To solve the trajectory tracking of service robots in autonomous navigation, a novel self-tuning proportional–integral–derivative controller identified by a radial basis function neural network (radial basis function proportional–integral–derivative controller) is presented. The error regarding the lateral distance and directional deviation angle of the service robot is taken as the control deviation in the radial basis function proportional–integral–derivative controller. During the trajectory tracking, the proportional–integral–derivative parameters of the proposed controller can be adaptively adjusted online by using a radial basis function identification network. To keep the tracking effect of the service robot from being influenced by the initial values (i.e. the initial proportional–integral–derivative parameters and their learning rates) of the radial basis function proportional–integral–derivative controller, a chaos small-world algorithm is introduced to optimize them. The simulation results of the trajectory tracking show that the proposed controller can realize online adjustment of proportional–integral–derivative parameters according to actual conditions of service robots and is characterized by strong noise and disturbance suppression capability. The optimization of the radial basis function network controller based on chaos small-world algorithm can further improve the trajectory tracking precision. Additionally, experiments in the indoor environment further support the validity of the proposed radial basis function proportional–integral–derivative controller for trajectory tracking of service robots.

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To solve the real-time path planning of multi-robots in complex environments, a new immune planning algorithm incorporating a specific immune mechanism is presented. In the immune planning algorithm incorporating a specific immune mechanism, a new coding format for an antibody is first defined according to the impact of the obstacle distribution on the obstacle avoidance behaviors of multi-robots. Then, a new robot immune dynamic model for antibody selection is designed in terms of different impacts of obstacles and targets on robot behaviors. Finally, aiming at the local minimum problem in complex environments and inspired by the specific immune mechanism, a series of appropriate avoidance behaviors are selected through the calculation of a specific immune mechanism to help robots walk out of local minima. In addition, to solve deadlock situations, a learning strategy for the antibody concentration is presented. Compared with four related immune planning algorithms—an improved artificial potential field, a rapidly exploring random tree algorithm, a D* algorithm and a A* algorithm—the simulation results in four static environments show that the paths planned by immune planning algorithm incorporating a specific immune mechanism are the shortest and the path smoothness is generally the highest, which shows its strong planning capability in multi-obstacle environments. The simulation result in a dynamic environment with local minima shows that the immune planning algorithm incorporating a specific immune mechanism has strong planning ability in dynamic obstacle avoidance and in escaping from local minima. Additionally, an experiment in a multi-robot environment shows that two robots can not only avoid static obstacles but also avoid dynamic obstacles, which further supports the validity of the proposed immune planning algorithm incorporating a specific immune mechanism for multi-robots in real environments.

Due to the vulnerability and high risk of the ship environment, the Ship Information System (SIS) should provide 24 hours of uninterrupted protection against network attacks. Therefore, the corresponding intrusion detection mechanism is proposed for this situation. Based on the collaborative control structure of SIS, this paper proposes an anomaly detection pattern based on risk data analysis. An intrusion detection method based on the critical state is proposed, and the corresponding analysis algorithm is given. In the Industrial State Modeling Language (ISML), risk data are determined by all relevant data, even in different subsystems. In order to verify the attack recognition effect of the intrusion detection mechanism, this paper takes the course/roll collaborative control task as an example to carry out simulation verification of the effectiveness of the intrusion detection mechanism.