Robust flocking of multiple intelligent agents with multiple disturbances

Abstract

The cooperation control of multiple intelligent agents (MIAs), which can solve complex engineering problems in practice, has received increasing attention. However, there are multiple disturbances in wireless sensor networks, which has a great effect on the collaboration of MIAs. In this paper, the problem of the flocking motion of second-order MIAs is addressed with collision avoidance and multiple disturbances. To estimate the matched/mismatched disturbances, the disturbance observers are designed. It is noted that the assumption that the differentials of disturbances converge to zeros in the literature is removed. A novel compound strategy is designed by using the robust auxiliary function and the potential function. The asymptotic properties of the flocking system are studied based on the Input-to-State Stability Theorem and the robust stability. Numerical simulation results verify the validity of the proposed protocol. K E Y W O R D S collision avoidance, distributed control protocol, multiple disturbances, multiple intelligent agents, robust flocking 1 | INTRODUCTION Recently, flocking behaviors have been studied by scholars from different fields, such as engineering, computer, and biology. Cooperative behaviors of multiple intelligent agents (MIAs) widely exist in the natural world, for example, the schooling of fish, flocking of birds, and swarming of bacteria. These behaviors increase the chances of finding food and avoiding

Obstacle Boundary Point and Expected Velocity-Based Flocking of Multiagents with Obstacle Avoidance

“…In this section, we design an obstacle boundary point and an expected velocity-based focking algorithm of multiagents with obstacle avoidance, which seeks to modify the gradientbased terms in formulas ( 8) and ( 10) and the consensus term in formula (10) to relax the constraints of obstacle shape and boundary in the previous focking algorithm with obstacle avoidance.…”

“…In this example, the simulation is performed on 30 α-agents and a c-agent moving in a two-dimensional Euclidean space with two spherical obstacles and a wall obstacle. Te initial position vectors of 30 α-agents are selected randomly from [10,30] × [5,20], the initial velocity vectors of 30 α-agents are selected randomly from [0, 2] × [0, 2], the initial position vector of c -agent is (75, 80) T , and the initial velocity vector of c-agent is zero. Te parameters of the proposed algorithm and the Olfati-Saber algorithm are shown in Table 3.…”

“…Flocking is an orderly motion phenomenon generated by relatively simple local interaction among multiagents, which is very common in nature, such as the migration of birds [1,2], foraging of fsh schools [3,4], avoiding predators of deers [5], and swimming of bacteria [6,7]. Multiagents often encounter some obstacles in the process of focking motion [8][9][10]. Terefore, obstacle avoidance is a key technology problem to be solved in the research of focking control.…”

Reinforcement learning method for target hunting control of multi‐robot systems with obstacles

“…The development of intelligent applications in the civilian and military fields has led to increasing complexity of tasks, making the problem of multi‐robot collaboration attract great attention 1–10 . Multi‐robot collaboration is based on multi‐robot systems.…”

“…The development of intelligent applications in the civilian and military fields has led to increasing complexity of tasks, making the problem of multi-robot collaboration attract great attention. [1][2][3][4][5][6][7][8][9][10] Multi-robot collaboration is based on multi-robot systems. The inspiration for modern research on multi-robot systems comes from imitating the group behavior of animals.…”