An unknown nonlinear disturbance seriously affects the trajectory tracking of autonomous underwater vehicles (AUVs). Thus, it is critical to eliminate the influence of such disturbances on AUVs. To address this problem, this paper proposes a double-loop proportional–integral–differential (PID) neural network sliding mode control (DLNNSMC). First, a double-loop PID sliding mode surface is proposed, which has a faster convergence speed than other PID sliding mode surfaces. Second, a nonlinear high-order observer and a neural network are combined to observe and compensate for the nonlinear disturbance of the AUV system. Then, the bounded stability of an AUV closed-loop system is analyzed and demonstrated using the Lyapunov method, and the time-domain method is used to verify that the velocity- and position-tracking errors of AUVs converge to zero exponentially. Finally, the radial basis function (RBF) neural network PID sliding mode control (RBFPIDSMC) and the RBF neural network PID sliding mode control (RBFPDSMC) are compared with this method in two trajectory tracking control simulation experiments. In the first experiment, the average Euclidean distance of the position-tracking error for this method was reduced by approximately 73.6% and 75.3%, respectively, compared to those for RBFPDSMC and RBFPIDSMC. In the second experiment, the average Euclidean distance of the position tracking error for this method was reduced by approximately 86.8% and 88.8%, respectively. The two experiments showed that the proposed control method has a strong anti-jamming ability and tracking effect. The simulation results obtained in the Gazebo environment validated the superiority of this method.

In this paper, a soft robot driven by a gas–liquid phase transition actuator with a new structure is designed; The soft robot is driven by the pressure generated by electrically induced ethanol phase transition. The gas–liquid phase transition drive was found to be able to generate a larger driving force by using only low voltage. Compared with the gas drive of a traditional soft robot, gas–liquid phase transition-driven soft robot does not require a complex circuit system and a huge external energy supply air pump, making its overall structure more compact. At the same time, because of the new structure of the actuator on the soft robot, the soft robot has good gas tightness and less recovery time. A reinforcement depth learning control strategy is also added so that the soft robot with this actuator could better grip objects of different sizes and weights.

Mesenchymal stem cells (MSCs) can be differentiated into various cell lineages under the influence of mechano-niche. Inspired by this approach, this study presents a differentiable stem cell actuator unit (SAU) driven by a shape memory alloy, and a modular robotic framework. Similar to mechanically guided differentiation of MSCs, SAUs can be differentiated into a series of differentiated actuator units (DAUs) under external preload. This process has been modeled, simulated, and experimentally validated, with testing conducted on three distinct types and 14 specifications of DAUs. DAUs weighing as light as 1.96g exhibited outputs reaching up to 10.6 N and 46.32 Nmm. Our team has developed seven application prototypes based on this bio-inspired framework including mobile robots, manipulators and end effectors. This work pioneers the integration of differentiable concepts and principles into the design of modular robots, enabling a wider range of potential configurations and capabilities.