In this paper, the robust formation control problem is investigated for a group of quadrotors. Each quadrotor dynamics exhibits the features of underactuation, high nonlinearities and couplings, and disturbances in both the translational and rotational motions. A distributed robust controller is developed, which consists of a position controller to govern the translational motion for the desired formation and an attitude controller to control the rotational motion of each quadrotor. Theoretical analysis and simulation studies of a formation of multiple uncertain quadrotors are presented to validate the effectiveness of the proposed formation control scheme.

Robust Formation Control for Multiple Quadrotors With Nonlinearities and Disturbances

Boosting whale optimization with evolution strategy and Gaussian random walks: an image segmentation method

Virtual target guidance-based distributed model predictive control for formation control of multiple UAVs

Nature-inspired approach: An enhanced whale optimization algorithm for global optimization

Whale optimization algorithm was developed based on the prey-catching characteristics of the humpback whales. Due to its simple structure and efficiency, the researchers employed the algorithm to address numerous disciplines’ numerous problems. The profound analysis of the whale optimization algorithm discloses that the algorithm suffers from low exploration ability, lesser accuracy, and early convergence. Additionally, performance of the whale optimization algorithm and most of its variants in high-dimensional optimization problems is not satisfactory. This study proposes a new variant with several modifications to the basic whale optimization algorithm to solve high-dimensional problems. A unique selection parameter is introduced in the whale optimization algorithm to balance the algorithm’s global and local search phase. The co-efficient vectors A and C are modified and used effectively to explore and exploit the search region better. In the exploration phase, random movement is allowed to reduce the computational burden of the algorithm. An inertia weight is introduced in the exploitation phase for exhaustive search nearby the best solution. The proposed algorithm evaluates twenty-five benchmark functions using dimensions 100, 500, 1000, and 2000 and compared the results with the whale optimization algorithm and its variants. The estimated outcomes are also compared with seven basic metaheuristic algorithms. Finally, statistical analysis, complexity analysis, and convergence analysis are performed to establish the algorithm’s efficacy. All the test result suggests better performance of the proposed algorithm on higher-dimensional problems.

An enhanced whale optimization algorithm for large scale optimization problems

An improved whale optimization algorithm with armed force program and strategic adjustment

Finite-time formation control for multiple flight vehicles with accurate linearization model

According to the characteristics of multiple missiles cooperative guidance, a time-cooperative guidance architecture based on leader-follower strategy is proposed This architecture is composed of individual guidance for each missile and coordinating strategy of the whole system The central process unit situated at the leader coordinates the system, and the cooperative information is broadcasted from leader to followers The coordinate information includes expected impact time and relative motion information of leader and target The time-cooperative guidance architecture has the characteristics of brief structure, short coordinate time and little information to hand on For the features of structure and the follower have no seeker, a specific and feasible algorithm of line of sight rate is presented, the requirement of follower computation and detection equipment is decreased Simulation results show the proposed architecture can realize coordinated attack effectively

Multiple missiles cooperative guidance based on leader-follower strategy

Multi-constraints adaptive finite-time integrated guidance and control design

The ability of a moving-mass control system to control the attitudes of a spinning vehicle is investigated. For the self-learning and adaptive abilities of neural networks, the hybrid attitude control scheme is produced to improve the dynamic performances of the complicated nonlinear system. An analysis of the torque disturbances caused by the relative movements of the masses to the vehicle's body reveals that the total movements of the masses are inclined to be minimized to mitigate these coupling effects. And based on the optimal theory, the mass position algorithm, which determines the mass location to realize the offset of the center of mass of system, is designed. A nonlinear eight degree-of-freedom simulation of a spinning vehicle with two internal moving elements demonstrates the abilities of the hybrid attitude control scheme and the mass position algorithm to effectively control the attitudes of spinning vehicles.

Songyan Wang

Papers

An improved whale optimization algorithm with armed force program and strategic adjustment

Finite-time formation control for multiple flight vehicles with accurate linearization model

Multiple missiles cooperative guidance based on leader-follower strategy

Multi-constraints adaptive finite-time integrated guidance and control design

Moving-Mass Trim Control System Design for Spinning Vehicles