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As known, the wire and arc additive manufacture technique can achieve stable process control, which is represented with periodic surface waviness, when using empirical methods or feedback control system. But it is usually a tedious work to further reduce it using trial and error method. The purpose of this paper is to unveil the formation mechanism of surface waviness and develop a method to diminish it.

Two forming mechanisms, wetting and spreading and remelting, are unveiled by cross-section observation. A discriminant is established to differentiate which mechanism is valid to dominate the forming process under the given process parameters.

Finally, a theoretical method is developed to optimize surface waviness, even forming a smooth surface by establishing a matching relation between heat input (line energy) and materials input (the ratio of wire feed speed to travel speed).

Formation mechanisms are revealed by observing cross-section morphology. A discriminant is established to differentiate which mechanism is valid to dominate the forming process under the given process parameters. A mathematical model is developed to optimize surface waviness, even forming a smooth surface through establishing a matching relation between heat input (line energy) and materials input (the ratio of wire feed speed to travel speed).

This paper aims to develop a method for tuning the parameters of the active disturbance rejection controller (ADRC) for fixed-wing unmanned aerial vehicles (UAVs). The bald eagle search (BES) algorithm has been improved, and a cost function has been designed to enhance the optimization efficiency of ADRC parameters.

A six-degree-of-freedom nonlinear model for a fixed-wing UAV has been developed, and its attitude controller has been formulated using the active disturbance rejection control method. The parameters of the disturbance rejection controller have been fine-tuned using the collaborative mutual promotion bald eagle search (CMP-BES) algorithm. The pitch and roll controllers for the UAV have been individually optimized to obtain the most effective controller parameters.

Inspired by the salp swarm algorithm (SSA), the interaction among individual eagles has been incorporated into the CMP-BES algorithm, thereby enhancing the algorithm's exploration capability. The efficient and accurate optimization ability of the proposed algorithm has been demonstrated through comparative experiments with genetic algorithm, particle swarm optimization, Harris hawks optimization HHO, BES and modified bald eagle search algorithms. The algorithm's capability to solve complex optimization problems has been further proven by testing on the CEC2017 test function suite. A transitional function for fitness calculation has been introduced to accelerate the ability of the algorithm to find the optimal parameters for the ADRC controller. The tuned ADRC controller has been compared with the classical proportional-integral-derivative (PID) controller, with gust disturbances introduced to the UAV body axis. The results have shown that the tuned ADRC controller has faster response times and stronger disturbance rejection capabilities than the PID controller.

The proposed CMP-BES algorithm, combined with a fitness function composed of transition functions, can be used to optimize the ADRC controller parameters for fixed-wing UAVs more quickly and effectively. The tuned ADRC controller has exhibited excellent robustness and disturbance rejection capabilities.

The CMP-BES algorithm and transitional function have been proposed for the parameter optimization of the active disturbance rejection controller for fixed-wing UAVs.