Data-Driven Adaptive Tracking Control of Unknown Autonomous Marine Vehicles

This paper is concerned with data-driven adaptive tracking control for unknown autonomous marine vehicles (AMVs) with uncertainties and disturbances. By deploying the data-driven technique and observer design, an equivalent data model of the AMV is firstly established. Based on the proposed data model, a novel data-driven adaptive tracking controller is designed, and the corresponding stability analysis for the closed-loop AMV system is presented theoretically. Finally, simulation studies are given to demonstrate the validity of the main results

The Effect of Scalp Point Cluster-Needling on Learning and Memory Function and Neurotransmitter Levels in Rats with Vascular Dementia

We observed the effect of scalp point cluster-needling treatment on learning and memory function and neurotransmitter levels in rats with vascular dementia (VD). Permanent ligation of the bilateral carotid arteries was used to create the VD rat model. A Morris water maze was used to measure the rats’ learning and memory function, and the changes in neurotransmitter levels in the rats’ hippocampus were analyzed. The results show that scalp point cluster-needling can increase the VD rat model’s learning and memory score. The VD rat model’s learning and memory score was significantly different when compared with that of the sham operation group P<0.05. Hippocampal acetylcholine (ACh), dopamine (DA), and 5-hydroxytryptamine (5-HT) concentrations significantly decreased in the rat model. Compared with the model group, the scalp point cluster-needling group’s ACh concentration markedly increased and DA and 5-HT levels increased as well. In conclusion, scalp point cluster-needling can improve learning and memory function in VD rats, and its function may be related to an increase in neurotransmitter release

SMC for nonlinear stochastic switching systems with quantization

This paper focuses on the sliding mode control (SMC) design for nonlinear stochastic switching systems subject to semi-Markov switching parameters and signal quantization. The aim of this work is to design an efficient SMC scheme under quantization error effect. To this end, a mode-independent sliding surface is adopted to avoid the potential repetitive jumping effects. Then, based on the weak infinitesimal operator theory, sufficient conditions are given for the corresponding stochastic stability criteria. Furthermore, an appropriate SMC law is proposed to drive the state signals onto the predefined manifold and the effect of quantization error can be effectively attenuated. Finally, a single-link robot arm model is provided to illustrate the effectiveness of the theoretical findings