In this work, aiming at the problem of cooperative task assignment for multiple unmanned aerial vehicles (UAVs) in actual combat, battlefield tasks are divided into reconnaissance tasks, strike tasks and evaluation tasks, and a cooperative task-assignment model for multiple UAVs is built. Meanwhile, heterogeneous UAV-load constraints and mission-cost constraints are introduced, the UAVs and their constraints are analyzed and the mathematical model is established. The exploration performance and convergence performance of the harmony search algorithm are analyzed theoretically, and the more general formulas of exploration performance and convergence performance are proved. Based on theoretical analysis, an algorithm called opposition-based learning parameter-adjusting harmony search is proposed. Using the algorithm to test the functions of different properties, the value range of key control parameters of the algorithm is given. Finally, four algorithms are used to simulate and solve the assignment problem, which verifies the effectiveness of the task-assignment model and the excellence of the designed algorithm. Simulation results show that while ensuring proper assignment, the proposed algorithm is very effective for the multi-objective optimization of heterogeneous UAV-cooperation mission planning with multiple constraints.

To solve the task assignment problem of heterogeneous multi-unmanned aerial vehicle (UAV) with different loads, an improved monkey swarm algorithm is proposed. First, the complex combat tasks are divided into three types of subtasks, and the multi-UAV task assignment model is established based on the performance of UAVs with specific loads. Second, an improved chaotic self-adapting monkey algorithm (ICSAMA) is proposed by introducing chaos optimization into the monkey swarm algorithm through the adaptive mechanism. The optimization ability of the improved algorithm is verified by the classical benchmark function containing single/multipeaks. Finally, taking the actual heterogeneous multi-UAV task planning problem as an example, ICSAMA is applied to solve it. The simulation results show that ICSAMA has higher convergence accuracy and robustness than the standard monkey swarm algorithm.

With the new applications of Internet of Things in recent years, radar sensors have become an important design unit in Internet of Things and embedded design. Considering that the dimensions of the radar sensors deployment problem increase with the increasing number of radars deployed and that the strength of the monkey algorithm is that it avoids the “dimension disaster,” this algorithm is introduced to solve the optimization problem. Some improvements are made based on the shortcomings of the traditional monkey algorithm. Adaptive climbing steps are used in the climbing process to enhance its local search capabilities, a tent function is used to balance the search accuracy and search time in the overlooking process and the jumping process, and a semi-group execution strategy is adopted for the above two processes. To improve the search accuracy, the learning factor and the Euclidean distance are introduced into the looping process, which improves the optimization ability and avoids the individual homoplasy. Therefore, the improved chaotic self-adapting monkey algorithm (ICSAMA) is proposed and abbreviated to ICSAMA. The simulation results show that the improved chaotic adaptive monkey algorithm is better than the monkey algorithm regarding the convergence precision and convergence rate. Finally, a mathematical model of radar deployment is established based on the volume of airspace coverage. Three simulation experiments are designed by using different conditions and scenarios, such as air defense, maritime combat, and trajectory planning, and an emphasis is placed on describing the applications of ICSAMA. The results show that ICSAMA can effectively solve the problem of radar deployment and provide technical support for the site selection of new observation and communication posts, deployment of maneuverable radar stations, and track planning of fleets.

In order to solve the problem of maximizing the utilization of resources through reasonable deployment under limited resources, this paper studies from two aspects: one is to establish the mathematical model of maximum coverage of space detection, and the other is to improve the harmony algorithm. The exploration performance and convergence performance of the harmony search algorithm are analyzed theoretically, and the more general formulas of exploration performance and convergence performance are proved. Based on theoretical analysis, the algorithm called opposition-based learning parameter adjusting harmony search is proposed. By using the algorithm to test the functions of different properties, the value range of key control parameters of the algorithm are given. The proposed algorithm is applied to optimize the problem of radar deployment. This paper takes a certain area of the Shandong Peninsula as the deployment scope. The simulation results show that the proposed algorithm is effective and practical. Although there is a large amount of calculation, it provides ideas and ways for other problems, such as the site selection of new observation and communication post, the deployment of maneuvering radar stations, and the track planning of fleet.

A new adaptive control method is designed for a class of pure-feedback nonlinear systems with prespecified tracking accuracy. The system functions of pure-feedback nonlinear systems are allowed to be semi-bounded and continuous. As for the system functions and complicated differential terms, some compact sets are constructed to obtain their bounds on these sets. Therefore, with the help of these bounds, the complicated functions of the system are tackled very well without using approximators, and the “explosion of complexity” inherently in backstepping-based methods are perfectly avoided without using any filters. Furthermore, it is proved that the designed method can guarantee the boundedness of all the closed-loop system signals and the convergence of the tracking error to arbitrarily prespecified small neighborhood of the origin. Finally, a practical simulation example of high-maneuver fighter flight control are given to verify the proposed method.