Autonomous underwater vehicles (AUVs) have broad applications owing to their ability to undertake long voyages, strong concealment, high level of intelligence and ability to replace humans in dangerous operations. AUV motion control systems can ensure stable operation in the complex ocean environment and have attracted significant research attention. In this paper, we propose a single-input fractional-order fuzzy logic controller (SIFOFLC) as an AUV motion control system. First, a single-input fuzzy logic controller (SIFLC) was proposed based on the signed distance method, whose control input is the linear combination of the error signal and its derivative. The SIFLC offers a significant reduction in the controller design and calculation process. Then, a SIFOFLC was obtained with the derivative of the error signal extending to a fractional order and offering greater flexibility and adaptability. Finally, to verify the superiority of the proposed control algorithm, comparative numerical simulations in terms of spiral dive motion control were conducted. Meanwhile, the parameters of different controllers were optimized according to the hybrid particle swarm optimization (HPSO) algorithm. The simulation results illustrate the superior stability and transient performance of the proposed control algorithm.

With the increasing stealth performance of underwater detection targets and more complex detection tasks, a single underwater unmanned underwater vehicle (UUV) has been already unable to meet the current mission requirements. Therefore, in recent years, multi-UUV cooperative detection has been widely used in certain task. To address the problem that the change of multiple UUV formations in the detection task will have a large impact on the detection effectiveness, a multi-UUV formation optimization model is proposed. Then, the formation optimization problem of multi-UUV cooperative detection is solved through the ant colony algorithm. The results show that the algorithm in this paper is more robust than the traditional algorithm. and the ability to globally search for optimal solutions.

UUV depth control requires the controlled system to have good transient response and robustness under the premise of ensuring real-time performance. The flexibility of fractional-order control provides an idea to solve this problem. This paper proposes a controller design method for UUV depth control (VD-SIFLC) based on fractional calculus, fuzzy control, dynamic parameters and a fast non-dominated sorting genetic algorithm (NSGA-II). First, the overall structure of the controller, the UUV model and the model of external disturbances are presented. Then, the design methods of control input, order selector, membership function and scale factor selector are given, respectively. Then, the necessary conditions, such as optimization objectives and optimization parameters in the optimization algorithm, are analyzed. Finally, the effectiveness of the proposed control scheme is verified by comparative experiments with the SIFLC. Simulation results show that the controlled system with the VD-FIFLC could achieve better robustness and dynamic and steady-state performance. Moreover, according to the actual task requirements, the appropriate parameters can be selected by the user from the pareto solution set, which is suitable to be used in the actual applications.

Superheated steam temperature affects both safety and economy in thermal power units, and requires good regulation performance. However, due to its long inertia, long delay characteristics, the improper installation position of the sensors and the poor flow characteristics of the regulating valve, some units have poor quality of superheated steam temperature regulation or are difficult to put into automatic operation. This paper introduces the principle, structure and parameter setting method of the new controller NFC based on inertial combination filter (ICF), the principle of closed-loop control structure of temperature-reducing water flow based on NFC, and the practical calculation steps of control object characteristics and NFC parameters of a 630 unit.