## 1 Introduction  
Nowadays, manipulators are used to assist humans and work in some specific fields such as assembling, industrial application, medical field, exploration, etc. Its high nonlinearity, coupling dynamic reaction, and uncertainties [1] are challenges for control strategy. In order to deal with these problems, some approaches, such as proportional integral derivative (PID) [2; 3], backstepping control [4; 5], sliding mode control (SMC) [6; 7], adaptive control [8; 9; 10; 11], and intelligent control techniques [12; 13; 14] have been investigated. In these methods, the actuator dynamics are usually excluded to simplify the control design. However, according to [15], they can affect the dynamic characteristic and stability [15; 16], as well as dominate the robot dynamics [17]. So, actuator dynamics should be included in control design [16; 17; 18; 19]. Conventionally, electric actuators (EAs) are utilized as primary torque generators in each joint of the manipulator because of owning properties such as excellent accuracy and controllability but suffer from the possibility of sparking [19]. Besides, this approach is not effective when the manipulator operates in wide force/torque range. Pneumatic actuators (PAs) are another potential candidate to replace the electric actuators because of its characteristics [20]. The main advantages of the PAs can be revealed as quick response, high power-to-weight, and power-to-volume ratios [21]. However,according to [PERSON] et al. air compressibility and the elasticity depending on material structure over the time are the main drawbacks to be considered. In addition, the high nonlinearities in the PAs is because its frame degrades the performance of the system [22]. Electro-hydraulic actuators (EHAs) are considered as a good choice to replace the EAs and PAs and are increasingly used in industries because of their high power to weight ratio and high stiffness [23; 24]. Nevertheless, the EHAs are complicated and still a big challenge because of their characteristics such as nonlinear and uncertainty parameters. To deal with the uncertainties which exist in the actuator dynamic and guarantee the stability of the system, the backstepping sliding mode control which takes advantage of the sliding surface in strong robustness against perturbation [25; 26; 27] and backstepping technique in solving both the problem of stabilization and tracking trajectory of nonlinear systems [4; 5] and [28; 29; 30] is exploited. In practice, the manipulator often works under variant uncertainties or external payload variation, for instance, thus degrading the performance of the robot. To cope with these issues, some techniques have been proposed to improve tracking performance. [PERSON] et al. came up with two-layer fraction order fuzzy logic controller [31; 32; 33] and mixed locally recurrent neural network [33] with the Cuckoo search algorithm (CSA). By combining with optimization technique to determine the optimal control parameters with the payload, the results indicated that the tracking performance is improved under the presence of the payload. Although the controller parameters are optimized, the experiments must be implemented in several conditions to get enough characteristic data for the optimal algorithm. Several experiments with the change of the system parameters and working conditions must be carried out. Once the experimental conditions are changed or different from characteristic data, the stability of the system cannot be guaranteed. [PERSON] et al. [34] applied fuzzy neural technique to estimate the payload for compensation. The idea is based on the change in acceleration error due to its change in inertia, thus designing a detect algorithm. However, according to the author, the network was trained off-line. This implies that the experimental data must be sufficient to characterize the system. Fuzzy logic system (FLS) has shown a great performance and considered as a powerful tool to be back up in various system applications [35; 36].