Fuzzy control heavily relies on a fuzzy rule base constructed from expert experience, while SMC (Sliding Mode Control) has a very high dependency on the model and is prone to high-frequency chattering issues [13;14]. Neural network control (NNC) is particularly influenced by the number of nodes in the neural network; while increasing the nodes can improve control accuracy, it also leads to a significant rise in computational complexity, posing challenges for practical engineering applications [15]. Furthermore, these approaches do not adequately address the constraints related to the ROV's state, potentially compromising tracking precision and risking damage to the thrusters. To overcome the limitations associated with the status of the Remotely Operated Vehicle (ROV), this research employs Linear Active Disturbance Rejection Control (LADRC), an optimal control approach. LADRC preserves the essential characteristics of the proportional-integral-derivative (PID) algorithm without requiring an accurate model of the controlled system [16]. Instead, it treats the unmodeled dynamics and external disturbances as \"total disturbances,\" which are then estimated and compensated for. This methodology offerssignificant advantages in engineering applications due to its ease of use and robust resistance to interference [17; 18]. [PERSON] et al. introduced a trajectory tracking control method for a dual-joint robotic arm system, integrating an extended state observer to estimate both the disturbances and states of the system. Additionally, they applied a state error feedback controller, and experimental findings indicate that the proposed control approach effectively meets control requirements under various conditions, including low-frequency, high-frequency, load, and disturbance scenarios [19; 20]. Despite the successful implementation of LADRC in nonlinear systems, its control effectiveness is limited by its fixed structure and parameters. The adaptive adjustment of parameters for control methods has been a prominent subject of interest, with various optimization algorithms being utilized to improve the robustness and control efficacy of these methods [21; 22]. Different operational contexts necessitate varying control parameters, posing challenges for controllers with fixed optimization parameters to achieve optimal control performance. Drawing inspiration from artificial intelligence technologies, reinforcement learning (RL) algorithms have been amalgamated with control theory to devise novel control strategies that augment the adaptability of control systems and uphold optimal control performance in real time. [PERSON] introduced a Q-learning-based adaptive tuning technique for LADRC parameters [23], which identifies optimal control parameters through iterative updates of the Q-value table and applies it to the heading control of ships. Nevertheless, the Q-learning algorithm mandates manual partitioning of the states of the controlled object and the specification of discrete actions, rendering it arduous to train and learn efficiently as the number of states and specified actions escalates. Furthermore, due to the discrete actions specified, the controller parameters can only assume predetermined values rather than varying continuously, thereby constraining the controller's flexibility. To solve this problem [24], this study employs the Deep Deterministic Policy Gradient (DDPG) RL algorithm to dynamically generate optimal control gains online for the designed LADRC within the Linear Extended State Observer (LESO), thereby determining the optimal parameters of the extended state observer under diverse unknown disturbance conditions. This methodology circumvents the issue of inaccurate disturbance estimation stemming from fixed parameters, and the efficacy of the algorithm is ultimately corroborated through simulations. The main contributions of this article include:  
1. This article presents a nonlinear model for underwater robots that considers parameter uncertainty in the dynamic model.