Abstract
This paper explores the full control of a quadrotor Unmanned Aerial Vehicles (UAVs) byexploiting the nature-inspired algorithms of Particle Swarm Optimization (PSO), Cuckoo Search(CS), and the cooperative Particle Swarm Optimization-Cuckoo Search (PSO-CS). The proposedPSO-CS algorithm combines the ability of social thinking in PSO with the local search capability inCS, which helps to overcome the problem of low convergence speed of CS. First, the quadrotordynamic modeling is defined using Newton-Euler formalism. Second, PID (Proportional, Integral,and Derivative) controllers are optimized by using the intelligent proposed approaches and theclassical method of Reference Model (RM) for quadrotor full control. Finally, simulation resultsprove that PSO and PSO-CS are more efficient in tuning of optimal parameters for the quadrotorcontrol. Indeed, the ability of PSO and PSO-CS to track the imposed trajectories is well seen from3D path tracking simulations and even in presence of wind disturbances.
Highlights
Over the past few years, Unmanned Aerial Vehicles (UAVs) of type quadrotors or quadcopters have seen an increasing interest since their wide range of civilian and military applications
Intelligent and classical control methods of Particle Swarm Optimization (PSO), Cuckoo Search (CS), the cooperative Particle Swarm Optimization-Cuckoo Search (PSO-CS), and the classical Reference Model (RM) are detailed for further reason to be applied in quadrotor control
To evaluate the optimization performance of the quadrotor’s responses using the proposed approaches, the fitness function is defined in a similar way in order to minimize the differences between the desired and the controlled outputs responses of the quadrotor. This fitness function is defined in Equation (22) as the sum of the errors Ek that characterize the difference in behavior between the inputs and the outputs of the quadrotor based on the Integral Square Error (ISE), with l = 6 and k = {1, 2, 3, 4, 5, 6}
Summary
Over the past few years, Unmanned Aerial Vehicles (UAVs) of type quadrotors or quadcopters have seen an increasing interest since their wide range of civilian and military applications. Nature-inspired optimization algorithms can effectively resolve complex problems compared to classical and statistical methods Some of these algorithms proposed in the literature are Genetic. In CS, immigration and environmental specifications have the advantage to help cuckoos’ groups to converge and reach the best places for breeding and egg laying [21] Another advantage of CS compared to PSO and GA is that it uses a smaller number of parameters to be tuned, which makes it more adaptable [21]. The velocities of displacement of particles and nests are calculated in different ways for PSO and CS, which generates differences in the convergence speeds of both algorithms For these reasons, it would be very interesting to analyze the social thinking and local search abilities of PSO and CS in quadrotor control.
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