Recently, researches in the interventional microrobots have taken the lion’s share in the field of biomedical devices. The aim of biomedical microrobots is to reach inaccessible areas of the human body and deliver drugs in high position. This work presents a new approach to elaborate a new physics-based model for novel self-propelled swimming microrobots. The robot is composed of an ellipsoidal head and hybrid tail that are propelled by a joint polymer metal composite actuator. Green’s function is used to solve the coupled elastic/fluid problems caused by the vibrating hybrid tail in a fluid. This method allowed producing the velocity of microrobot. For the control of the swimming microrobot in hazardous environment, the flatness-fuzzy-based control strategy is developed to eliminate the effect of nonlinear model and to generate the optimal trajectory of flat outputs. The fuzzy technique is aimed to adjust the law control gains in real time for improving the precision of the proposed microrobot in tracking the desired trajectory in fluid. The multi-objectives genetic algorithm is employed to optimize both the reference trajectory and the design parameters in order to enhance the time response and to minimize the dynamic tracking error of the trajectory. To achieve this, a numerical model based on accurate solutions of Navier–Stokes equations is developed. The results of the simulation show that the proposed design with ellipsoidal head gives better performance in comparison with that achieved by the conventional structure.

Recently, researches in the interventional microrobots have taken the lion’s share in the field of biomedical devices. The aim of biomedical microrobots is to reach inaccessible areas of the human body and deliver drugs in high position. This work presents a new approach to elaborate a new physics-based model for novel self-propelled swimming microrobots. The robot is composed of an ellipsoidal head and hybrid tail that are propelled by a joint polymer metal composite actuator. Green’s function is used to solve the coupled elastic/fluid problems caused by the vibrating hybrid tail in a fluid. This method allowed producing the velocity of microrobot. For the control of the swimming microrobot in hazardous environment, the flatness-fuzzy-based control strategy is developed to eliminate the effect of nonlinear model and to generate the optimal trajectory of flat outputs. The fuzzy technique is aimed to adjust the law control gains in real time for improving the precision of the proposed microrobot in tracking the desired trajectory in fluid. The multi-objectives genetic algorithm is employed to optimize both the reference trajectory and the design parameters in order to enhance the time response and to minimize the dynamic tracking error of the trajectory. To achieve this, a numerical model based on accurate solutions of Navier–Stokes equations is developed. The results of the simulation show that the proposed design with ellipsoidal head gives better performance in comparison with that achieved by the conventional structure.

Recently, researches in the interventional microrobots have taken the lion’s share in the field of biomedical devices. The aim of biomedical microrobots is to reach inaccessible areas of the human body and deliver drugs in high position. This work presents a new approach to elaborate a new physics-based model for novel self-propelled swimming microrobots. The robot is composed of an ellipsoidal head and hybrid tail that are propelled by a joint polymer metal composite actuator. Green’s function is used to solve the coupled elastic/fluid problems caused by the vibrating hybrid tail in a fluid. This method allowed producing the velocity of microrobot. For the control of the swimming microrobot in hazardous environment, the flatness-fuzzy-based control strategy is developed to eliminate the effect of nonlinear model and to generate the optimal trajectory of flat outputs. The fuzzy technique is aimed to adjust the law control gains in real time for improving the precision of the proposed microrobot in tracking the desired trajectory in fluid. The multi-objectives genetic algorithm is employed to optimize both the reference trajectory and the design parameters in order to enhance the time response and to minimize the dynamic tracking error of the trajectory. To achieve this, a numerical model based on accurate solutions of Navier–Stokes equations is developed. The results of the simulation show that the proposed design with ellipsoidal head gives better performance in comparison with that achieved by the conventional structure.