This paper reviews a majority of the nature-inspired algorithms, including heuristic and meta-heuristic bio-inspired and non-bio-inspired algorithms, focusing on their source of inspiration and studying their potential applications in drones. About 350 algorithms have been studied, and a comprehensive classification is introduced based on the sources of inspiration, including bio-based, ecosystem-based, social-based, physics-based, chemistry-based, mathematics-based, music-based, sport-based, and hybrid algorithms. The performance of 21 selected algorithms considering calculation time, max iterations, error, and the cost function is compared by solving ten different benchmark functions from different types. A review of the applications of nature-inspired algorithms in aerospace engineering is provided, which illustrates a general view of optimization problems in drones that are currently used and potential algorithms to solve them.

Unmanned Aerial Vehicles (UAVs) have a variety of applications in our daily life that have attracted the attention of many researchers around the world. There are a variety of innovations in the flight mechanisms that UAVs are applying for flight. There is also a significant interest in the development of new types of drones that can fly autonomously in different locations, such as cities, marine, and space environments, and perform various missions. This paper reviews the different configurations, flight mechanisms, and applications of drones. First of all, UAVs are divided into four main categories, including Horizontal Takeoff and Landing (HTOL), Vertical Takeoff and Landing (VTOL), Hybrid, and Bio-Based drones. Then each category is divided into some sub-categories to have a coherent review. The characteristics, advantages, and drawbacks of each category are discussed elaborately. Moreover, a comprehensive study is carried out on the applications of UAVs and their specifications.

 Abstract

This paper presents a novel concept of a co-axial bi-rotor UAV which is controlled by moving its center of gravity. After deriving a 6DOF nonlinear model using the Euler-Newton method, we introduce a simplified 3DOF model for planar motion. Due to the limited available torque in pitch and roll channels, a linear quadratic regulator controller is designed for planar motion control, to evaluate the performance and maneuverability of the system while respecting the control limits. The simulation results of implementing an LQR controller on the nonlinear model for tracking problem shows that the system has convincing performance, and while control inputs are constrained, controlling the system is possible with no significant restrictions. Even with considering a first-order dynamic for the actuators, the implementation of the LQR controller for tracking an 8-shape trajectory with different velocities shows that the system has high enough maneuverability for a wide range of applications, like Air Shipping and Delivery, Photography and Multimedia, Monitoring (Traffic, Wildlife, Industry, …), Search and Rescue, Weather Forecasting, etc.

This paper presents design and implementation of Model Based Predictive Controller (MPC) for a novel Bi-Rotor Moving Mass Controlled (MMC) Unmanned Aerial Vehicle (UAV). Due to the strict constrained control inputs in this type of UAV, it is necessary to take into account the constrained controller design and un-constrained control methods are not applicable. MPC controller which is designed based on the linear model by considering control constraints, is implemented on the nonlinear model of the UAV’s planar motion and compared with LQR controller, the simulation results show significant performance of this controller in control of the UAV while respecting control constraints.

In this paper, an Orbit Control algorithm is implemented to correct satellite’s position for Low Earth Orbit (LEO) satellites using Artificial Neural Networks, Model-based Predictive Control, and Linear Quadratic Regulator . as a Self Tuning Regulator structure, an Artificial Neural Networks is used to learn the model of the satellite and compute satellite’s Dynamics and Parameters with external disturbances, after extracting a linear and nonlinear online model based on Artificial Neural Networks model, we used both Linear Quadratic Regulator and Model predictive Control controllers to keep and correct the satellite in its orbit and correct the satellite’s position.