## 1 Introduction  
In recent decades, electro-hydraulic systems (EHSs) have been widely employed in various applications such as construction machinery [1], hydraulic presses [2], robot manipulators [3], aircraft [4], and so forth which require tremendous force/torque, remarkable physical endurance, and high-reliability [5, 6]. Hence, achieving high-accuracy tracking performance as one of the control problems for such EHSs has attracted considerable attention from researchers in both academia and industry [7]. However, it is still a challenging task owing to some disadvantages including high nonlinearity, parameter deviations, modeling errors, unmodeled dynamics, and unknown external loads. In addition, the shortage of state information is also another hindrance to attaining the desired tracking precision since some system states, e.g., velocity and acceleration, may not be directly measured for reducing the system cost or exactly observed due to the influence of measurement noise. It is worth noting that nonlinear control methods, namely feedback linearization control (FLC) [8, 9], sliding mode control (SMC) [10, 11], and backstepping control (BSC) [12, 13, 14] are powerful tools to deal with nonlinearities in system dynamics of EHSs based on their model compensation abilities. Remarkably, among the aforementioned control techniques, the backstepping framework is the most appropriate methodology to construct a controller for high-order nonlinear EHSs by virtue of its recursive procedure. Nonetheless, there are three main impediments that restrict the applicability of backstepping controllers in real applications. Firstly, for high-order nonlinear systems, a backstepping framework-based control strategy cannot achieve the desired tracking qualification in case of abruptly changing reference trajectories. Secondly, the computational complexity is inevitable when adopting the conventional backstepping technique because it is required to analytically calculate the derivative of virtual control at each iteration. Finally, in the absence of disturbances and uncertainties, a backstepping controller is able to achieve asymptotic stability, however, the tracking performance indexes including settling time, overshoot, and steady-state error are not explicitly exhibited. It is worth noting that under the circumstance of a steep reference trajectory, it is relatively difficult for traditional backstepping controllers to achieve a satisfactory transient performance owing to the system kinematic and dynamic constraints. To ensure the feasibility of the tracking trajectories, several algorithms [15, 16, 17, 18, 19] have been introduced to online generate the optimally practicable tracking trajectories, which consider both dynamic and kinematic restraints. Hence, the tracking performance was significantly enhanced as a result. For the second drawback of the backstepping framework, to efficiently reduce the computational complexity of the conventional backstepping due to the repeatedly analytic derivative calculation of virtual control law at each step, dynamic surface control (DSC) technique can be considered as a noticeable tool, which was originally introduced in [20]. The essential principle of this concept is to use a first-order filter for the virtual control at each backstepping iteration, and consequently, the first-order derivatives of virtual control laws are calculable. This control technique has been successfully adopted in previous works [21, 22, 23, 24]. However, due to the lack of filter error compensation mechanisms, only bounded stability is achieved when using the DSC approach. As a solution to this problem, the command filter (CF) approach integrated into the backstepping design was originally developed in [25], and has been utilized in various applications such as hydraulic systems [26], active suspension systems [27], wind turbine hydraulic pitch systems [28, 29], and so on in recent years.