Intelligent group formation movement control method based on fuzzy logic

Abstract

The invention discloses an intelligent group formation movement control method based on fuzzy logic, and solves the technical problems of group movement oscillation, poorer stability and low control efficiency of multi-agent formation. The overall scheme is that the method comprises the steps of generating and gathering intelligent groups; constructing the formation of the intelligent group; carrying out orthogonal decomposition on the stress of the intelligent agent; introducing a dissipation force to optimize the moving efficiency; introducing a retaining force to optimize the stability; and setting a fuzzy controller to perform intelligent group formation movement control to realize group following movement control. The dissipation force and the retention force are introduced to optimize the potential field force, so that the invalid motion is reduced, and the efficiency and the stability are improved; control parameters of the fuzzy controller are adjusted, so the output is better matched with the actual environment, and the adaptability of the intelligent agent is improved; each agent can independently process data and information, and damage and departure of nodes in a group do not affect group movement; the method is suitable for multi-agent formation movement control.

Description

technical field [0001] The invention belongs to the technical field of intelligent control and artificial intelligence, and mainly relates to the formation movement control of multi-agents, specifically a fuzzy logic-based intelligent group formation movement control method, which is used for attack and defense coordination, patrol search, group movement and group control, unknown Environmental exploration and military operations, etc. Background technique [0002] Intelligent control is an interdisciplinary subject based on control theory, computer science, artificial intelligence, and operations research. Intelligent control expands related theories and technologies, among which fuzzy logic, neural network, expert system, genetic algorithm and other theories are widely used, as well as technologies such as adaptive control, self-organizing control and self-learning control. Intelligent control is mainly used to solve the control problems of complex systems that are diffic...

AI Technical Summary

Problems solved by technology

[0009] Although the existing intelligent agent formation movement control technology has made considerable progress, the autonomy, fault tolerance, flexibility, scalability and collaboration capabilities of the agent have not been fully utilized and effectively developed, and its control is difficult to adapt to the changing environment. Environmental scenarios, it is also difficult to promote and expand other control functions of swarm agents

The limitations of its control technology make the control effect difficult to be good and stable

The current group mobility control mainly has the following problems: 1. Poor autonomy: Because the control method is not fully distributed, individual functions are restricted, and they cannot autonomously adapt to the surrounding environment to adjust and change the state, requiring unified scheduling; 2. Poor fault tolerance: Due to individual differences, the failure of individuals will affect the overall control effect, or make the control invalid; 3. Poor flexibility and scalability: due to incomplete distributed control, changes in the environment or new individuals join, will cause early-stage The control function is completely invalid and needs to be re-deployed; 4. Poor collaboration ability: due to incomplete distribution, individual autonomy is restricted, and its ability to cooperate with each other is difficult to carry out effectively; 5. Poor adaptability: due to its limited adaptability , with the increase of the number of individuals in the group, the force of each agent fluctuates greatly, it is difficult to make flexible and effective adjustments, and then the problem of group movement oscillation occurs, resulting in low movement efficiency; 6. The optimization measures are not perfect: During the formation movement, due to the change of the potential field force, the stability of the formation is poor, and the original method has not been effectively optimized; 7. Poor self-learning ability: because the current intelligent control does not use fuzzy logic control to control parameters Self-learning and sharing measures within the experience group, so in practical applications, the applicability and robustness of the agent in unknown environments needs to be improved

[0010] The above defects in the prior art limit the improvement of the mobile performance of the intelligent group formation, the flexibility and adaptability of the control are restricted, the control efficiency is not high, the control stability is easily affected, the control scalability is not good, and the control effect is not good. Robust, the sharing and collaboration capabilities of the intelligent group have not been effectively utilized and utilized

This leads to an increase in energy consumption of the group, a decrease in the life cycle of the agent, a poor control effect, and an increase in control costs

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