Deep reinforcement learning-driven multi-constraint fractional order sliding mode guidance law

A deep reinforcement learning-driven multi-constraint fractional order sliding mode guidance law is proposed to address the challenges of multi-constraint such as attack angle and field of view in the end guidance of hypersonic interception missiles. Dual delay depth deterministic strategy gradient algorithm is improved, multi-head attention mechanism is introduced as the feature extraction layer, multi-scale feature fusion of environmental states is achieved, long short-term memory network is utilized to enhance temporal feature understanding, and convergence and stability of the algorithm is improved through adaptive exploration and turn-based training optimization framework. Based on the three-dimensional relative kinematics model of the missile target, a fractional order sliding mode basic guidance law framework with attack angle constraints is constructed. By improving the reinforcement learning algorithm, the guidance parameters are dynamically optimized to solve the problem of precise guidance under multi-constraint. Simulation results show that the proposed guidance law can significantly improve the guidance speed and accuracy under various angle constraints. The correctness and effectiveness of the proposed method are verified.