针对速度不可控条件下高速机动目标拦截制导面临的目标高速机动特性、导弹过载的抖振和饱和等问题，提出了一种三维预设性能角度约束学习制导（Prescribed Performance Control-Based Angle-Constrained Learning Guidance, PPCALG）律，其中预设性能边界内辅助面弹性收敛技术是降抖振和抗饱和的核心。基于弹目相对运动模型，构建了高速机动目标拦截角度约束制导模型和碰撞时刻避免无界扰动辅助面。通过预设性能函数将辅助面进行同胚映射，并针对映射后的系统设计了非线性动态逆角度约束制导律，此时制导增益仅需大于扰动上界即可保证状态变量的瞬态和稳态性能。针对辅助面收敛速度与抖振和饱和的矛盾问题，提出了预设性能边界内辅助面弹性收敛方法，在非线性动态逆角度约束制导律的基础上，设计了预设性能角度约束学习制导律。该方法引入模糊扰动观测器（Fuzzy Disturbance Observer, FDO）降低制导增益，采用（Proximal Policy Optimization, PPO）对预设性能收敛速度参数进行学习，通过制导模型-辅助面-PPC映射-PPO确定PPC收敛速度参数这一体系，在保证辅助面弹性收敛速度的同时不会超出预设性能边界，实现状态变量的性能保证以及降抖振和抗饱和。仿真结果表明，所提预设性能角度约束学习制导律能够基于PPO输出动作和观测器补偿，保证辅助面在预设性能边界内弹性收敛，兼顾状态量瞬态和稳态性能、抗饱和、降抖振和场景适应性。

To address the challenges encountered in high-speed maneuvering target interception guidance under uncontrollable speed, including targets’ high-speed and maneuvers as well as missile overload-induced chattering and saturation, a three-dimensional prescribed performance control angle-constrained learning guidance (PPCALG) law is proposed. The elastic convergence of the auxiliary surface within the prescribed performance bounds constitutes the core mech-anism for chattering reduction and saturation mitigation. Based on the missile–target relative motion model, an angle-constrained guidance framework against high-speed maneuvering targets is established, together with an auxiliary surface design that avoids unbounded disturbances near the collision instant. By introducing prescribed performance functions, the auxiliary surface is diffeomorphically mapped, and a nonlinear dynamic inversion–based angle-constrained guidance law is developed for the transformed system, wherein the guidance gain only needs to exceed the disturbance upper bound to guarantee both transient and steady-state performance of the state variables. To re-solve the inherent conflict between auxiliary surface convergence speed and chattering and saturation, an elastic con-vergence strategy for the auxiliary surface within prescribed performance bounds is further proposed. Building upon the nonlinear dynamic inversion angle-constrained guidance law, a PPCALG law is constructed. Specifically, a fuzzy disturbance observer (FDO) is incorporated to reduce the required guidance gain, while proximal policy optimization (PPO) is employed to learn the convergence rate parameters of the prescribed performance functions. Through the integrated framework of guidance model–auxiliary surface–PPC mapping–PPO-based convergence rate determina-tion, elastic convergence of the auxiliary surface is ensured without violating the prescribed performance bounds, thereby achieving guaranteed state performance along with chattering reduction and saturation avoidance. Simulation results demonstrate that the proposed PPCALG law, leveraging PPO-generated actions and FDO estimates, ensures elastic convergence of the auxiliary surface within the prescribed performance boundaries, while simultaneously achieving favorable transient and steady-state performance, anti-saturation, chattering suppression, and strong sce-nario adaptability.