PID controllers are ubiquitous in industrial control applications due to their simplicity. However, static gain control in PID controllers is restricted in dealing with non-linear dynamics, leading to overshooting, oscillations, or inefficiencies. This paper proposes an active vibration control system that is self-sustaining in nature. The control signals are adapted using the Deep Reinforcement Learning (DRL) algorithm. A Soft Actor Critic algorithm was designed to self-adjust the proportional, integral, and derivative gains every 10 ms based on the changes in the system dynamics. The system was tested in an in-house Open AI-based customized environment that considers the electromechanical properties of the DC motor. The test output reveals that the control system removes overshooting, brings down the settling time by 40%, and optimizes energy efficiency by 17%. Moreover, the adaptive gain process discovers that the control algorithm independently learns the variable-structure control logic. The control logic hardens or softens the system as required. The experiment validates the efficacy of the novel control logic based on the superior power of DRL algorithms as contrasted with the Ziegler-Nichols PID logic in self-healing non-linear industrial dynamics.