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This article proposes the use of a deep reinforcement learning method—and precisely a variant of the deep deterministic policy gradient (DDPG) method known as twin delayed DDPG, or TD3—for maximum power point tracking in wind energy conversion systems that use permanent magnet synchronous generators (PMSGs). An overview of the TD3 algorithm is provided, together with a detailed description of its implementation and training for the considered application. Simulation results are provided, also including a comparison with a model‐based control method based on feedback linearization and linear‐quadratic regulation. The proposed TD3‐based controller achieves a satisfactory control performance and is more robust to PMSG parameter variations as compared to the presented model‐based method. To the best of the authors' knowledge, this article presents for the first time an approach for generating both speed and current control loops using DRL for wind energy conversion systems. [ABSTRACT FROM AUTHOR]

The current study concerns improving the performance of a renewable energy system using systematically designed control algorithms. The performance of the system under study is evaluated under two operating scenarios: the first in which the system consists of only a wind-driven synchronous generator connected to the utility grid; in the second scenario, the generator is combined with a photo-voltaic solar system and a battery for supplying a load. Each system component is modeled and thoroughly described. To maximize the benefits of solar and wind energies, two separate maximum power point tracking procedures are adopted. Furthermore, to enhance the generator's dynamics, a novel predictive control scheme is designed and validated by comparing its performance with traditional predictive control. The novel predictive controller utilized a simple and unique cost function to avoid the shortages of traditional predictive controllers. For standalone operation, an effective procedure is adopted to ensure the power balance between the generation, storage, and isolated load units. To evaluate the effectiveness of the designed controllers under different operating regimes, Matlab/Simulink is utilized for this task. The obtained results confirm the superiority of the novel predictive scheme used with the synchronous generator over the classic control approach for the two operating scenarios. This has been shown in the form of reduced ripples and reduced current harmonics. The obtained results are also confirming the validity of the adopted maximum power tracking strategies with solar panels and wind turbines as well. Furthermore, balanced power delivery is achieved thanks to the adopted management strategy for standalone operation, which enhances the overall system performance. [ABSTRACT FROM AUTHOR]

Three-phase dual active bridge converters (DAB3) are a widely used topology in battery charging applications thanks to their numerous advantages, such as bidirectional power flow, galvanic isolation, low output current ripple, and inherent soft-switching. In such applications, three single-phase transformers are commonly employed as the AC-link to simplify manufacturing and reduce costs. These transformers' leakage inductance can be utilized instead of the external leakage inductance to achieve high power density. However, the assumption of uniformity in these inductances is not always accurate as they can vary significantly during fabrication. This study presents a comprehensive analysis of the impact of transformer leakage inductance variation, which can deviate by up to 24% from the desired value. The effects of this variation are investigated from different perspectives, including power transfer, soft-switching range, root-mean-square (RMS) current, and the temperature rise of the transformer winding. Although the power transfer and total copper loss of transformers are changed insignificantly even under highly mismatched leakage inductance, the currents and thermal distribution among phases are considerably impacted. Based on statistical probability, a maximum leakage inductance variation threshold of 10–15% compared to the desired value is recommended to ensure the maximum acceptable temperature rise among phases. Experimental results are presented to validate the analysis. [ABSTRACT FROM AUTHOR]

This article presents a comprehensive review of current Magnetic Imaging Techniques (MIT), specifically Magnetic Particle Imaging (MPI) and Magnetic Resonance Imaging (MRI) methods as well as their impacts and potential in biomedical applications. The paper particularly focuses on several perspectives of MPI and MRI techniques including technical considerations, advantages, limitations, applications, future trends. Surprisingly, there are not many review articles that focused on MPI and MRI. This review will give a complete overview of present technology, perspectives, and potential future developments for MPI and MRI. MIT has become an essential and integral part of medical diagnosis in many large medical clinics and hospitals. This area is rapidly developing and evolving to meet the huge demands in medical diagnosis and prevention. MPI and MRI are promising technologies that provide reliable and effective diagnosis for many diseases including cancer. MIT uses non‐invasive scanning to obtain a detailed image of the body's tissues or organs without the necessity for surgery. These techniques allow us to detect symptoms of serious diseases at an early stage. Early detection also gives patients the chance to get the right care before their illnesses advance to a late, incurable stage, potentially saving many lives. [ABSTRACT FROM AUTHOR]

This paper proposes a novel disturbance rejection control scheme for permanent magnet synchronous motor (PMSM) drives. Based on the framework of modified disturbance observer (DOB)-based control, the final topology of the proposed disturbance rejection proportional–integral (DR-PI) controller includes a pre-filter and a controller in a proportional–integral (PI) form. The proposed DR-PI control scheme is practical with a straightforward gain tuning rule. Note that the gain selection method is the main issue of not only conventional PI controllers but also advanced methods such as DOB-based controllers. In addition, by starting from the framework of modified DOB, this paper also proves that the PI controller with an pre-filter possesses a disturbance rejection ability similar to a DOB-based control method. To the best of our knowledge, this is the first time that such a simple and effective PI controller is designed for the speed control of PMSMs as well as theoretically proven to have a perfect disturbance rejection ability. This paper shows the steps of selecting the parameters of the proposed controller in terms of the parameters of a desired plant model, disturbance observer and compensator. Hence, unlike a traditional DOB case, in this approach, one can simultaneously tune the controller and observer at the same time. The appearance of the pre-filter from the modified DOB scheme solves an overshoot problem, thus the general motor operation is significantly improved, which is validated by experiments. The experimental evidence under two scenarios of load torque change and speed change prove the effectiveness of the proposed method compared to conventional PI and DOB control (DOBC) schemes. All the experiments were implemented on a 300 W PMSM of a setup manufactured by Lucas-Nuelle GmbH with a digital signal processor. [ABSTRACT FROM AUTHOR]

In this article, integral servomechanism based state‐dependent Riccati equation (SDRE) nonlinear output feedback control for wind energy conversion systems (WECSs) has been proposed. The numerical off‐line solving the proposed SDRE control requires to formulate algebraic Riccati equation and algebraic Lyapunov equation. This method approximates the solution of SDRE with Taylor series expansions which are solved with MATLAB solvers. The maximum power point tracking is achieved by defining relation between the optimal angular shaft speed and maximum power providing optimal tip speed ratio in variable‐speed WECSs. The proposed control can significantly reduce the angular shaft speed errors of the permanent magnet synchronous generator (PMSG)‐based WECSs without use of disturbance observers to compensate model uncertainties, modeling errors and noise as in the conventional SDRE control method. The simulation results demonstrate the superior results under mean average errors and root mean square error evaluation methods of the angular shaft speed of PMSG. The performance of the proposed integral servomechanism based SDRE control system has been improved by 80.67% and 80.05% in both scenarios compared to the conventional SDRE control based on compensation technique with disturbance observers. [ABSTRACT FROM AUTHOR]

A high-speed supercavitating vehicle is a future underwater vehicle which exploits the supercavitating propulsion technology providing a promising way to increase the vehicle speed. Robust control challenges include complex vehicle maneuvering dynamics caused by factors such as undesired switching, delayed state dependency, and nonlinearities. As effective and applicable controllers, a novel fractional-order sliding mode controller is proposed to robustly control the uncertain high-speed supercavitating vehicle system against external disturbances. The control scheme uses sliding mode control and can produce better control actions than conventional the integer-order counterpart. In this algorithm, the fractional calculus is applied to calculate the noninteger integral or derivative in the sliding mode control algorithm, providing new capabilities for uncertain high-speed supercavitating vehicle control in seeking to operate the underwater vehicle better. The performance of the proposed fractional-order sliding mode controller has been proven through analytic simulation results, which show fast responses with smooth control actions and the ability to deal with nonlinear planing force and external disturbance. One of the interesting features of the fractional-order control system is the time convergence rate of the sliding variable vector, which is greatly improved compared with the integer-order sliding mode control. Finally, the robust control system with a novel fractional-order sliding mode controller algorithm, using high flexibility of controlling undersea vehicles, can provide superior dynamical performance with stability compared with its integer-order counterpart against system uncertainties and disturbances. [ABSTRACT FROM AUTHOR]

This paper introduces a direct model predictive voltage control (DMP VC) for a sensorless five-phase induction motor drive. The operation of the proposed sensorless DMP VC is based on the direct control of the applied stator voltages instead of controlling the torque and flux as in model predictive direct torque control (MP DTC). Thus, the simplicity of the control system is enhanced, which saves the computational time and reduces the commutation losses as well. The methodology based on which the proposed sensorless DMP VC performs its operation depends on minimizing a cost function that calculates the error between the reference and actual values of the direct and quadrature (d-q) axes components of stator voltages. The reference values of d-q components of stator voltages are obtained through incorporating the deadbeat control within the proposed model predictive system. A robust back-stepping observer is proposed for estimating the speed, stator currents, rotor flux, and rotor resistance. The validity of the proposed sensorless DMP VC is confirmed through performing detailed and extensive comparisons between the proposed DMP VC and MP DTC approach. The obtained results state that the drive is exhibiting better performance under the proposed DMP VC with less ripples content and reduced computational burden. Moreover, the proposed back-stepping observer has confirmed its effectiveness in estimating the speed and other variables for a wide range of speed operation. [ABSTRACT FROM AUTHOR]

Recently, wind power production has been under the focus in generating power and became one of the main sources of alternative energy. Generating of maximum power from wind energy conversion system (WECS) requires accurate estimation of aerodynamic torque and uncertainties presented in the system. The current paper proposed the generalized high‐order disturbance observer (GHODO) with integral sliding mode control (ISMC) for extraction of maximum power via variable speed wind turbine by accurate estimation of wind speed. The assumption in previous works that considers the aerodynamic torque as slow‐varying is not applicable for the real system. Therefore, the high‐order disturbance observers were designed for precise estimation of uncertainties with fast‐changing behavior. A robust control system was designed to control the speed of the rotor at the optimal speed ratio. The obtained simulation results have shown the better performance characteristics than conventional linear quadratic regulator (LQR) approach. The stability of the proposed algorithm was proven by Lyapunov stability anaysis. Simulations results were obtained in Matlab/Simulink environment. [ABSTRACT FROM AUTHOR]

The issue of tracking the optimal power for wind energy conversion systems (WECSs) via regulating the rotor speed of the generator is taken into account in this study. Additionally, a novel polynomial observer is proposed for estimating not only aerodynamic torque in WECSs but also d‐axis and electromagnetic torque. Therefore, in this new approach, only the rotor speed of the generator is required to be measured instead of measuring all state variables. With the new observer form, the aerodynamic torque does not need to satisfy any constraints, which are mandatory in the previous methods. It should be noted that this methodology has not been investigated for the WECSs in any previous papers. To design a complete control system, a linear optimal control method cooperated with the polynomial observer is employed to track the optimal trajectory of a generator. Moreover, in this paper, the permanent magnet synchronous generator is used. In addition, on the basis of the Lyapunov theory and sum‐of‐square (SOS) technique, the conditions for observer synthesis are derived in the main theorems. Finally, the simulation results are provided to prove the effectiveness and merit of the proposed method. [ABSTRACT FROM AUTHOR]

Summary: Tip speed ratio control is a popular method in wind energy conversion systems in order to capture the maximum power. This method, however, requires wind speed information, which is difficult in practice to accurately measure it. Therefore, estimation methods are usually applied, where a high‐precision estimate leads to a high‐efficient system. Based on the fact that the wind speed varies in a random way, this paper proposes a generalized high‐order observer to estimate the aerodynamic torque and the wind speed accordingly. This observer algorithm releases the assumption that the wind speed should be slowly varying, which is required in previous observer designs. Moreover, two other generalized high‐order observers are also applied to estimate the uncertainties, which depend on state variables and cannot be considered as slow‐varying disturbances. Using the outputs of these observers, a robust high‐performance optimal control system is developed for the rotor speed to keep the optimal tip speed ratio. The stability analysis of the designed control system is fully presented. The effectiveness of the proposed technique is validated via simulation studies. [ABSTRACT FROM AUTHOR]

In recent studies, we introduced the concept of functionalized magnetic force as a method to prevent nanoparticles from sticking to vessel walls caused by extensive simulation and in vitro experiments involving a Y-shaped channel. In this paper, we further investigated the effectiveness of the functionalized magnetic force with a realistic 3-D vessel through simulations. For the simulations, we considered a more realistic continuous injection of particles with different magnetic forces and frequencies. Based on the results from our simulation studies, we performed in vivo mice experiments to evaluate the effectiveness of using a functionalized magnetic force to aid magnetic nanoparticles (MNPs) in crossing the blood–brain barrier (BBB). To implement the functionalized magnetic force, we developed an electromagnetic actuator regulated by a programmable direct current power supply. Our results indicate that a functionalized magnetic field (FMF) can effectively prevent MNPs from sticking, and also guide them across the BBB. We used 770 nm fluorescent carboxyl MNPs in this paper. Following intravenous administration of MNPs into mice, we applied an external magnetic field to mediate transport of the MNPs across the BBB and into the brain. Furthermore, we evaluated the differential effects of FMFs (0.25, 0.5, and 1 Hz) and constant magnetic fields (CMFs) on the transport of MNPs across the BBB. Our results showed that an FMF is more effective than a CMF in the transport and uptake of MNPs across the BBB in mice. In particular, applying an FMF with a 3 A current and 0.5 Hz frequency mediated the greatest transport and uptake of MNPs across the BBB in mice. [ABSTRACT FROM PUBLISHER]

This paper presents a nonlinear optimal direct torque control (DTC) scheme of interior permanent magnet synchronous motors (IPMSMs) based on an offline approximation approach for electric vehicle (EV) applications. First, the DTC problem is reformulated in the stationary reference frame in order to avoid estimating the stator flux angle, which the previous DTC schemes in the rotating stator reference frame require. Thus, the proposed DTC method eliminates the Park's transformation, and consequently, it reduces the computational efforts. Particularly, since the estimated stator flux angle is not accurate in low speed range, the proposed method that does not need this information can significantly improve the control performance. Moreover, a nonlinear optimal DTC algorithm is proposed to deal with the nonlinearity of the IPMSM drive system. In this paper, a simple offline θ-D approximation technique is utilized to appropriately determine the controller gains. Via an IPMSM test bed with a TI TMS320F28335 DSP, the experimental results demonstrate the feasibility of the proposed DTC method by accomplishing better control performances (e.g., more stable in low speed region, much smaller speed and torque ripples, and faster dynamic responses) compared to the conventional proportional-integral DTC scheme under various scenarios with the existence of parameter uncertainties. [ABSTRACT FROM PUBLISHER]

This paper proposes an image-based visual servo (IBVS) controller for the 3D translational motion of the quadrotor unmanned aerial vehicles (UAV). The main purpose of this paper is to provide asymptotic stability for vision-based tracking control of the quadrotor in the presence of uncertainty in the dynamic model of the system. The aim of the paper also includes the use of flow of image features as the velocity information to compensate for the unreliable linear velocity data measured by accelerometers. For this purpose, the mathematical model of the quadrotor is presented based on the optic flow of image features which provides the possibility of designing a velocity-free IBVS controller with considering the dynamics of the robot. The image features are defined from a suitable combination of perspective image moments without using the model of the object. This property allows the application of the proposed controller in unknown places. The controller is robust with respect to the uncertainties in the translational dynamics of the system associated with the target motion, image depth, and external disturbances. Simulation results and a comparison study are presented which demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper proposes nonlinear optimal controller and observer schemes based on a θ-D approximation approach for surface-mounted permanent magnet synchronous motors (PMSMs). By applying the θ- D method in both the controller and observer designs, the unsolvable Hamilton–Jacobi–Bellman equations are switched to an algebraic Riccati equation and state-dependent Lyapunov equations (SDLEs). Then, through selecting the suitable coefficient matrices, the SDLEs become algebraic, so the complex matrix operation technique, i.e., the Kronecker product applied in the previous papers to solve the SDLEs is eliminated. Moreover, the proposed technique not only solves the problem of controlling the large initial states, but also avoids the excessive online computations. By utilizing a more accurate approximation method, the proposed control system achieves superior control performance (e.g., faster transient response, more robustness under the parameter uncertainties and load torque variations) compared to the state-dependent Riccati equation-based control method and conventional PI control method. The proposed observer-based control methodology is tested with an experimental setup of a PMSM servo drive using a Texas Instruments TMS320F28335 DSP. Finally, the experimental results are shown for proving the effectiveness of the proposed control approach. [ABSTRACT FROM PUBLISHER]

This paper proposes a robust adaptive voltage control of three-phase voltage source inverter for a distributed generation system in a standalone operation. First, the state-space model of the load-side inverter, which considers the uncertainties of system parameters, is established. The proposed adaptive voltage control technique combines an adaption control term and a state feedback control term. The former compensates for system uncertainties, while the latter forces the error dynamics to converge to zero. In addition, the proposed algorithm is easy to implement, but it is very robust to system uncertainties and sudden load disturbances. In this paper, a stability analysis is also carried out to show the robustness of the closed-loop control system. The proposed control strategy guarantees excellent voltage regulation performance (i.e., fast transient response, zero steady-state error, and low THD) under various types of loads such as balanced load, unbalanced load, and nonlinear load. The simulation and experimental results are presented under the parameter uncertainties and are compared to the performances of the corresponding nonadaptive voltage controller to validate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive control method of three-phase inverters for stand-alone distributed generation systems (DGSs). The proposed voltage controller includes two control terms: an adaptive compensating term and a stabilizing term. The adaptive compensating control term is constructed to avoid directly calculating the time derivatives of state variables. Meanwhile, the stabilizing control term is designed to asymptotically stabilize the error dynamics of the system. Also, a fourth-order optimal load current observer is proposed to reduce the number of current sensors and enhance the system reliability and cost effectiveness. The stability of the proposed voltage controller and the proposed load current observer is fully proven by using Lyapunov theory. The proposed control system can establish good voltage regulation such as fast dynamic response, small steady-state error, and low total harmonic distortion under sudden load change, unbalanced load, and nonlinear load. Finally, the validity of the proposed control strategy is verified through simulations and experiments on a prototype DGS test bed with a TMS320F28335 DSP. For a comparative study, the control scheme of feedback linearization for multi-input and multioutput is implemented, and its results are presented in this paper. [ABSTRACT FROM PUBLISHER]

This work presents a control scheme to control a grid-connected single-phase photovoltaic (PV) system. The considered system has four 250 W solar panels, a non-inverting buck-boost DC-DC converter, and a DC-AC inverter with an inductor-capacitor-inductor (LCL) filter. The control system aims to track and operate at the maximum power point (MPP) of the PV panels, regulate the voltage of the DC link, and supply the grid with a unity power factor. To achieve these goals, the proposed control system consists of three parts: an MPP tracking controller module with a fuzzy-based modified incremental conductance (INC) algorithm, a DC-link voltage regulator with a hybrid fuzzy proportional-integral (PI) controller, and a current controller module using a linear quadratic regulator (LQR) for grid-connected power. Based on fuzzy control and an LQR, this work introduces a full control solution for grid-connected single-phase PV systems. The key novelty of this research is to analyze and prove that the newly proposed method is more successful in numerous aspects by comparing and evaluating previous and present control methods. The designed control system settles quickly, which is critical for output stability. In addition, as compared to the backstepping approach used in our past study, the LQR technique is more resistant to sudden changes and disturbances. Furthermore, the backstepping method produces a larger overshoot, which has a detrimental impact on efficiency. Simulation findings under various weather conditions were compared to theoretical ones to indicate that the system can deal with variations in weather parameters. [ABSTRACT FROM AUTHOR]

This paper presents a nonlinear optimal speed controller based on a state-dependent Riccati equation (SDRE) for permanent magnet synchronous motor (PMSM). An SDRE-based near optimal load torque observer is also proposed to provide the load torque information for the controller. In both designs, the stability is analytically proven, and the Taylor series method is used to find an approximate solution because the SDRE cannot be directly solved. The SDRE-based optimal controller and the observer can ensure better control performance such as no overshoot and fast transient response in speed tracking than the linear conventional controllers such as linear quadratic regulator and proportional–integral controller even under the variations of the model parameters and load torque. The proposed SDRE-based control strategy is implemented on a PMSM testbed using TMS320F28335 DSP. The simulation and experimental results are given to prove the feasibility of the proposed control scheme. [ABSTRACT FROM PUBLISHER]

Underwater vehicles (UVs) are subjected to various environmental disturbances due to ocean currents, propulsion systems, and un-modeled disturbances. In practice, it is very challenging to design a control system to maintain UVs stayed at the desired static position permanently under these conditions. Therefore, in this study, a nonlinear dynamics and robust positioning control of the over-actuated autonomous underwater vehicle (AUV) under the effects of ocean current and model uncertainties are presented. First, a motion equation of the over-actuated AUV under the effects of ocean current disturbances is established, and a trajectory generation of the over-actuated AUV heading angle is constructed based on the line of sight (LOS) algorithm. Second, a dynamic positioning (DP) control system based on motion control and an allocation control is proposed. For this, motion control of the over-actuated AUV based on the dynamic sliding mode control (DSMC) theory is adopted to improve the system robustness under the effects of the ocean current and model uncertainties. In addition, the stability of the system is proved based on Lyapunov criteria. Then, using the generalized forces generated from the motion control module, two different methods for optimal allocation control module: the least square (LS) method and quadratic programming (QP) method are developed to distribute a proper thrust to each thruster of the over-actuated AUV. Simulation studies are conducted to examine the effectiveness and robustness of the proposed DP controller. The results show that the proposed DP controller using the QP algorithm provides higher stability with smaller steady-state error and stronger robustness. [ABSTRACT FROM AUTHOR]

This paper proposes a super-twisting adaptive sliding mode control law for maximum power point tracking of wind energy conversion systems based on permanent magnet synchronous generators (PMSGs). The employed sliding mode control algorithm can be considered an effective solution, as it retains the robustness properties of classical sliding mode control methods in the presence of disturbances and parameter uncertainties; at the same time, it provides chattering reduction via gain adaptation and generation of second-order sliding modes. In our work, a nonlinear multi-input multi-output tracking control problem is solved to maximize the captured power. Simulation results are presented using real wind speed data, and discussed for the proposed controller and four other sliding mode control solutions for the same problem, in the presence of variations of stator resistance, stator inductance and magnetic flux linkage. The proposed controller achieves the best trade-off between tracking performance and chattering reduction among the five considered solutions: compared to a standard sliding mode control algorithm, it reduces the amount of chattering from two to five orders of magnitude, and the steady-state error on PMSG rotor velocity of one order of magnitude. Also, it reduces the above-mentioned steady-state error of four orders of magnitude with respect to a previously-proposed linear quadratic regulator based integral sliding mode control law for the same system. [Display omitted] • Maximum power point tracking of PMSG rotor speed in WECSs. • Super-twisting sliding mode control with automatic adaptation of control gains. • Robustness to parameter variations and external disturbances. • Reduction of chattering compared to (adaptive) first-order sliding mode methods. • Simulation results provided with real wind speed profile and parameter variations. [ABSTRACT FROM AUTHOR]

This article presents the assessment of time-dependent national-level restrictions and control actions and their effects in fighting the COVID-19 pandemic. By analysing the transmission dynamics during the first wave of COVID-19 in the country, the effectiveness of the various levels of control actions taken to flatten the curve can be better quantified and understood. This in turn can help the relevant authorities to better plan for and control the subsequent waves of the pandemic. To achieve this, a deterministic population model for the pandemic is firstly developed to take into consideration the time-dependent characteristics of the model parameters, especially on the ever-evolving value of the reproduction number, which is one of the critical measures used to describe the transmission dynamics of this pandemic. The reproduction number alongside other key parameters of the model can then be estimated by fitting the model to real-world data using numerical optimisation techniques or by inducing ad-hoc control actions as recorded in the news platforms. In this article, the model is verified using a case study based on the data from the first wave of COVID-19 in the Republic of Kazakhstan. The model is fitted to provide estimates for two settings in simulations; time-invariant and time-varying (with bounded constraints) parameters. Finally, some forecasts are made using four scenarios with time-dependent control measures so as to determine which would reflect on the actual situations better., Competing Interests: Kok Yew Ng is an Academic Editor for PeerJ., (© 2021 Do et al.)

Magnetic nanoparticles (MNPs) are recently used in a drug delivery system to pass the blood brain barrier. However, because the magnetic force acting on particles is proportional to their volumes, as the size of particles is small, the large magnetic field is required to produce enough magnetic force for overcoming the hydrodynamic drag force as well as other forces in blood vessels. Other difficulties for controlling MNPs are the complicated behavior of hydrodynamic drag force and uncertain factors in their dynamics. Therefore, open-loop control methods cannot guarantee guiding every MNP to the correct location. Considering these challenges, this paper introduces a feedback control approach for magnetic nanoparticles (MNPs) in blood vessels. To the best of our knowledge, this is the first time feedback controller that is designed for MNPs without aggregation. Simulation studies in MATLAB and real-time verifications on a physical model in COMSOL-MATLAB interface are performed to prove the feasibility of the proposed approach. It is shown that the proposed control scheme can accurately and effectively navigate the MNP to the correct path with feasible hardware supports.

The "impermeability" of the blood-brain barrier (BBB) has hindered effective treatment of central nervous system (CNS) disorders such as Alzheimer's disease (AD), which is one of the most common neurodegenerative disorders. A drug can be delivered to a targeted disease site effectively by applying a strong electromagnetic force to the conjugate of a drug and magnetic nanocontainers. This study developed a novel nanotechnology-based strategy to deliver therapeutic agents to the brain via the BBB as a possible therapeutic approach for AD. First, a novel approach for an electromagnetic actuator for guiding nanocontainers is introduced. Then, we analyzed the in vivo uptake in mice experimentally to evaluate the capacity of the nanocontainers. In the mouse model, we demonstrated that magnetic particles can cross the normal BBB when subjected to external electromagnetic fields of 28 mT (0.43 T/m) and 79.8 mT (1.39 T/m). Our study also assessed the differential effects of pulsed (0.25, 0.5, and 1 Hz) and constant magnetic fields on the transport of particles across the BBB in mice injected with magnetic nanoparticles (MNPs) via a tail vein. The applied magnetic field was either kept constant or pulsed on and off. Relative to a constant magnetic field, the rate of MNP uptake and transport across the BBB was enhanced significantly by a pulsed magnetic field. Localization inside the brain was established using fluorescent MNPs. These results using 770-nm fluorescent carboxyl magnetic nanocontainers demonstrated the feasibility of the proposed electromagnetic targeted drug delivery actuator. These results establish an effective strategy for regulating the biodistribution of MNPs in the brain through the application of an external electromagnetic field. This might be a valuable targeting system for AD diagnosis and therapy.