In this study, a cell puncture mechanism driven by a piezoelectric ceramic (PEA) is fabricated, and a novel precision robust motion controller is proposed to complete the cell puncture. A system dynamics model of the cell puncture mechanism is simplified by considering the hysteresis and disturbance as unknown terms to avoid the parameter identification of nonlinear dynamics in engineering applications. A sliding mode control (SMC) strategy with fast reaching law and proportional-integral- differential (PID)-type sliding surface, denoted as FPIDSMC, based on the simplified Bouc-Wen model is combined with time delay estimation (TDE) technology to form a novel controller, called FPID-TDE controller in this study. The stability of the controller is proven based on the Lyapunov theory. Computer simulation and semi-physical simulation experiments show the TDE technology can estimate and compensate accurately the unknown terms and does not require prior knowledge of unknown disturbance boundaries, thereby reducing controller gain. SMC can realize fast response speed, few steady-state errors, continuous output, and avoid chattering. Through a micropuncture experiment of a zebrafish embryo, the proposed FPID-TDE controller is shown to have high efficiency, high precision, and strong robustness. Therefore, the proposed FPID-TDE controller can be applied effectively to other micro nano-positioning systems driven by PEA.

A proposed strategy for managing airspace and preventing illegal drones from compromising security involves the use of autonomous drones equipped with three key functionalities. Firstly, the implementation of YOLO-v5 technology allows for the identification of illegal drones and the establishment of a visual-servo system to determine their relative position to the autonomous drone. Secondly, an extended Kalman filter algorithm predicts the flight trajectory of illegal drones, enabling the autonomous drone to compensate in advance and significantly enhance the capture success rate. Lastly, to ensure system robustness and suppress interference from illegal drones, an adaptive fast nonsingular terminal sliding mode technique is employed. This technique achieves finite time convergence of the system state and utilizes delay estimation technology for the real-time compensation of unknown disturbances. The stability of the closed-loop system is confirmed through Lyapunov theory, and a model-based hardware-in-the-loop simulation strategy is adopted to streamline system development and improve efficiency. Experimental results demonstrate that the designed autonomous drone accurately predicts the trajectory of illegal drones, effectively captures them using a robotic arm, and maintains stable flight throughout the process.

The rapidly exploring random tree star (RRT*) algorithm is widely used to solve path planning problems. However, the RRT* algorithm and its variants fall short of achieving a balanced consideration of path quality and safety. To address this issue, an improved discretized artificial potential field-QRRT* (IDAPF-QRRT*) path planning strategy is introduced. Initially, the APF method is integrated into the Quick-RRT* (Q-RRT*) algorithm, utilizing the attraction of goal points and the repulsion of obstacles to optimize the tree expansion process, swiftly achieving superior initial solutions. Subsequently, a triangle inequality-based path reconnection mechanism is introduced to create and reconnect path points, optimize the path length, and accelerate the generation of sub-optimal paths. Finally, by refining the traditional APF method, a repulsive orthogonal vector field is obtained, achieving the orthogonalization between repulsive and attractive vector fields. This places key path points within the optimized vector field and adjusts their positions, thereby enhancing path safety. Compared to the Q-RRT* algorithm, the DPF-QRRT* algorithm achieves a 37.66% reduction in the time taken to achieve 1.05 times the optimal solution, and the IDAPF-QRRT* strategy nearly doubles generated path safety.