## 1 Introduction  
Recent years have witnessed an increasing demand for reliable and economical access to space. Reusable launch vehicles (RLV), as a cost-effective means of undertaking space missions, are attracting more and more attention from researchers [1]. A dynamic model of RLV provides strong non-linear and coupling characteristics due to the complex flight environment of the re-entry phase. External disturbances, uncertain structural and aerodynamic parameters, and input constraints inevitably exist during real flight, having a significant impact on the attitude control system. In this context, attitude control for RLV is a challenging topic and has elicited widespread interest. Various control methodologies, such as adaptive control [2], dynamic inversion control [3], robust control [4], sliding mode control [5, 6], and neural network (NN) control [7, 8], have been applied over the past decades. Nevertheless, there is still scope to develop an optimal control approach for RLV suffering from complicated non-linear dynamics, parametric uncertainties and limited inputs. From a mathematical point of view, the Hamilton-Jacobi-Bellman (HJB) function and its solution are required to be established to solve the optimal control problems. However, it is difficult to derive an analytical solution from the HJB function for non-linear continuous-time systems. Given this, a reinforcement learning (RL) scheme with an actor-critic (AC)structure was initially created by [PERSON] [9], whereby a critic network was exploited to approximate the value function, and an actor network was deployed to obtain the optimal control policy. Informed by [PERSON] contribution, [PERSON] et al. developed an online AC algorithm to solve the continuous-time infinite horizon optimal control problem [10]. [PERSON] et al. proposed a novel online learning and optimization structure by incorporating a reference network into the AC structure [11]. [PERSON] et al. devised a learning-based adaptive sliding mode control scheme for a tethered space robot with limited inputs [12]. Although the above control strategies have provided excellent results in terms of optimal control, the existing problem concerns the need for accurate dynamic modeling [13]. Given the parametric uncertainties and unknown disturbances, it is, in practice, difficult to exactly determine the system dynamics for RLV, limiting the methods' applicability. Therefore, a further problem exists in that the aforementioned methods' robustness must be enhanced for practical systems with unknown dynamics. [PERSON] et al. combined (ISMC) with a reinforcement learning control scheme for non-linear systems with partially unknown dynamics [14]. However, the input constraints were not considered, and the ISMC used may lead to unexpected oscillation [15]. [PERSON] et al. developed a learning-based \(H_{\infty}\) tracking control scheme for which the uncertainties and input constraints were considered [16]. Nevertheless, the control design is conservative, and the iterative algorithm is rather complicated. It is of note that previous RL-based control methods only establish asymptotic or finite-time convergence [17]. Therefore, the upper bound of the convergence time is uncontrollable, and the transient and steady-state performance, namely, the maximum overshoot and the steady accuracy, cannot be quantitatively prearranged by users. As a promising solution to this problem, the prescribed performance control (PPC) method created by [PERSON] et al. has attracted widespread attention [18]. The salient feature of PPC is that users can quantitatively pre-arrange both the transient performance and the steady tracking error. In [19], a novel PPC scheme combined with a command filter was proposed for a quadrotor unmanned aerial vehicle subject to error constraints. In [20], an NN-based adaptive non-affine tracking controller was devised for an air-breathing hypersonic vehicle with guaranteed prescribed performance.