinear system to a linear form [12]. However, the dynamic model of robotic manipulators is always uncertain because of unknown external disturbances and uncertain system parameters. To handle the issue of uncertainty, many efforts have been made such as sliding mode control (SMC) [13, 14, 15, 16, 17, 18], fuzzy logic system (FLS)-based control [19, 20, 21], neural network (NN)-based control [22, 23, 24, 25, 26], and disturbance observer (DOB)-based control [27, 28, 29]. More precisely, [PERSON] et al. [13] propose a fixed time sliding mode control for uncertain robotic manipulators in which a conservative switching gain requiring the upper bound of lumped uncertainty is used. In [21], an adaptive controller-based T-S (Takagi-Sugeno) fuzzy system is designed, and the modified T-S fuzzy system can efficiently approximate the unknown model of robotic manipulators. [PERSON] et al. [26] present a multiple-layer neural network-based controller that can achieve a high accuracy of motion control of robotic manipulators subject to unknown disturbances. In [28], a high-order-sliding-mode differentiator (HOSMD)-based estimator is designed to compensate the mismatched uncertainties without the need of assuming a bounded uncertainty. Besides the uncertainty of the dynamic model, state constraint is also a common problem of robotic manipulators, which has attracted much attention. For example, [PERSON] et al. [PERSON] _et al._ (2018) proposed an adaptive neural network control scheme considering full-state constraints of robotic manipulators. In [PERSON] _et al._ (2018), an adaptive fuzzy control scheme that can guarantee the constrained output is presented. [PERSON] et al. [PERSON] _et al._ (2018) designed an adaptive fuzzy control scheme working with a disturbance observer for the manipulators with full-state constraints. Nevertheless, those controllers can only achieve either the constrained tracking error of joint angle or the constrained error between the actual angular velocity and the virtual control signal. In other words, controllers in [PERSON] _et al._ (2018); [PERSON] _et al._ (2018); [PERSON] _et al._ (2018) do not guarantee the angular position and angular velocity for each joint of the robotic manipulator, within the preset constraints. Recently, [PERSON] et al. [PERSON] _et al._ (2018) designed a new adaptive control scheme that can guarantee the angular position for each joint of a robotic manipulator to never exceed the preset constraints. However, the constrained angular velocity of each joint is not guaranteed. In the light of reviewing the existing literature, the following issues need to be further solved:  
* In order to safely perform robotic manipulators, both the angular position and angular velocity of each joint of robotic manipulators should be controlled to not exceed the preset constraints. More precisely, the angular position (rotation angle) of each joint should be always within a reasonable range to have no risk on physically breaking the joint.