To solve the problems of complex parameter tuning of traditional active disturbance rejection controller (ADRC) and low adaptive ability of extended state observer, this paper designs a novel intelligent attitude controller based on active disturbance rejection control technology. The adaptive radial basis function neural network can improve the adaptability of the extended state observer, and the fuzzy control can adjust the parameters of the state error nonlinear control law online. The intelligent ADRC controller can eliminate the influence of internal and external disturbances on the unmanned helicopter, and reduce the difficulty of controller parameter tuning. Based on the intelligent ADRC controller, this paper designs an intelligent attitude control system for a small unmanned helicopter and completes the attitude control test. The corresponding test results demonstrate that the intelligent attitude controller has excellent anti-disturbances, robustness and tracking ability, and the control effect is better than that of the traditional ADRC controller.

The internal and external disturbance of the quad tiltrotor UAV stem from its rotor tilting motion and wind disturbance, which affect the control stability of the quad tiltrotor UAV in full flight. This paper proposes a control law design method based on the Adaptive Neural Network Extended State Observer-based Finite-Time Convergent Sliding Mode Control (ANNESO-FTCSMC). The adaptive neural network obtains the unknown total disturbance term that needs to be compensated in the finite-time convergent sliding mode control. An extended state observer is used to estimate the state variables of the controlled plant. The stability of the ANNESO-FTCSMC controller is proved with the Lyapunov stability theory. The unknown total disturbance as the expanded state variable of the state observer has the advantage of high adaptability, and the ANNESO-FTCSMC has a strong ability to compensate for the internal and external disturbances of the system. Compared with traditional sliding mode control, ANNESO-FTCSMC does not contain switching terms, so ANNESO-FTCSMC is chattering-free and converges faster. Based on this method, the flight control system of the quad tiltrotor UAV is designed and verified by hardware-in-loop simulation. The hardware-in-loop simulation results of attitude control in different flight modes and trajectory tracking control in full flight show that the ANNESO-FTCSMC controller is suitable for the flight control of quad tiltrotor UAV. Compared with the PID controller and ADRC controller, ANNESO-FTCSMC controller has more stable flight state of quad tiltrotor UAV under different flight modes and the same total disturbance. The transition process of ANNESO-FTCSMC controller from helicopter mode to airplane mode is also smoother and will not have large fluctuations. Therefore, ANNESO-FTCSMC controller has strong anti-disturbance and robustness.

This article investigates the prescribed-time fault-tolerant formation tracking problem in multiple unmanned helicopters subject to the both gain and bias actuator faults. In contrast to the existing results separating the under-actuated model into an outer-loop and an inner-loop subsystems on the whole, three high-order fully actuated subsystems are derived for design convenience at first. Subsequently, a prescribed-time fault estimation observer is developed to approximate the subsystem states and lumped faults, which can be completed within a user-preassigned time regardless of the initial observation conditions. Such a convergence time can also determine the relevant parameters to some extent. Afterwards, we incorporate the high-order fully actuated system approach into the prescribed-time fault-tolerant formation control protocol design procedures, to eliminate the influence of failures and preserve the target geometric pattern, in which an artificial repulsive potential function is utilized to avoid collisions among the followers on $\mathbf {xoy}$ -plane. In this manner, the prescribed-time property and the higher tracking accuracy can be accomplished. Finally, simulation results are presented to prove the reliabilities and benefits of the algorithm.

The internal uncertainty and external disturbance of the quadrotor will have a significant impact on flight control. Therefore, to improve the control system’s dynamic performance and robustness, the attitude active disturbance rejection controller (ADRC) of the quadrotor is established. Simultaneously, an adaptive genetic algorithm-particle swarm optimization (AGA-PSO) is used to optimize the controller parameters to solve the problem that the controller parameters are difficult to tune. The performance of the proposed ADRC is compared with that of the sliding mode controller (SMC). The simulations revealed that the dynamic performance and robustness of the ADRC is better than that of the SMC.

To solve the problem that the trajectory tracking control system of the unmanned helicopter is limited by the accuracy of the physical model (parameters and dynamic characteristics) and external disturbance of the unmanned helicopter, this paper designs a trajectory tracking control system of the unmanned helicopter based on the linear/nonlinear hybrid Active Disturbance Rejection Control (ADRC). At the same time, this paper proposes a method for tuning the controller parameters based on the Bacterial Foraging Optimization-Flower Pollination Algorithm (BFO-FPA). Finally, the simulation test of spiral climb and “8”-figure climb is applied to verify the controller's performance. The results show that the controller proposed in this paper can effectively overcome the influence of the unmanned helicopter's internal and external disturbance, and the controller has the advantages of strong anti- disturbance ability and strong robustness. Simultaneously, the optimization algorithm can obtain the optimal global solution, and can improve the controller's performance.