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The differential evolution algorithm (DEA)-based iterative sliding mode control (ISMC) method was proposed for the path tracking problem of three-degree-of-freedom (3-DoF) underactuated ships under external interference, with the nonlinear separate model proposed by mathematical model group (MMG). To improve control quality and enhance robustness of the control system, a swarm intelligence optimization algorithm is used to design a controller parameter optimization system. The DEA was adopted in the system to solve the minimum system evaluation index function, and the optimal controller parameters are acquired. Considering the impact of chattering on the actual project, a chattering measurement function is defined in the controller design and used as an input of the controller parameter optimization system. Finally, the 5446TEU container ship is carried out for simulation. It is verified that the designed controller with strong robustness can effectively deal with the disturbances; meanwhile, the chattering of the output is significantly reduced, and the control rudder angle signal conforms to the actual operation requirements of the ship and is more in line with the engineering reality.

Most density-based clustering algorithms have the problems of difficult parameter setting, high time complexity, poor noise recognition, and weak clustering for datasets with uneven density. To solve these problems, this paper proposes FOP-OPTICS algorithm (Finding of the Ordering Peaks Based on OPTICS), which is a substantial improvement of OPTICS (Ordering Points To Identify the Clustering Structure). The proposed algorithm finds the demarcation point (DP) from the Augmented Cluster-Ordering generated by OPTICS and uses the reachability-distance of DP as the radius of neighborhood eps of its corresponding cluster. It overcomes the weakness of most algorithms in clustering datasets with uneven densities. By computing the distance of the k-nearest neighbor of each point, it reduces the time complexity of OPTICS; by calculating density-mutation points within the clusters, it can efficiently recognize noise. The experimental results show that FOP-OPTICS has the lowest time complexity, and outperforms other algorithms in parameter setting and noise recognition.

In this paper, a type of tracking controller on the basis of parameters optimization was proposed for unmanned surface vehicles (USVs). Taking into account the unique nonlinear and large inertia characteristics of USVs, an iterative sliding mode control (ISMC) was adopted to construct the controller including the USVs’ main engine speed controller to determine the longitudinal velocity and the steering controller to control the lateral displacement. In designing, the hyperbolic tangent function with the saturation characteristic is introduced to design the output feedback control law of nonlinear iterative sliding mode. Then, the differential evolution algorithm (DEA) is applied to construct the parameters optimization system for acquiring the optimal parameters of the proposed controller, and the control quality with adaptive ability and robustness of the optimized controller is achieved. It is verified by computer experiments that the optimized controller realizes the tracking control for USV under interference; meanwhile, compared with the iterative sliding mode controller, the control performance of the controller is better and the robustness of that is stronger.