This study addresses the finite‐time tracking control problem for autonomous airships with uncertainties and external disturbances. Six‐degree‐of‐freedom kinematics and dynamics equations are established by considering the model uncertainties and external disturbances. To handle the model uncertainties and external disturbances, a finite‐time sliding mode disturbance observer (DOB) is designed. To have a good tracking performance, a finite‐time command‐filtered backstepping‐supertwisting controller is proposed with DOB. By using Lyapunov theory, the finite‐time convergence of tracking errors and the stability of the control method is proved. Finally, the simulation results show that the controller can track the desired trajectory well in spite of model uncertainties and external disturbances.

Parylene is widely used in the field of biomedical engineering due to its strong barrier properties against water and ions, as well as its superior biocompatibility. In this study, we have developed a pressure sensor module specifically for electronic capsules, where parylene is employed as the sealing material. The pressure sensors were packaged using parylene C and parylene N films with thicknesses of 15 μm, 30 μm, 45 μm, 60 μm, and 75 μm. The effect of parylene coating thickness on the performance of pressure sensors was investigated quantitatively by experiments. Various performance indicators such as sensitivity, linearity, root mean square error (RMSE), repeatability and dynamic response of the pressure sensors packaged with different parylene thicknesses have been thoroughly tested and analyzed. This research bears significance in informing the design of implantable pressure sensors.

This paper is concerned with sampled-data control problem for a class of nonlinear systems with input quantization. The nonlinear system is converted into a linear-like system with modeling error by using partition of unity method. A time-dependent Lyapunov functional is introduced to capture the characteristic of nonlinear sampled-data systems and the exponential stability conditions are derived by the use of inequality techniques. The desired sampled-data controller is then synthesized. An example is provided to illustrate the effectiveness and benefits of the proposed scheme.

In recent years, repackaging technology has been widely used in miniaturized implantable pressure sensors. However, the current packaging structure still has significant problems regarding biocompatibility, environmental adaptability, and measurement accuracy, which greatly limits its application in vivo measurement systems. In this paper, we report a method for implantable pressure sensor repackaging based on silicone oil, polydimethylsiloxane (PDMS) film, and polymer (parylene) coating. A systematic investigation using finite element analysis is conducted to assess the impact of packaging components on sensor performance, providing a solid theoretical foundation for packaging optimization. Experimental results demonstrate that when the parylene coating thickness is below 30 µm, the sensors exhibit superior linearity, repeatability, and reliability, along with exceptional stability and dynamic response across clinically relevant pressure ranges. This research provides valuable insights into the packaging design of implantable pressure sensors, facilitating the development of more stable, reliable, and cost-effective sensors for in vivo measurement systems.

Colonic pressure is a principal parameter reflecting intestinal motility, and is also an important basis for diagnosing intestinal motility disorders. The physiological signals of the colon are mixed with the disturbing signals such as breathing and heartbeat due to the complex environment of the intestine. It is essential to find an effective method to reduce the noise in the signals. To solve this problem, a combined denoising method of colonic pressure signals is proposed in this paper, which combines the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet transform. In this method, colonic peristalsis pressure signal is first separated out by wavelet transform, then decomposed by CEEMDAN. According to pearson correlation coefficient, the high-frequency intrinsic mode functions (IMFs) with noise was selected and denoised by wavelet threshold. Finally, the signal was reconstructed by combining the reserved IMFs with the denoised IMFs. The validity of the proposed method is verified by the real colon pressure data collected by the wireless motility capsule in this paper. The experimental results show that the combined denoising method can deal with the noise better than the single denoising method on the premise of fully retaining the real components of the colonic pressure signal.