Research On Modeling And Rolling Optimization Methods For Multi-UCAV Dynamic Cooperative Mission Planning

Using multiple UCAVs(Unmanned Combat Aerial Vehicle) fight cooperatively will be an important manner to perform a military mission in the future. Studying on the multi-UCAV cooperative mission planning modeling and optimizing methods is the key to take full advantage of multi-UCAV combating cooperatively, which is of significant theoretical value and great practical value. The problem focus on planning elegant task plan and path plan for each UCAV to make the integrated effects of multi-UCAV system combating cooperatively exceed the total effects of each UCAV combating separately. Properly modeling and solving, online planning for dynamic environment are the keys to solve this problem. Based on the model predict control(MPC) theory, this dissertation studies the problem model, algorithms and online optimizing methods for multi-UCAV cooperative mission planning. The main work and contributions are as follows:(1) A basic mathematic model for multi-UCAV dynamic cooperative mission planning is presented. Following thorough analysis on the characteristics of multi-UCAV performing combat missions, a modeling method based on MPC is proposed. Setting the complex system includes targets, threats and UCAVs as the controlled object, setting the task plan and path plan of UCAVs' as the controlling inputs, a state-predict model reflects system's dynamics and an optimization model reflects the control inputs performance are presented, by extracting the primary states and their transformation of the whole system. By decomposing the basic mathematic model according to different time granularity, the multi-UCAV cooperative mission planning problem is devided into two subproblems for solving the task plan and path plan respectively, which are called task scheduling and path planning. Then with the analysis on the combination of each subproblem, a logic process for solving them is presented. Simulations show that characterizing the dynamics in the process of multi-UCAV completing mission is an important performance factor of planning model. And the model of multi-UCAV dynamic cooperative mission planning problem presented in this paper is beneficial to improve the effectiveness of control. (2) Two different approaches for particle swarm optimization(PSO) algorithm solving discrete combinatorial optimizing problems(DCOP) are proposed. On analyzing the different combinatorial characters of task scheduling and path planning problems, two discrete PSO algorithms are presented. One is continuous space discrete PSO(CDPSO) for DCOP with scheduling, the other is discrete space discrete PSO(DDPSO) for DCOP without scheduling. In CDPSO algorithm, both the integer and decimal fraction of a particle's position are utilized simultaneously through real-number coding, so the discrete problem is mapped into a smaller continuous particle position space. Dynamic sub-swarms strategy and double precision gauss disturbances strategy make particles searching the problem space comprehensively and breaking away from the local extreme. Thus the algorithm performance in local searching is improved while keeping the convergence ability of traditional PSO. The key of the DDPSO is mapping the PSO algorithm into discrete problem space based on the framework of classical PSO algorithm. A totally new algorithm pattern is proposed by redefining the particles' position, velocity and their operation rules. The tests results for two typical DCOP show that those algorithms can both find the best or hypo-best result quickly and steadily with high efficiency.(3) An online task scheduling method based on rolling window and SA-CDPSO (Simulated Annealing CDPSO) algorithm is presented. Since task scheduling online needs to respond realtime, a rolling window optimizing approach is proposed by expanding traditional rolling horizon approach of MPC. The approach can respond very quickly for environment changes by optimizing and rolling online. Rolling rules, rolling window updating and local optimizing problem formatting approaches fit for task scheduling problem are studied. A SA-CDPSO hybrid algorithm is proposed for the task scheduling problem for its discontinuous problem space caused by various constraints of the problem. A result in the global problem space is found using CDPSO algorithm, and then the neighborhood around the result is constructed and searching in the neighborhood is carried out using SA algorithm. This hybrid technique ensures algorithm converging at the best result quickly. A set of test instances which reflects different mathematical properties of the problem are constructed via orthogonal design method for verifying the performance of the SA-CDPSO algorithm and rolling window method. Experiments show that the SA- CDPSO algorithm can solve all of the instances effectively with steady and excellent performance, which proves the rolling window method is adaptive to varieties in environment.(4) An online path planning method based on double precision rolling windows and AI-DDPSO (Artifical Immune DDPSO) algorithm is presented. A double precision rolling windows strategy for online path planning is proposed. Local elaborate path and global cursory path are planned respectively in different window with different information granularity. Two windows roll on different frequency with different mode. An AI-DDPSO hybrid algorithm is proposed for elaborate path planning. An angle err based particle coding method is presented, taking into account the restrictions in UCAV path planning, including minimal straight flying distance, maximal turning angle, climbing-diving angle and minimal adjusting angle. Escape operator and repulsion operator based on artifical immune(AI) theory are proposed, which can keep the diversity of particle swarm by checking the density of particle individuals and swarm to change particles tracks. This method can utilize current information sufficiently and get an optimum path for UCAV quickly.