This paper presents a study on controlling the out-of-water motion of amphibious multi-rotor UAVs using a cascade control method based on the Active Disturbance Rejection Control (ADRC) algorithm. The aim is to overcome the challenges of time-varying model parameters and complex external disturbances. The research involves developing an underwater dynamic model and analyzing hydrodynamic forces to calculate theoretical inertial hydrodynamic forces and simulate viscous hydrodynamic forces. This establishes the relationship between viscous hydrodynamic forces and exit velocity. A complete air dynamic model is then established, selecting model parameters based on the center of mass position of the amphibious vehicle to enable switching from water to air. To address control algorithm instability caused by changes in model parameters, position and attitude controllers are built using the ADRC algorithm. The control effects are compared with traditional PID and sliding mode controllers (SMC) to verify the effectiveness and superiority of the proposed cascade ADRC control strategy. Experimental results show that our controller has stronger anti-interference than traditional PID and SMC controllers and can overcome control instability caused by changes in model parameters. Our research highlights the importance of using ADRC-based controllers for amphibious multi-rotor UAVs to achieve robust and stable control.
When a quadruped robot is climbing stairs, due to unexpected factors, such as the size of the differing from the international standard or the stairs being wet and slippery, it may suddenly fall down. Therefore, solving the self-recovery problem of the quadruped robot after falling is of great significance in practical engineering. This is inspired by the self-recovery of crustaceans when they fall; the swinging of their legs will produce a resonance effect of a specific body shape, and then the shell will swing under the action of external force, and self-recovery will be achieved by moving the center of gravity. Based on the bionic mechanism, the kinematics model of a one-leg swing and the self-recovery motion model of a falling quadruped robot are established in this paper. According to the established mathematical model, the algorithm training environment is constructed, and a control strategy based on the reinforcement learning algorithm is proposed as a controller to be applied to the fall self-recovery of quadruped robots. The simulation results show that the quadruped robot only takes 2.25 s to achieve self-recovery through DDPG on flat terrain. In addition, we compare the proposed algorithm with PID and LQR algorithms, and the simulation experiments verify the superiority of the proposed algorithm.
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