Anex vivoplatform that can simulate physiological vascular environments while maintaining experimental accessibility is essential for developing effective magnetically guided microswarm delivery systems and transitioning them from a laboratory concept to a practical therapeutic tool. In this article, we present a novel platform that uses decellularized spinach leaves as engineered vascular networks to manipulate magnetic microswarm. Through quantitative characterization, including structural integrity analysis, optical transparency measurements, and perfusion studies, we validate the platform’s suitability for microswarm manipulation. Integrating a customized permanent magnetic control system and machine learning-based visual tracking, we demonstrate four key maneuvers for targeted therapeutic applications within the proposed platform, including forward motion, backward motion, selective branch navigation, and localized aggregation at targeted sites. This physiologically relevant yet accessible platform establishes a crucial bridge betweenin vitromodels andin vivosystems. Specifically, it enables the systematic development, quantitative evaluation, and control strategy implementation of magnetically guided microswarm, potentially accelerating the development of magnetic microswarm-based targeted therapy systems.

The motion of magnetic microrobots relies on an external magnetic field. Current electromagnetic propulsion systems are often complex in structural design, challenging in control strategies, and associated with high energy consumption. In this study, we introduce a mechano-electromagnetic hybrid actuation system that employs only a pair of Helmholtz coils to generate an oscillating uniform magnetic field, combined with a rotating platform to modify the field’s direction, thereby enabling the control of microrobot speed and direction. The properties of this hybrid actuation system were modeled and validated to analyze the magnetic field distribution within the workspace. The design and actuation of two microrobots are described in detail. Furthermore, a visual feedback closed-loop control strategy for the system was developed. A series of navigation control experiments were conducted, demonstrating that the proposed hybrid actuation system achieves effective microrobot actuation and precise control while reducing manufacturing costs and simplifying control strategies. The proposed novel mechano-electromagnetic hybrid structure contributes to the field of electromagnetically driven systems and magnetically controlled microrobots.

In minimally invasive surgery, pneumatic soft actuators have gained significant attention due to their promising applications. Valves, which are critical components in pneumatic driving systems, typically operate in only two modes in current research. In multi-actuator systems, this limitation results in a large number of valves and a bulky driving system, which restricts the system’s portability. In this study, we report a four-mode, multi-stable, energy-efficient electromagnetic mini valve. A coil serves as the driving source. The valve has three outlets, enabling it to control up to three actuators independently. The moving components include a cylindrical magnet and a ring magnet. They are concentrically arranged and can only move along the axial direction. These magnets can realize four distinct positions based on the electromagnetic force and their mutual magnetic force. Moreover, the multi-stable design reduces energy consumption. The two cores make the magnets stable when no current is applied. The valve exhibits a shortest response time of 4.75 ms and an energy cost of 0.007 J. It can handle a maximum flow rate of 9.7 L/min at a pressure of 200 kPa, with a maximum back pressure of 100 kPa. Its energy-efficient and four-operating-mode characteristics prove its potential in pneumatic soft actuator systems. A modular design by connecting multiple mini valves reduces the number of pumps and valves, thereby lowering the overall size and weight of pneumatic driving systems.

This article addresses the challenge of state observer design for sliding mode security control in Markov jump cyber-physical systems subjected to stochastic injection attacks. To enhance network efficiency, a dynamic event-triggered algorithm is introduced in the communication channel. First, the design begins with a Luenberger state observer featuring an adaptive compensator. This configuration aims to effectively counteract malicious attacks. Second, an integral sliding hyperplane is formulated within the estimation space, which serves as the foundation for deriving the sliding mode dynamics, ensuring robustness against disturbances. Recognizing the diversity of transition rates (TRs), an elastic sliding mode controller is designed to accommodate three distinct types of TRs, which is also strategically designed to guarantee reachability and maintain sliding motion. Third, stochastic stability with an$H_{\infty }$attenuation level is conducted separately for each type of TR. Correspondingly, the development of an algorithm for determining threshold parameters in triggered conditions is presented. Simultaneously, a proof of the nonexistence of Zeno behavior is provided, ensuring the stability and efficiency of the proposed system. Finally, a simulation study using a practical model is included to empirically demonstrate the validity of the proposed method in a real-world context.

Microrobots show great targeted-delivery potential in precision medicine. This article presents the use of a model-free approach to the navigation control of a microrobot in the cardiovascular environment. With the proposed approach, the microrobot can adapt to the non-Newton behavior of blood and environmental disturbances when it moves in blood vessels without knowledge of blood-velocity distribution. The referred trajectory of the navigated microrobot is generated by using a breadth-first search and genetic algorithm, aiming to minimize the energy consumption. The proposed navigation controller combines sliding mode control, backstepping control, and disturbance compensation. A high-gain extended state observer is designed to estimate and reject the uncertainties of model parameters and environmental disturbances. Simulations are performed to demonstrate the effectiveness of the proposed approach in a small artery compared with other control methods.

Electromagnetically actuated microparticles can be widely applied in the field of biomedicine, for its advantages of minimally invasive feature and approachability to complex microenvironments. In this paper, we propose a robust feedback control approach for precise 3D manipulation of a microparticle actuated by a self-constructed electromagnetic coil system. Model uncertainties, environmental disturbances as well as actuator energy loss problem are all taken into account in the controller design. It is shown that this proposed control scheme can enable the entire system to maintain the input-to-state stability in presence of various perturbations. Experimental results have demonstrated the effectiveness of the proposed control approach. Success of the current study will benefit the precise motion control with high throughput in applications of the targeted material delivery.

Precise manipulation of microparticles has received considerable attention for its great potential applications to clinical medicine. Among the existing manipulation techniques, the method of magnetic force based manipulation exhibits great advantages for its minimally-invasive feature and insensitivity to biological substance, making it ideally suitable to in vivo environment. On the other hand, increasing demand for accurate and high throughput magnetic manipulation highlights the need of incorporating automation technology in the manipulation. In this paper, we propose an automated control approach to manipulating a magnetic microparticle (bead) with a home-designed electromagnetic coil system. A simplified two-order dynamic model for a microparticle suspended in fluidic environment is established first. A closed-loop controller with utilizing visual feedback is then developed based on input-to-state stability and backstepping methodology. The proposed controller guarantees that the microparticle follows the desired trajectory even in presence of environmental uncertainties and disturbances. Experiments are performed to demonstrate the effectiveness of the proposed approach.