Role-playing language models (RPLMs) have made significant strides in mimicking static character identities.However, their personality simulations remain superficial, lacking a profound understanding of complex human psychological mechanisms.We identify a critical bottleneck termed "Personality Inertia"-a behavioral rigidity where RLHF-induced alignment bias traps models in a sanitized, "helpful assistant" persona.This inertia prevents models from adapting to diverse social contexts or expressing essential but negative traits under pressure.To bridge this gap, we propose PD-LLM, a situation-aware framework grounded in Trait Activation Theory.PD-LLM introduces Bipolar Latent Decomposition, which decouples personality traits into bidirectional LoRA adapters.These adapters are dynamically modulated by a situation-aware module based on the DIAMONDS taxonomy, allowing for precise behavioral regulation.Empirical results show that while baseline methods fail to synchronize multidimensional traits under pressure, PD-LLM achieves superior performance in both static fidelity and dynamic adaptability.By advancing from prompt engineering to intrinsic parameter control, PD-LLM effectively overcomes personality rigidity, facilitating the creation of vivid and psychologically consistent agents.