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This study aims to discuss the simultaneous longitudinal and lateral flight control of the octorotor, a rotary wing unmanned aerial vehicle (UAV), for the first time under the effect of morphing and to improve autonomous flight performance.

This study aims to design and control the octorotor flight control system with stochastic optimal tuning under morphnig effect. For this purpose, models of different arm lengths of the octorotor were drawn in the Solidworks program. The morphing was carried out by simultaneously lengthening or shortening the arm lengths of the octorotor. The morphing rate was estimated by using simultaneous perturbation stochastic approximation (SPSA). The stochastic gradient descent algorithm, which is frequently used in machine learning, was used to estimate the changing moments of inertia with the change of arm lengths. The proportional integral derivative (PID) controller has been preferred as an octorotor control algorithm because of its simplicity of structure. The PID gains required to control both longitudinal and lateral flight were also estimated with SPSA.

With SPSA, three longitudinal flight PID gains, three lateral flight PID gains and one morphing ratio were estimated. PID gains remained within the limits set for SPSA, giving satisfactory results. In addition, the cost index created was 93% successful. The gradient descent algorithm used for the moment of inertia estimation achieved the optimum result in 1,570 iterations. However, in the simulations made with the obtained data, longitudinal and lateral flight was successfully carried out.

Octorotor longitudinal and lateral flight control was performed quickly and effectively with the proposed method. In addition, the desired parameters were obtained with the optimization methods used, and the longitudinal and lateral flight of the octorotor was successfully carried out in the desired trajectory.

Path planning is an important part of UAV mission planning. The main purpose of this paper is to overcome the shortcomings of the standard particle swarm optimization (PSO) such as easy to fall into the local optimum, so that the improved PSO applied to the UAV path planning can enable the UAV to plan a better quality path.

Firstly, the adaptation function is formulated by comprehensively considering the performance constraints of the flight target as well as the UAV itself. Secondly, the standard PSO is improved, and the improved particle swarm optimization with multi-strategy fusion (MFIPSO) is proposed. The method introduces class sigmoid inertia weight, adaptively adjusts the learning factors and at the same time incorporates K-means clustering ideas and introduces the Cauchy perturbation factor. Finally, MFIPSO is applied to UAV path planning.

Simulation experiments are conducted in simple and complex scenarios, respectively, and the quality of the path is measured by the fitness value and straight line rate, and the experimental results show that MFIPSO enables the UAV to plan a path with better quality.

Aiming at the standard PSO is prone to problems such as premature convergence, MFIPSO is proposed, which introduces class sigmoid inertia weight and adaptively adjusts the learning factor, balancing the global search ability and local convergence ability of the algorithm. The idea of K-means clustering algorithm is also incorporated to reduce the complexity of the algorithm while maintaining the diversity of particle swarm. In addition, the Cauchy perturbation is used to avoid the algorithm from falling into local optimum. Finally, the adaptability function is formulated by comprehensively considering the performance constraints of the flight target as well as the UAV itself, which improves the accuracy of the evaluation model.