This　study　proposes　an　active　surge　control　method　based　on　deep　reinforcement　learning　to　ensure　the　stability　of　compressors　when　adhering　to　the　pressure　rise　command　across　the　wide　operating　range　of　an　aeroengine.　Initially,　the　study　establishes　the　compressor　dynamic　model　with　uncertainties,　disturbances,　and　Close-Coupled　Valve　(CCV)　actuator　delay.　Building　upon　this　foundation,　a　Partially　Observable　Markov　Decision　Process　(POMDP)　is　defined　to　facilitate　active　surge　control.　To　address　the　issue　of　unobservability,　a　nonlinear　state　observer　is　designed　using　a　finite-time　high-order　sliding　mode.　Furthermore,　an　Improved　Soft　Actor-Critic　(ISAC)　algorithm　is　developed,　incorporating　prioritized　experience　replay　and　adaptive　temperature　parameter　techniques,　to　strike　a　balance　between　exploration　and　convergence　during　training.　In　addition,　reasonable　observation　variables,　error-segmented　reward　functions,　and　random　initialization　of　model　parameters　are　employed　to　enhance　the　robustness　and　generalization　capability.　Finally,　to　assess　the　effectiveness　of　the　proposed　method,　numerical　simulations　are　conducted,　and　it　is　compared　with　the　fuzzy　adaptive　backstepping　method　and　Second-Order　Sliding　Mode　Control　(SOSMC)　method.　The　simulation　results　demonstrate　that　the　deep　reinforcement　learning　based　controller　outperforms　other　methods　in　both　tracking　accuracy　and　robustness.　Consequently,　the　proposed　active　surge　controller　can　effectively　ensure　stable　operation　of　compressors　in　the　high-pressure-ratio　and　high-efficiency　region.　©　2024