A Behavioral Adaptive Fuzzy controller of multi robots in a cluster space

Hybrid swarm intelligent algorithm for multi-UAV formation reconfiguration

Formation flight of unmanned aerial vehicles (UAVs) utilizes reconfiguration procedures to handle a variety of emergencies, such as collision avoidance, malfunctions, fuel savings, and member replacement. As UAVs have limited computing power and energy resources, it is necessary to optimize the control inputs to reduce the distance travelled by UAVs while reducing the computing costs during formation reconfiguration. In this paper, the problem of multi-UAV reconfiguration is decoupled into two stages: task assignment and control input optimization of UAVs. For a solution to the above problem, we propose an adaptive hybrid particle swarm optimization and differential evolution algorithm (AHPSODE) to optimize minimize the distance of the total movement and reduce the computing cost of formation reconfiguration. Based on the idea of receding horizon control (RHC) and the nonlinear model of multi-UAV formation reconfiguration, an RHC controller using AHPSODE is designed to optimize the control input of the UAV group to obtain the shortest movement distance, and this method can reduce the computation time. We use the CEC 2017 test suit to test the performance of our proposed AHPSODE algorithm, and simulate the AHPSODE-based RHC controller to manage formation reconfiguration. The results show that our proposed AHPSODE performed well in convergence and accuracy and the RHC controller is effective.

Formation deployment control of multi-agent systems modeled with PDE

In this paper, the formation control problem of PDE-based multi-agent systems (MASs) is discussed. Firstly, the MASs are developed on a one-dimensional chain topology based on the polar coordinate system, and the dynamics of MASs is simulated using the spatial-varying coefficient wave equation. Secondly, a boundary control scheme is proposed by combining PDE-backstepping technique and the Volterra integral transformation. The well-posedness of kernel function is proved by using the iterative and inductive methods. Then, the stability of the closed-loop system is proved by using Lyapunov direct method. Finally, the PDE model is discretized using the finite difference method, and the distributed cooperative control protocol is obtained, in which the followers only need to know the location information of themselves and their neighbors. With this control protocol, leaders drive the MAS to stabilize in the desired formation. Both theoretical analysis and numerical simulation prove that the proposed control scheme is effective.

Publish and Subscribe-Based Formation and Containment Control of Heterogeneous Robotic System with Actuator Time Delay

This paper addresses the problem of controlling a heterogeneous system composed of multiple Unmanned Aerial Vehicles (UAVs) and Autonomous Underwater Vehicles (AUVs) for formation and containment maintenance. The proposed approach considers actuator time delay and, in addition to formation and containment, considers obstacle avoidance, and offers a robust navigation algorithm and uses a reliable middleware for data transmission and exchange. The methodology followed uses both flocking technique and modified L1 adaptive control to ensure the proper navigation and coordination while avoiding obstacles. The data exchange between all the agents is provided through the data distribution services (DDS) middleware, which solves the interoperability issue when dealing with heterogeneous multiagent systems. The modified L1 controller is a local controller for stabilizing the dynamic model of each UAV and AUV, and the flocking approach is used to coordinate the followers around the leader or within the space delimited by their leaders. Potential Field (PF) allows obstacle avoidance during the agents’ movement. The performance of the proposed approach under the considerations mentioned above are verified and demonstrated using simulations.

Multi-Metaheuristic Competitive Model for Optimization of Fuzzy Controllers

This article describes an optimization methodology based on a model of competitiveness between different metaheuristic methods. The main contribution is a strategy to dynamically find the algorithm that obtains the best result based on the competitiveness of methods to solve a specific problem using different performance metrics depending on the problem. The algorithms used in the preliminary tests are: the firefly algorithm (FA), which is inspired by blinking fireflies; wind-driven optimization (WDO), which is inspired by the movement of the wind in the atmosphere, and in which the positions and velocities of the wind packages are updated; and finally, drone squadron optimization (DSO)—the inspiration for this method is new and interesting—based on artifacts, where drones have a command center that sends information to individual drones and updates their software to optimize the objective function. The proposed model helps discover the best method to solve a specific problem, and also reduces the time that it takes to search for methods before finding the one that obtains the most satisfactory results. The main idea is that with this competitiveness approach, methods are tested at the same time until the best one to solve the problem in question is found. As preliminary tests of the model, the optimization of the benchmark mathematical functions and membership functions of a fuzzy controller of an autonomous mobile robot was used.

Inverse Dynamics Based Optimal Fuzzy Controller for a Robot Manipulator via Particle Swarm Optimization

This paper endeavors to contribute to the field of optimal control via presenting an optimal fuzzy Proportional Derivative (PD) controller for a RPP (Revolute-Prismatic-Prismatic) robot manipulator based on particle swarm optimization and inverse dynamics. The Denavit-Hartenberg approach and the Jacobi method for each of the arms of the robot are employed in order to gain the kinematic equations of the manipulator. Furthermore, the Lagrange method is utilized to obtain the dynamic equations of motion. Hence, in order to control the dynamics of the robot manipulator, inverse dynamics and a fuzzy PD controller optimized via particle swarm optimization are used in this research study. The obtained results of the optimal fuzzy PD controller based on the inverse dynamics are compared to the outcomes of the PD controller, and it is illustrated that the optimal fuzzy PD controller shows better controlling performance in comparison with other controllers.