The quad tilt rotor (QTR) has complex dynamics characteristics, and the environmental factors have a great influence on it. This increases the difficulty of its control especially in transition mode. To solve this problem, the design of the controller based on active disturbance rejection control (ADRC) with a novel tuning algorithm is proposed in this paper. The aerodynamic models of propeller, wing, vertical tail and fuselage are build respectively by using the idea of component modeling. The pitch channel of the linearized flight dynamic model is provided for the control system. A simple tuning method of active disturbance rejection control for the quad tilt rotor in transition process that achieves high performance and good robustness is presented. The proposed method makes ADRC become easy to tune and more practical. The problem of parameter tuning is turned into a multi-objective optimization problem, and using radial basis function(RBF) neural network with particle swarm optimization algorithm(PSO) and bacterial foraging optimization algorithm(BFO) hybrid algorithm tuning method(NBPO) to fit the tuning rules. We introduce the control input, the overshoot and rise time of the system into the fitness function. The local and global optimal solutions are introduced into the chemotaxis and migration to improve the information exchange ability of BFO. The correctness and effectiveness of the NBPO tuning method were verified by numerical simulation results, compared with the one parameter tuning method(OPM) and PID, the results showed that the NBPO tuning method can greatly improve the control performance of the system.

With the popularity of electric multi-rotor unmanned aerial vehicles (EMRUAV), the problem of low efficiency of the motor propeller system (MPS) becomes serious especially for EMRUAVs which have multiple working states. To solve this problem, an integrated optimization design method for high-efficiency MPS is proposed using an improved parallel particle swarm optimization and differential evolution (PSODE) hybrid algorithm. First, the brushless motor as well as the variable-speed and the adjustable-pitch propellers is modelled. Then considering the characteristics of the motor and the propeller sufficiently, an integrated optimization method for variable-speed and adjustable-pitch MPS is proposed by utilizing the improved PSODE. Specially, the self-organizing feature map theory is added to substitute the traditional “winner-take-all” method in the interactive study of the improved PSODE. Next, the optimization of a small electric-powered tilt quad rotor (TQR) which has four states: hovering, cruising, transition and maximum speed is carried out. At last, the wind-tunnel experiments are implemented to verify the feasibility and effectiveness of the proposed optimization method. The results indicate that the adjustable-pitch MPS is more suitable for EMRUAVs which have multiple working states, and that the improved PSODE has faster convergence speed and better global search ability than other optimization methods.

The tilt quad rotor (TQR) has the problem of manipulation redundancy due to the aerodynamic structure changing in conversion mode. The tilting path is limited by the conversion corridor. In order to solve the problem of manipulation redundancy in conversion mode and find out the optimal tilting path in conversion corridor, the aerodynamic model of the TQR based on Goldstein vortex theory is established to obtain the manipulation derivative matrix and conversion corridor. A novel manipulation strategy is proposed, the altitude, forward velocity and tilt angle are introduced into the manipulation strategy to ensure the stability of the altitude and attitude in conversion process. To find out the optimal tilting path in conversion corridor, a novel tilting strategy is proposed based on Ant Colony Optimization (ACO) algorithm and compared with another three tilting path. To verify the credibility of the flight dynamics model, the effectiveness of manipulation strategy and tilting path optimization, the simulation and flight test were carried out. The simulation and flight test results show that the manipulation strategy proposed in this paper can solve the manipulation redundancy in conversion mode very well, and the proposed tilting path can ensure the stability of the altitude and attitude in conversion corridor.

Unmanned helicopters are widely used in military and civil fields. One of the most important applications is flying with an underslung load, but the pendulum-like behavior of the load can cause damage or even forced landing to the helicopter. To solve this problem, a control strategy to stabilize the helicopter/load system based on active disturbance rejection control (ADRC) and improved sliding mode control (ISMC) algorithms is proposed in this paper. First, the helicopter/load system is modelled using Newton-Euler equations according to the multi-body dynamics theory. Then a manipulation strategy which can reduce the swing angle of the load and an overall control strategy for the helicopter/load system are presented. Specifically, ADRC is applied to attitude control due to its ability to regard the pendulum-like behavior as the internal uncertainties of the system, meanwhile ISMC to position control. Within ISMC, two sliding surfaces with adjustable weights are constructed by employing the position of the helicopter as well as the swing angle of the load. In addition, a real-time beetle antennae search algorithm is designed to online modify the weights by taking the minimum error at current time as the optimization objective. Besides, the radial basis function neural network is introduced to approach the uncertainty coefficients considering the system’s complexity. At last, relevant simulations are carried out and the results indicate that the system is capable of not only controlling the attitude and position of the helicopter precisely but also stabilizing the underslung load rapidly with ADRC and ISMC.

An improved multi-objective design optimization framework is proposed for the efficient design of proprotor blades tailored to specific high-altitude mission requirements. This framework builds upon existing methods by leveraging a reformulated Vortex Particle Method (rVPM) and incorporates three key stages: (1) rapid determination of overall proprotor parameters using a semi-empirical model, (2) optimized blade chord and twist distribution bounds based on minimum energy loss theory, and (3) global optimization with a high-fidelity rVPM-based aerodynamic solver coupled with a multi-objective hybrid optimization algorithm. Applied to a small high-altitude tiltrotor, the framework produced Pareto-optimal proprotor designs with a figure of merit of 0.814 and cruise efficiency of 0.896, exceeding mission targets by over 15%. Key findings indicate that large taper ratios and low twist improve hover performance, while elliptical blade planforms with high twist enhance cruise efficiency, and a tip anhedral further boosts overall performance. This framework streamlines the industrial customization of proprotor blades, significantly reducing the design space for advanced optimization while improving performance in demanding high-altitude environments.