To enhance locoregional therapies for liver cancer treatment, we propose in this study a mathematical model to optimize the transcatheter arterial delivery of therapeutical agents. To maximize the effect of the treatment and minimize adverse effects on the patient, different mathematical models of the tumor growth are considered in this study to find the optimal number of the therapeutic drug-loaded magnetic nanoparticles to be administered. Three types of therapy models are considered, e.g., angiogenesis inhibition therapy, chemotherapy and radiotherapy. We use state-dependent Riccati equations (SDRE) as an optimal control methodology framework to the Hahnfeldt's tumor growth formulation. Based on this, design optimal rules are derived for each therapy to reduce the growth of a tumor through the administration of appropriate dose of antiangiogenic, radio- and chemo-therapeutic agents. Simulation results demonstrate the validity of the proposed optimal delivery approach, leading to reduced intervention time, low drug administration rates and optimal targeted delivery.

This paper proposes a novel Takagi-Sugeno fuzzy predictor observer to tackle the problem of the constant and known delay in the measurements. The proposed observer is developed for a trajectory-tracking problem of a wheeled mobile robot where a parallel-distributed compensation control is used to control the robot. The L2-stability of the proposed observer is also proven in the paper. Both, the control and the observer gains are obtained by solving the proposed system of linear matrix inequalities. To illustrate the efficiency of the proposed approach, an experimental comparison with another predictor observer was done.

Magnetic nanoparticles (MNPs) are very attractive components in many biomedical applications, particularly as therapeutic magnetic microcarriers (TMMC) for drug delivery. Although MNPs can be effectively gathered and transported using external magnetic fields, the optimal delivery is yet to be fully investigated. In this paper, we discuss the modeling and the characterization of deformable softmagnetic microrobots under various magnetic field conditions. The microrobots considered consist of superparamagnetic iron oxide (SPIO) immersed in different carrier fluids, and the behavior of which has been characterized experimentally under weak magnetic fields. The experimental results clearly show that the observations properly follow the model prediction. Soft magnetic microrobots with controllable shape deformation have great potential for targeted drug delivery due to the adaptability of their characteristics to environmental conditions (e.g. vessel size, velocity, shear stress).

Trajectory tracking of microrobots presents a significant challenge in biomedical research applications such as drug delivery and artery unclogging. Microrobots are inherently subject to uncertainties and unmodeled dynamics, complicating their control. Additionally, the overheating of coils in the electromagnetic actuation system can limit controller performance. In this paper, we propose an observer-based optimal control strategy for microrobots, where the desired trajectory is generated online by means of a joystick. The control law addresses the problem of coils overheating by minimizing energy consumption while achieving trajectory tracking despite uncertainties and unmodeled dynamics. For practical implementation, we introduce a modified nonlinear extended state observer to estimate the entire state of the microrobot. Analytical tuning approaches for the control law and observer are developed using D-pole placement technique and Kharitonov’s theorem. This solution is an alternative to the use of cooling systems, which require more energy consummation and often reduce the performance of the controller. The tracking performance of the proposed control strategy is evaluated through simulation and experimental tests on a dedicated platform. Both simulation and practical results demonstrate the effectiveness of the developed control strategy.