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The report illustrates a stepwise execution for system for an electric drive having a control system using the software ‘Simulink’ on MATLAB. The induction motor (IM) presented in the report is constructed after an circuit in the stator reference frame that is actually based on space vector theory. The control system for motor is prepared via the (indirect) vector control (field oriented control) which is used for the rotor flux reference frame. It makes use of a converter. The prepared simulink model can be potentially implemented as model for illustrating electrical motors. Especially in terms of schooling where the output is centered and focused on getting introduced to new things by doing them and where the outcome is to get introduced to new things by the help of a practical real life approach. The following application thus correlates the space vector theory and practice.

The problems of bus bunching mitigation and the energy management of groups of vehicles have traditionally been treated separately in the literature and been formulated in two different frameworks. The present work bridges this gap by formulating the optimal control problem of the bus line eco-driving and regularity control as a smooth, multi-objective nonlinear program. Since this nonlinear program has only a few coupling variables, it is shown how it can be solved in parallel aboard each bus, such that only a marginal amount of computations need to be carried out centrally. This procedure leverages the structure of the bus line by enabling parallel computations and reducing the communication loads between the buses, which makes the problem resolution scalable in terms of the number of buses. Closed-loop control is then achieved by embedding this procedure in a model predictive control. Stochastic simulations based on real passengers and travel times data are realized for several scenarios with different levels of bunching for a line of electric buses. Our method achieves fast recoveries to regular headways as well as energy savings of up to 9.3% when compared with traditional holding or speed control baselines.

This paper proposes an adaptive sliding mode control (ASMC) with a modified reduced-order proportional integral observer (MROPIO) to provide speed control for a permanent magnet synchronous motor against unknown disturbances, such as system parameter uncertainties and external load torque variation. The ASMC method, based on a new sliding mode reaching law, improves chattering in the control signal and reduces the time required for the system trajectory to reach the sliding mode surface. The MROPIO estimates unknown disturbances and provides feedforward compensation for the ASMC, thus overcoming incorrect estimations of unknown disturbances during speed jumps can cause overshoots in the speed response. All the proposed methods were implemented on an industrial motor drive. The experimental results confirm the validity of the proposed method for practical applications.

International audience; This paper presents a study of control strategies for 5-phase permanent magnet synchronous motors (PMSMs) supplied by a five-leg voltage source inverter. Based on the vectorial decomposition of the multi-phase machine, fictitious machines, magnetically decoupled, allow a more adequate control. In this paper, our study focuses on the vector control of a multi-phase machine using a linear proportional-integral-derivative (PID) current regulator in the cases of sinusoidal and trapezoidal back-electromotive force (EMF) waveforms. In order to determine currents' references, two strategies are adopted. First one aims to minimize copper losses under constant torque, while the second one targets to increase torque for a given copper losses. These techniques are tested under a variable speed control strategy based on a proportional-integral (PI) regulator and experimentally validated.

This paper proposes an adaptive maximum torque per ampere (MTPA) control algorithm for interior permanent magnet synchronous motor (IPMSM) drives. This algorithm is insensitive to motor parameters variation due to operating conditions that is important for systems, which prioritizes reliability and efficiency. The proposed algorithm is designed to improve conventional control schemes, where the output of the speed controller is the commanded quadrature current and direct current is calculated using an MTPA equation. However, in distinction from the conventional MTPA approach, the proposed algorithm does not use any motor parameters. It implements a seeking technique and continuously varies the stator current phase angle while trying to minimize the magnitude of the current. The main contribution of this paper is the development of the method to overcome the main disadvantage of seeking algorithms – the necessity of stable disturbances at the consecutive measurement intervals. This paper adopts the seeking technique for the operation with the load torque varying over mechanical revolution, which is typical for many applications such as compressors, washing machines, robots, mechanical manipulators, servodrives, etc., The feasibility of the proposed algorithm is proven with experimental results shown and described in the corresponding section.

In this paper, a new design of the state observer is proposed to reduce the unstable low-speed region of the adaptive full-order observer (AFO) based speed sensorless speed control of induction motors (IM). The proposed state observer is designed in z-domain rather than in s-domain following the pole-placement approach. The convergence rate of the proposed observer can be made faster than that of the IM poles without extra oscillation effects and the unstable low-speed region of AFO can be reduced to the zero-synchronous-frequency (ZSF) line. Also, a modified adaptation law of the AFO is derived to estimate the rotor speed. Based on the modified AFO, a continuous control set model predictive control (CCS-MPC) is designed for the inner-loop torque control. The cost function of the CCS-MPC is made of the predicted tracking errors on the state and control input. Finally, the dual-loop speed sensorless speed control using the modified AFO and CCS-MPC is presented in simulations and experiments.

This paper designs a novel finite-time extended state observer (FTESO) based super-twisting sliding mode (STSM) control scheme to promote the anti-disturbance property and tracking performance of permanent magnet synchronous motor (PMSM). Firstly, a FTESO combined with generalized super-twisting technique is constructed to estimate the unknown lumped disturbance and compensate the estimated value to the STSM speed controller. Compared with the conventional extended state observer (ESO), the proposed FTESO can make the estimation error converge to the origin in finite time, which provides a faster convergence speed and higher estimation accuracy. Moreover, in order to tackle the problem of performance deterioration caused by inertia mismatch, particularly for PMSM speed control, an inertia identification method is employed to calculate the true value of inertia online to tune the parameters of FTESO and STSM controller automatically. Comprehensive simulation and experimental results demonstrate that the proposed control method can improve the robustness of PMSM system in the presence of load torque and inertia variations.

The high precision control of angular speed in low-speed gimbal servo systems is an essential topic in the operation of the single gimbal control moment gyro (SGCMG). This paper proposes a discrete control algorithm to suppress the influence of various disturbances on the low-speed gimbal servo system and the constraint between controller parameters and sampling time is established to ensure the stability of the system. To begin with, the disturbance analysis of the low-speed gimbal servo system is presented, including the torque ripple in the permanent magnet synchronous motor (PMSM), nonlinear friction, coupling torque caused by the spacecraft motion, and the parameter uncertainties. Then, a composite control method combining the sliding mode controller (SMC) with sliding mode observer (SMO) is applied to the speed loop and q-axis current loop. The interferences are estimated by two sliding mode observers while sliding mode controllers are designed to eliminate the influence brought by the residual disturbances. The stability of the overall system is proved based on the Lyapunov criterion. Finally, simulation and experimental results verify the effectiveness of the proposed composite control method.

In the paper, an improved hybrid particle swarm optimization (IHPSO) algorithm is proposed to solve the optimization problem of controller parameters in the design of permanent magnet synchronous motor (PMSM) servo system. The proposed algorithm presents the directional mutation operation to the particles which fixes the position of some particular particles so that enhance the searching ability to some remote regions. In order to cooperate with directional mutation operation, the updating formula of particles velocity is ulteriorly improved. Then, the proposed IHPSO algorithm is adopted to optimize parameters of the designed controller. A simulation and an experimental platform of PMSM servo system are designed using biological intelligence controller based on hormone regulation for the speed control and feedforward compensation for the position controller, where IHPSO is applied to the parameter optimization for speed and position controller, which validate the effectiveness of the IHPSO algorithm and the designed control system.

This paper presents an evaluation of a family of Adaptive Position Estimators (APEs) for anisotropy-based Self-Sensing Control (SSC) of Permanent Magnet Synchronous Machine (PMSM). The APEs use the Gradient Descent Method (GDM) to minimise a cost function and thus estimate the rotor position. Three different APEs have been developed in previous work and are revised in this paper. Their dynamic performance is experimentally evaluated and compared with each other and with an encoder-based control using the same test bench. Simplified models are derived that allow an analytic calculation of the individual speed control loop dynamics. The speed control bandwidth using the latest APE reaches almost 50% of when using a 2000 slot incremental encoder. In addition, this version of APE with GDM features parameter tuning based on analytic equations.

Switched reluctance motors are nonlinear systems with some uncertainties and unmodeled dynamics. Propulsion force and speed in these motors have inherently high fluctuations complicating their applications. The conventional controllers could not offer a precise performance for nonlinear systems because they require analytical calculations of the partial derivatives. Accordingly, in this article, a multilayer perception is presented to overcome this problem and control a linear motor. Training algorithms require a complete dataset of the system output, which complicates their implementation. To solve this problem, a Kalman filter is used to estimate uncertain parameters. Thus, the proposed control system does not require a complete dataset of the system. It can process data and predict the next values in a short time without complete observations. The proposed control strategy is implemented to a linear switched reluctance motor and the results are compared with two other conventional methods via simulation and experimental tests. The results confirm the ability and accuracy of the proposed method. 2012 IEEE. Scopus 2-s2.0-85122479638

This study suggests an intelligent position-tracking control algorithm for rover vehicles considering actuator (DC motor) dynamics. The parameter and load uncertainties in the vehicle and DC motor dynamics are explicitly handled by modifying the original open-loop system dynamics. The proposed controller forms the conventional multi-loop structure including disturbance observers (DOBs) for each loop. The features of this study fall into three parts: first, the learning part from the real-time feedback gain mechanism (named the self-tuner) in the closed-form for outer loop; second, the adaptation part from the online wheel radius estimation securing the outer-loop control accuracy; and third, the parameter-independent angular acceleration observer-based pole-zero cancellation DC motor speed controller without current feedback considering the inner- and outer-loop vehicle control algorithms. Experimental evidence is also provided to demonstrate the practical merits of the proposed technique with the use of the TETRIX, myRIO-1900, and LabVIEW.

The cascaded structure is generally adopted in the permanent magnet synchronous motor drive system, however, the speed dynamic performance is always deteriorated due to the limited bandwidth of cascade linear controllers. Therefore, in order to realize quick dynamic response, the speed loop is eliminated in the non-cascaded control structure, and the non-cascaded model-free predictive speed control is proposed in this paper. First, the ultra-local model of SMPMSM drive system is established with a compound variable which implicitly unifies the speed and current of different time scales, and then the Lyapunov function is designed to derive control law and generate the inverter reference voltage automatically by considering the delay of digital control. Moreover, the inverter reference voltage is further modified under voltage and current constraints to guarantee system safety and reliable operation. Finally, the feasibility and validity of the proposed method has been verified by system simulation and experimental researches.

As tire forces approach road friction limits, vehicles may always exhibit performance degradation and even instability. The actual capacity of the available road friction imposes coupled limits on a vehicle's longitudinal and lateral accelerations. In this paper, a varying speed method is proposed to design feasible speeds and accelerations, which ensure that the autonomous vehicle will not reach the tire-road friction limits during traversing a clothoid-based path. With the consideration of uncertain traction losses and vehicle parameters, a second-order super-twisting (ST) based speed control strategy is proposed to track above feasible speeds based on varying speed method, and to stabilize the sliding-mode variable of the speed tracking error with relative degree 1. Meanwhile, a second-order quasi-continuous (QC) based path-following control strategy is proposed to follow a desired transition path, and to stabilize the sliding-mode variable of the composite path-following errors with relative degree 2. On this basis, the proposed controllers have been verified to lead good robustness for tracking the ideal speeds and following the desired paths. As compared with the boundary of the autonomous vehicle running at a constant speed, the feasible speed boundary using varying speed method without exceeding the tire-road friction limits can be enlarged up to about 1.59 times, which is decided by the direction change between the entry and exit points of the desired path.

This paper proposes and implements a new wireless power and drive transfer (WPDT) system using wireless switches. A wireless piping network is conceived especially for pipeline transportations and in-pipe robots desiring wireless motor drive. Also, a multi-frequency resonant network is newly explored to generate the variable-frequency-modulated magnetic fields where the wireless switches can extract the drive information directly. Differing from single-frequency T-circuits, this multi-frequency network can offer different resonant frequencies which are not only coupling-independent but also large-scale load-independent. Importantly, it can realize both the power self-equalization and zero-phase-angle operation in transmitter and receiver circuits. To conduct the conceived WPDT, the magnetic resonant modulation is used to control the wireless switches automatically at the receiver side, thus flexibly enabling the bidirectional motions of DC motor and the variable-frequency speed control of AC induction motor. The experimental system efficiency can reach 83.5% based on a 200-W prototype at a 130-mm distance. Both theoretical analysis and hardware experimentation are given to verify the feasibility of proposed WPDT system for wireless piping networks.

In series hybrid electric vehicles, the fossil energy source is composed of an electrical generator driven by an internal combustion engine. Compared to constant speed control, variable speed control of the electrical generator improves the vehicle’s energetic efficiency and releases the battery load. Such control is extremely sensitive because of the nonlinearities. In a plug-in hybrid vehicle, in addition to the obvious inherent variable speed nonlinearity, the large range of battery voltage variation increases the complexity of the controller design. Moreover, in our case, to take advantage of the variable speed feature, the generator load has transient constraints because it must follow the vehicle load. In equivalent applications, for the electrical side, usually there is a use of high-frequency control strategies, based on the generator’s three phases current and voltage acquirement. This paper investigates a low-frequency power control strategy through model-based method for a specific power electronics vehicle architecture. Starting with an adaptive PID based controller to tackle the non-linearity issues, the paper explains progressively the reasons and choices that lead to design a sliding hybrid fuzzy logic controller. The designed controller demonstrates good performance in power tracking and permits to reduce the fuel consumption.

Direct Torque Control (DTC) scheme introduces a robust and simple control of electrical drive. However, its shortcomings such as broad torque ripple and variable switching frequency have offered several improvements like Space Vector Modulation (SVM) strategy, multilevel inverter (MLI) topology, etc. The conventional DTC which is fed by the two-level inverter has limited voltage vector, results in some difficulties to optimize the operation, especially at low operating speed. In contrast to MLI, the abundant of voltage vector has provided various amplitudes and angles that can overcome the problem of conventional DTC. Thus, this paper introduces the selected optimal voltage vector obtained from five-level Cascaded H-Bridge (CHB) inverter that employs in DTC hysteresis-based to achieve better optimization that similar to the DTC-SVM. Initially, the research work begins with an investigation on the performance comparison between a DTC hysteresis-based between two-level inverter (conventional method) and a five-level CHB inverter (proposed method). Here, a DC generator acted as a load is employed to control the operating speed instead of the speed controller (speed controller is negligible). Hence, the DTC method is optimized by minimizing the torque ripple as well as retaining the torque control capability at constant torque region on several operating speed. The selected optimal vector from the look-up table DTC of five-level CHB inverter must be dynamically appropriate to any change of torque (increased or decreased torque). For simplicity, this paper will only discuss the experimental results for both topologies of drive system. From the experimental results, it is verified that the torque ripples by the proposed method have achieved 10% and 50% reduction at high and low operating speed respectively. It is found that the DTC hysteresis-based result simpler control method than DTC-SVM while maintaining similar output performance.

Properly designed roundabouts may lead to safety improvements based on both reducing approaching speeds and controlling traffic. Measurements of deflection of vehicle trajectories are commonly used to estimate roundabout speed control. One of these measurements is the deviation angle, which is mentioned in both the Italian and Swiss road standards and, in specific conditions, can be more effective than other methods. This article presents a general mathematical formulation for linking several geometric parameters with the deviation angle in different rural and urban one-lane roundabout configurations, which is currently missing in the literature. For urban roundabouts, refuge islands for pedestrians and cyclists were considered. Based on the proposed formulation, a sensitivity analysis of the influential geometric parameters was conducted. Results suggest that an insufficient deflection of trajectories (deviation angle less than 45°) is always present for roundabouts with inscribed circle diameter less than 25 m; for urban roundabouts with refuge islands for pedestrians and cyclists having inscribed circle diameter less than 34 m and orthogonal legs; and for roundabouts with angles between opposite legs smaller than 140°. The main parameters which are responsible for a decrease in the deviation angle are: a decrease in the inscribed circle diameter; a decrease in the angle between opposite legs; and an increase in the width of the circulatory lane. Some optimized procedures for roundabout design, the generalized application of the deviation angle method and alternative speed control measures in cases of small deviation angles are discussed.

In order to design a high-performance synchronous reluctance motor (SynRM) drive system, a novel adaptive complementary sliding mode (ACSM) speed control and an effective d -axis current control (EDCC) are proposed in this article. First, a classical proportional-integral based field-oriented control of the SynRM drive with a constant d -axis current command is introduced. However, the constant d -axis current command is obtained by trial and error method or satisfying the minimum excitation current of the SynRM. More importantly, the constant value of the d -axis current command is not suitable for the highly nonlinear and time-varying SynRM drive at the varied load torque conditions. Therefore, an ACSM speed control with the d -axis current control (ACSMSC-DCC) system is designed for the speed regulation of the SynRM. The ACSM speed control is proposed to generate the q -axis current commands, and an EDCC by using the stator flux estimator is proposed to produce the d -axis current commands. Finally, the proposed ACSMSC-DCC system is implemented in a 32-bit floating-point digital signal processor TMS320F28075 and its effectiveness are verified by some experimental results.

The Three Phase Induction Motor (TIM) is one of the most widely used motors due to its low price, robustness, low maintenance cost, and high efficiency. In this paper, a Support Vector Regression (SVR) based controller for TIM speed control using Indirect Vector Control (IVC) is presented. The IVC method is more frequently used because it enables better speed control of the TIM with higher dynamic performance. Artificial Neural Network (ANN) controllers have been widely used for TIM speed control for several reasons such as their ability to successfully train without prior knowledge of the mathematical model, their learning ability, and their fast implementation speed. The SVR-based controller overcomes the drawbacks of the ANN-based controller, i.e. its low accuracy, overfitting, and poor generalization ability. The speed response under the proposed controller is faster in terms of rising and settling time. The dynamic speed response of the proposed controller is also superior to that of the ANN-PI controller. The performance of the proposed controller was compared for TIM speed control with an ANN-PI controller via simulations in SIMULINK.

Inpipe inspection technology is an important method for pipelines detection, and the smooth running speed of the inner detector is a prerequisite for ensuring the quality of inspection. Since the inner detector runs as a black box in the pipeline, it is difficult to observe the current speed state, and the energy source it carries is limited. To overcome the above problems, this article proposes an interval dynamic matrix control algorithm with additional margin (AM-IDMC). First, as an optimized control strategy, interval control can suppress disturbances and reduce energy consumption with smaller actions, which is a novel way to solve the speed control problem of the inner detector. Second, the dynamic matrix control can effectively predict the speed of the inner detector. At the same time, the additional margin method can avoid the actual speed from violating the expected interval to the greatest extent, allowing the controller to intervene in the control as soon as possible. Third, an adaptive penalty coefficient and interval reference trajectory are designed to ensure the rapidity and stability of the speed adjustment process. This article also systematically and completely builds the speed control model of the inner detector from pressurizing station and discharge device. The results have demonstrated that the AM-IDMC algorithm has good performance and has a good effect on the speed control of the inner detector.

Recent developments in emissions regulations are pushing Non-Road Mobile Machineries manufacturers towards the adoption of more efficient solutions to reduce the amount of pollutants per unit of work performed. Electrification can be a reasonable alternative to traditional powertrain to achieve this goal. The higher complexity of working machines architectures requires, now more than ever, better design and testing methodologies to better integrate electric systems into mechanical and hydraulic layouts. In this work, the attention focused on the use of a Hardware in the Loop (HIL) approach to test performance of an energy management strategy (called load observer) developed specifically for an orchard tractor starting from field characterization. The HIL bench was designed to replicate a scaled architecture of a parallel hybrid electric tractor at mechanical and electrical level. The vehicle behavior was simulated with a personal computer connected on the CAN BUS network designed for the HIL system. Several tasks were simulated starting from data gathered during field measurements of a daily use of the machine. Results showed good performance in terms of load split between the two power sources and stability of the speed control although the variability of the applied load.

As an efficient control strategy, model predictive current control (MPCC) has rapid response and simple calculation. This article proposes an improved MPCC scheme for permanent-magnet synchronous hub motor (PMSHM) drives. The mentioned control scheme uses the parameter values at the last moment to obtain the back electromotive force (EMF) and utilizes the obtained back EMF to obtain the predicted current value at the next moment. In the actual application of the motor, to enhance the robustness of the control system, a sliding mode controller is used to replace the conventional proportional-integral (PI) speed loop, and a finite position phase-locked loop based on the dichotomy is added to achieve sensorless speed control and provide an accurate rotor position angle. To improve the steady-state performance, the method of duty cycle is introduced, and the null vector and the actual vector are used together in the same control cycle. The simulation and experimental results both show the effectiveness of the proposed MPCC scheme, and the steady-state performance of MPCC is greatly improved compared with traditional MPCC.

This article presented a single-phase shaded-pole induction motor (SPIM) system, which aims to work in an isolated environment. Compared with the previous wireless SPIM, this system employs less controlled switches and can work at full-resonant condition, while retaining the merit of secondary controllerless. The key is to employ a half-bridge inverter and a proper dual-frequency resonant network; thus, only two controlled switches are adopted at the motor side. Since the SPIM is purposely designed with a dual-stator winding, the system robustness and efficiency can be significantly improved. In this article, simulation has been conducted to verify the system's feasibility and self-drive ability. Meanwhile, a 30-W prototype has been built for the experimental verification, and the results show that this motor can achieve flexible speed control with the transfer distance of 20 mm and the transmission efficiency of 77%.

Minimizing the amount of fuel consumed by a moving vehicle can be formulated as an optimal control problem that determines the speed profile that the vehicle should follow. The fuel consumption is generally a function of speed and acceleration, and is optimized under external parameters (e.g., road grade or surrounding traffic conditions) known to affect fuel economy. Uncertainty in the traffic conditions, and in particular traffic speed, has seldom been investigated in this context, which may prevent the vehicle from following the optimal speed profile and consequently affect the fuel economy and the journey time. This paper describes two stochastic optimal speed control models for minimizing the fuel consumption of a vehicle traveling over a given stretch of road under a given time limit, where the maximum speed that can be achieved by the vehicle over the journey is assumed to be random and follow a certain probability distribution. The models include chance constraints that either (i) limit the probability that the desired vehicle speed exceeds the traffic speed, or (ii) bound the probability that the journey time limit is violated. The models are then extended into distributionally robust formulations to capture any uncertainties in the probability distribution of the traffic speed. Computational results are presented on the performance of the proposed models and to numerically assess the impact of traffic speed variability and journey duration on the desired speed trajectories: The results affirm that uncertainty in traffic speeds can significantly increase the amount of fuel consumption and the journey time of the speed profiles created by deterministic model. Such increase in journey duration can be mitigated by incorporating the stochasticity at the planning stage using the models described in this paper, and more so with the distributionally robust formulations particularly with higher levels of uncertainty. The solutions themselves generally exhibit low levels of speeds, which ensure the feasibility of the speed profile against any variabilities in the traffic speed.

This article proposes a cascade proportional-integral second-order sliding mode control (PI-SMC) method for matrix converter-fed brushless doubly fed induction machine (BDFIM). The PI-SMC method consists of a PI speed controller with an inner-loop second-order sliding mode control (SMC). The PI controller provides a reference to the inner-loop second-order SMC with constraints according to the system requirements in terms of maximum current and speed. As the discontinuous sign function in the traditional SMC is replaced by the continuous supertwisting function, the chattering problem is eliminated for inner-loop second-order SMC. Moreover, the inner-loop second-order SMC has robustness and interference immunity against nonlinear disturbances of the BDFIM. Simulation and experimental results demonstrate the excellent robust tracking performance of the proposed PI-SMC method.

In order to develop a wide speed range operation of synchronous reluctance motor (SynRM) drive system for constant torque, constant power and reduced power regions, an extended speed control (ESC) with maximum efficiency (ME), flux weakening (FW), and maximum torque per voltage (MTPV) is proposed in this study. Firstly, a conventional FW control system with field-oriented control is introduced. Moreover, to achieve the ME operation, a lookup table of the results of ME analysis by using the finite element analysis technology is adopted to produce the d-axis current commands for the current control mode of the SynRM in the constant torque region. Furthermore, the FW voltage controller is employed to produce the incremental value of the stator voltage command to achieve the maximum voltage limit. In addition, a novel feedforward based voltage angle controller with an MTPV limiter is designed to generate the voltage angle command for the voltage control mode in the constant power and reduced power regions. Finally, the proposed ESC system is implemented in a 32-bit floating-point digital signal processor TMS320F28075 and its robustness and effectiveness are verified by some experimental results.

An optimal control strategy of winner-take-all (WTA) model is proposed for target tracking and cooperative competition of multi-UAVs (unmanned aerial vehicles). In this model, firstly, based on the artificial potential field method, the artificial potential field function is improved and the fuzzy control decision is designed to realize the trajectory tracking of dynamic targets. Secondly, according to the finite-time convergence high-order differentiator, a double closed-loop UAV speed tracking the controller is designed to realize the speed control and tracking of the target tracking trajectory. Numerical simulation results show that the designed speed tracking controller has the advantages of fast tracking, high precision, strong stability and avoiding chattering. Finally, a cooperative competition scheme of multiple UAVs based on WTA is designed to find the minimum control energy from multiple UAVs and realize the optimal control strategy. Theoretical analysis and numerical simulation results show that the model has the fast convergence, high control accuracy, strong stability and good robustness.

The forces of drag, tire and road surface friction resistance, the drive motor characteristics, the hill climbing angle, and other non-linear dynamic factors affect the performance of electric vehicles (EV) tremendously. The proposed self-construction of type-2 fuzzy neural network (SCT2FNN) controller was based on the robust typical type-2 fuzzy neural network (T2FNN) controller. T2FNN with the self-construct parameter and online learning could estimate the angular velocity of the motor operation to control the EV. Hence, SCT2FNN with the self-construct parameter and online learning could promptly track the speed of EV. SCT2FNN also could estimate the torque control of DC motor. The simulation results showed that SCT2FNN controller was more efficient than PID controller, while the speed was controlled by considering the difference of the climbing slope.

Currently, electric vehicles can achieve high driving performance when they use brushless motors. Moreover, the demand for more electric vehicles pushes companies to improve their driving performance and improve the speed and torque response. However, the power electronics stages inside the vehicle must be studied during the vehicle's design and operation. Generally, when high driving performance is reached, the command signals for achieving this performance stressed the power electronics stage, so its lifetime could be decremented drastically. Thus, the power electronic stage has to be incorporated into the design of the speed controller and be optimized to expand its lifetime. This paper deals with the optimization process between the power electronic stage and the speed controller. As a result, the power electronics stage will expand its lifetime. Since there is not enough information about the lifetime of the power electronic stage into electric vehicles and how it is affected by the high driving performance conditions, this paper contributes to generating knowledge about its lifetime and how it could be improved. Besides, this work illustrates the implementation of a fuzzy logic speed controller optimized by particle swarm optimization using co-simulation. This controller is adapted according to the speed and lifetime requirements. A conventional PID controller is compared against the proposed controller to show the advantages of using co-simulation between Multisim and LabVIEW. As a result, the superior performance of the proposed controller is demonstrated when the temperature is controlled in electric vehicles, so the lifetime of the power electronics stage could be expanded. Also, this paper presents a novel approach using a metaheuristic optimization that runs online.

This paper presents a dual-motor drive system with a three-phase two-level inverter for independent speed control of a three-phase motor and a single-phase motor. The single-phase motor is connected in series between the neutral point of the three-phase motor and the midpoint of two split DC-link capacitors. As the single-phase motor current is a zero-sequence component flowing in the three-phase motor, it does not affect the operation of the three-phase motor. The proposed drive system can reduce the number of power switches compared with the conventional drive system that uses a single-phase and a three-phase inverter. Thus, the proposed system can reduce the cost of the drive system. Simulations and experiments are presented to verify the proposed drive system.

This study suggests a dynamic current cut‐off frequency‐based pole‐zero cancellation speed controller for permanent magnet synchronous motors (PMSMs). The proposed self‐tuning algorithm automatically increases the current cut‐off frequency during only the transient periods and restores it as approaching the steady‐state operation. The outer loop control injects the active damping effect, resulting in a closed‐loop order reduction by pole‐zero cancellation from the particularly structured feedback gain. These two benefits contribute to the following advantages: (a) lowering the steady‐state current cut‐off frequency to improve the relative stability margin and (b) securing the capability of assigning the desired cut‐off frequency to both the inner and outer loops in the first‐order low‐pass filter form. A 500‐W PMSM experimental prototype platform confirms the effectiveness of the proposed controller.

Microfluidic-based biosensors have been developed for their precise automatic reaction control. However, these biosensors require external devices that are difficult to transport and use. To overcome this disadvantage, our group made an easy-to-use, cheap, and light pumpless three-dimensional photo paper–based microfluidic analytical device (3D-μPAD; weight: 1.5 g). Unlike conventional paper-based microfluidic analytical devices, the 3D-μPAD can be used to control fluid flow in a 3D manner, thus allowing sophisticated multi-step reaction control. This device can control fluid flow speed and direction accurately using only the capillary-driven flow without an external device like a pump. The flow speed is controlled by the width of the microfluidic channel and its surface property. In addition, fluid speed control and 3D-bridge structure enable the control of fluid flow direction. Using these methods, multi-step enzyme-linked immunosorbent assay (ELISA) can be done automatically in sequence by injecting solutions (sample, washing, and enzyme’s substrate) at the same time in the 3D-μPAD. All the steps can be performed in 14 min, and data can be analyzed immediately. To test this device, thioredoxin-1 (Trx-1), a biomarker of breast cancer, is used as the target. In the 3D-μPAD, it can detect 0–200 ng/mL of Trx-1, and the prepared 3D-μPAD Trx-1 sensor displays excellent selectivity. Moreover, by analyzing the concentration of Trx-1 in real patients and healthy individuals’ blood serum samples using the 3D-μPAD, and comparing results to ELISA, it can be confirmed that the 3D-μPAD is a good tool for cancer diagnosis.

Control of the speed as well as shaping the speed transient response of a surface-mounted permanent magnet synchronous motor (PMSM) is achieved using the method of feedback linearization and extended high-gain observer. To recover the performance of feedback linearization, an extended high-gain observer is utilized to estimate both the speed of the motor and the disturbance present in the system. The observer is designed based on a reduced model of the PMSM, which is realized through the application of singular perturbation theory. The motor parameters are assumed uncertain and we only assume knowledge of their nominal values. The external load torque is also assumed to be unknown and time-varying, but bounded. Stability analysis of the output feedback system is given. Experimental results confirm the performance and robustness of the proposed controller and compare it to the cascaded proportional integral (PI) speed controller.

In the traditional finite control‐set model predictive direct speed control (FCS‐MPDSC), the coupling items in the current equation have been ignored because of predictive complexity, leading to imperfect prediction accuracy and control performance. Meanwhile, the current error and speed error are both included in the weighted sum cost function. The two types of errors lead to the difficulty of weight coefficients' distribution, which can only rely on subjective experiences. Thus, an FCS‐MPDSC strategy based on the 2nd‐Taylor‐series model with quadratic cost function is proposed. The dynamic performance has been greatly improved because the input variables of the Taylor series model are unified as the basic voltage vector with the same control period. And the static error is also reduced as the weight coefficient matrix of the quadratic cost function is solved offline. On this basis, the verification experiments are carried out. The results show that a better dynamic and static performance is achieved at the same time by the proposed strategy.

In this work, fractional order proportional integral derivative controller (FOPIDC) is proposed and implemented for speed control of electric machines using nature-inspired metaheuristic optimization algorithm (MOA) named as flower pollination algorithm (FPA). Investigations are performed by considering the second-order transfer function of D.C Motor. FOPIDC constants K P , K I , K D , λ , and μ are optimized by considering FPA. The desired response of the electric machine is obtained by using tuned parameters of FOPIDC. After that, FOPIDC response using FPA is compared with PIDC response using FPA in the sense of time-domain specifications and performance indices (PI) like ‘integral of square error (ISE), integral of time-weighted square error (ITSE), integral of absolute error (IAE) and integral of time-weighted absolute error (ITAE)’. Finally, the desired response of electric machine using FPA based FOPIDC is compared with desired response of electric machine using firefly algorithm (FA) and particle swarm optimization (PSO) algorithm based FOPIDC.

Handling the internal parametric variations and the nonlinearities of the high rated wind energy conversion system (WECS) is remained among the main challenges to maximize the produced energy, ameliorate its quality and ensure its efficient integration on the grid. In this context, a robust integral sliding mode control (ISMC) with Lyapunov function is proposed to control the active and reactive powers of a doubly fed induction generator (DFIG) based wind turbine, and to assure high dynamic performances according to the wind speed variation. To operate around an optimal rotational speed, a robust MPPT algorithm with mechanical speed control based on artificial Neural Network Controller (ANNC) is presented in order to extract the maximum power. Thereafter, the robust integral SMC are replaced by Field Oriented Control (FOC_PI) for comparative purposes. The objective is to prove the best performances of the system obtained by the proposed control method in terms of the dynamic response, total harmonic distortion THD (%) of the injected current into the grid, the reference tracking ability, Overshoot (%), precision and robustness. The effectiveness and robustness of each control techniques has been implemented and tested under MATLAB/Simulink environment by using a 1.5 MW wind system model.

In this article, an improved direct instantaneous torque control (DITC) based on adaptive terminal sliding mode control (ATSMC) is proposed to suppress torque ripple and enhance the anti-interference ability for switched reluctance motors (SRMs). The proposed speed controller used in the outer loop of DITC is based on the rate of change in load torque. The state of the system converges quickly to the equilibrium point and realizes fast output of the given torque without known disturbance upper bound. Moreover, a novel reaching law is proposed to reduce torque ripple and startup time. The stability of the adaptive terminal sliding mode motion is verified by using the Lyapunov function. The improved DITC system is implemented on a 16/10 segmented-rotor SRM. Compared with sliding mode control and terminal sliding mode control, the proposed ATSMC in the DITC system behave with better performances in aspects of the speed and torque response, antiinterference ability, robustness, and torque ripple suppression. Simulation and experimental results are given to verify the effectiveness of the improved DITC.

Along with the rapid development of science and technology, including robot technology. It can lighten and help the work done by humans. In the military world, the role of robots is indispensable both in peace and in war. This research was carried out using a pure experimental method on the Design of a Propulsion System for Combat Robots using the Internet of Things (IoT) Based on Raspberry 4.0. The working principle of this combat robot is based on data sent using MiFi (Tx) which will then be processed using Raspberry 4.0 and transmitted to MiFi (Rx) via the Internet of Things (IoT). Furthermore, the data received will be processed so that the driving system will produce driving force using a differential drive system, the difference in rotation of the driving motor is regulated by the microprocessor to the MY 1061 speed control, the pulse signal sent by the Raspberry Pi will regulate the amount of current sent to the DC motor so that the motor can turn left and right.

This paper proposes a new non-cascade fast nonsingular terminal sliding mode control algorithm based on disturbance observers for the speed control of permanent magnet synchronous motor (PMSM). To ensure the high precision control and fast finite-time convergence, a fast nonsingular terminal sliding mode controller is designed for PMSM. The novel controller adopts the single-loop non-cascade control structure. Then, to improve the anti-disturbance ability, a nonlinear disturbance observer and a dual disturbance observer are respectively designed to estimate the disturbances during motor operation, which are used for the feed-forward compensation control. The composite control method can effectively alleviate the larger chattering caused by the high sliding mode gain. Finally, the stability is proved based on Lyapunov stability theory. Simulation and experimental results show that the proposed method has the advantages of fast convergence and strong robustness against parameter variations and external disturbances.

For the speed control system of autonomous electric vehicle (AEV), challenge happens with how to determine an appropriate driving speed to satisfy the dynamic environment while resisting uncertainty and disturbance. Therefore, this paper proposes a robust optimal speed control approach based on hierarchical architecture for AEV through combining deep reinforcement learning (DRL) and robust control. In decision‐making layer, a deep maximum entropy proximal policy optimization (DMEPPO) algorithm is presented to obtain an optimal speed via dynamic environment information, heuristic target entropy and adaptive entropy constraint. In motion control layer, to track the learned optimal speed while resisting uncertainty and disturbance, a robust speed controller is designed by the linear matrix inequality (LMI). Finally, simulation experiment results show that the proposed robust optimal speed control scheme based on hierarchical architecture for AEV is feasible and effective.

Vehicle automation is among the best possible solutions for traffic issues, including traffic accidents, traffic jams, and energy consumption. However, the user acceptance of automated vehicles is critical and is affected by riding comfort. In addition, human factors in automated vehicle control should be clear. This study evaluates the effect of different courses on driving comfort in automated vehicles using field experiments with 25 subjects. This study focused on lateral motion, but speed control was not targeted. Further, generating a path for obstacle avoidance and lane keeping, which have several constraining conditions, was also not targeted. Rendering a comfortable path is beneficial for developing an acceptable system as a car developer and for building new curves for automated or driving assistance systems from the perspective of construction. The automated vehicle drove at a speed of 30 km/h on four courses, namely, clothoid, two types of spline curves, and arc, based on the real intersection. Each participant sat on both the driver and passenger seat and answered a questionnaire. The experimental data indicated the clothoid course to be the most comfortable, while the arc was most uncomfortable for a significance level of 1%. These tendencies are applicable to driver and passenger seats, all genders, and experiences and will be beneficial for human factor research in automated vehicle control.