This brief proposes a novel chaotic fractional-order beetle swarm optimization (CFBSO) algorithm that combines chaos concepts and a fractional derivative structure with the beetle swarm algorithm. The proposed CFBSO was compared with other advanced meta-heuristic algorithms on 23 benchmark functions with single-peak, multi-peak, and fixed-dimensional multi-peak optimization problems, and the effectiveness and superiority of the proposed algorithm were verified. The proposed CFBSO was then used to optimize the parameters of the active disturbance rejection controller (ADRC). The CFBSO-based ADRC was further applied for the load frequency control (LFC) system of a four-area interconnected power system consisting of a hydraulic turbine with a non-minimum phase (NMP) and three reheat turbines. The results showed that the proposed method had a smaller undershoot and shorter settling time than those obtained by a linear ADRC and a proportional–integral–derivative controller. Thus, it can meet the high-performance requirements of LFC.

Towing is a widely used mode of transportation in offshore engineering, and towing of unpowered platforms is of particular significance. However, the addition of unpowered facilities has increased the difficulty of ship maneuvering. Moreover, the marine environment is complex and changeable, and sudden winds or waves can have unpredictable effects on the towing process. Therefore, it is of great significance to overcome the influence of the harsh marine environment while navigating the towing system following a planned course to a target sea area. To tackle the time‐varying disturbances, a course control method for a towing system of unpowered cylindrical drilling platform is designed based on double deep Q‐network (DQN) optimized linear active disturbance rejection control (LADRC). To be specific, to tackle the difficulty of LADRC tuning, double DQN is applied to select the best parameters of the LADRC at any time according to the states of the system, without relying on the specific information of the model and the controller. The course control performance of the towing system is evaluated in a simulation environment under various disturbances. Moreover, the Monte Carlo experiment is used to test the robustness of the controller when the ship's mass changes and the robustness of the proposed method is verified by testing with various heading angles. The results show that the LADRC with adaptive parameters optimized by double DQN performs well under external interference and inherent uncertainty, and compared with the traditional LADRC, the proposed method has better course control effects.

To ensure the safe operation of an interconnected power system, it is necessary to maintain the stability of the frequency and the tie-line exchanged power. This is one of the hottest issues in the power system field and is usually called load frequency control. To overcome the influences of load disturbances on multi-source power systems containing thermal power plants, hydropower plants, and gas turbine plants, we design a linear active disturbance rejection control (LADRC) based on the tie-line bias control mode. For LADRC, the parameter selection of the controller directly affects the response performance of the entire system, and it is usually not feasible to manually adjust parameters. Therefore, to obtain the optimal controller parameters, we use the Soft Actor-Critic algorithm in reinforcement learning to obtain the controller parameters in real time, and we design the reward function according to the needs of the power system. We carry out simulation experiments to verify the effectiveness of the proposed method. Compared with the results of other proportional–integral–derivative control techniques using optimization algorithms and LADRC with constant parameters, the proposed method shows significant advantages in terms of overshoot, undershoot, and settling time. In addition, by adding different disturbances to different areas of the multi-source power system, we demonstrate the robustness of the proposed control strategy.