Particle swarm optimization based proportional-derivative parameters for unmanned tilt-rotor flight control and trajectory tracking

This paper presents the dynamic modelling and control technique for a tilt-rotor aerial vehicle operating in bi-rotor mode. This kind of aircraft combines two flight envelopes, making it ideal for scenarios that require hovering, vertical take-off/landing and fixed-wing capabilities. In this work, a detailed mathematical model is derived using Newton–Euler formalism. Based on the obtained model, a new control scheme that incorporates six Proportional-Derivative (PD) controllers is proposed for the attitudes (roll (φ), pitch (θ), yaw (ψ)) and the positions (x, y, z) of the aircraft. Then, intelligent Particle Swarm Optimization (PSO) and conventional Reference Model (RM) techniques are applied for optimal tuning of the controllers\u27 parameters. The stability analysis is developed using the Lyapunov approach and its application to the tilt-rotor system in the case of intelligent and conventional PD controllers. Numerical results of two scenarios prove the efficiency of the controllers tuned using the PSO method. Indeed, its ability to track the desired trajectories is demonstrated through 3D path tracking simulations, even in the presence of wind disturbances. Finally, experimental tests of stabilization and trajectory tracking are carried out on our prototype. These testing showed that our tilt-rotor was stable and suitably follows the imposed trajectories

A novel improved elephant herding optimization for path planning of a mobile robot

Swarm intelligence algorithms have been in recent years one of the most used tools for planning the trajectory of a mobile robot. Researchers are applying those algorithms to find the optimal path, which reduces the time required to perform a task by the mobile robot. In this paper, we propose a new method based on the grey wolf optimizer algorithm (GWO) and the improved elephant herding optimization algorithm (IEHO) for planning the optimal trajectory of a mobile robot. The proposed solution consists of developing an IEHO algorithm by improving the basic EHO algorithm and then hybridizing it with the GWO algorithm to take advantage of the exploration and exploitation capabilities of both algorithms. The comparison of the IEHO-GWO hybrid proposed in this work with the GWO, EHO, and cuckoo-search (CS) algorithms via simulation shows its effectiveness in finding an optimal trajectory by avoiding obstacles around the mobile robot

Particle Swarm Optimization and Cuckoo Search-Based Approaches for Quadrotor Control and Trajectory Tracking

This paper explores the full control of a quadrotor Unmanned Aerial Vehicles (UAVs) by exploiting the nature-inspired algorithms of Particle Swarm Optimization (PSO), Cuckoo Search (CS), and the cooperative Particle Swarm Optimization-Cuckoo Search (PSO-CS). The proposed PSO-CS algorithm combines the ability of social thinking in PSO with the local search capability in CS, which helps to overcome the problem of low convergence speed of CS. First, the quadrotor dynamic modeling is defined using Newton-Euler formalism. Second, PID (Proportional, Integral, and Derivative) controllers are optimized by using the intelligent proposed approaches and the classical method of Reference Model (RM) for quadrotor full control. Finally, simulation results prove that PSO and PSO-CS are more efficient in tuning of optimal parameters for the quadrotor control. Indeed, the ability of PSO and PSO-CS to track the imposed trajectories is well seen from 3D path tracking simulations and even in presence of wind disturbances