Sallam Mohamed, Shamseldin Mohamed A, Ficuciello Fanny
ICAROS Lab, Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy.
Department of Mechanical Engineering, Helwan University, Cairo, Egypt.
Front Robot AI. 2024 Aug 13;11:1439427. doi: 10.3389/frobt.2024.1439427. eCollection 2024.
Microparticles are increasingly employed as drug carriers inside the human body. To avoid collision with environment, they reach their destination following a predefined trajectory. However, due to the various disturbances, tracking control of microparticles is still a challenge. In this work, we propose to use an Adaptive Nonlinear PID (A-NPID) controller for trajectory tracking of microparticles. A-NPID allows the gains to be continuously adjusted to satisfy the performance requirements at different operating conditions. An study is conducted to verify the proposed controller where a microparticle of 100 m diameter is put to navigate through an open fluidic reservoir with virtual obstacles. Firstly, a collision-free trajectory is generated using a path-planning algorithm. Secondly, the microparticle dynamic model, when moving under the influence of external forces, is derived, and employed to design the A-NPID control law. The proposed controller successfully allowed the particle to navigate autonomously following the reference collision-free trajectory in presence of varying environmental conditions. Moreover, the particle could reach its targeted position with a minimal steady-state error of 4 m. A degradation in the performance was observed when only a PID controller was used in the absence of adaptive terms. The results have been verified by simulation and experimentally.