Role-playing agents (RPAs) 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.