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The purpose of this article is to treat the nonlinear optimal control problem in EV traction systems which are based on 5-phase induction motors. Five-phase permanent magnet synchronous motors and five-phase asynchronous induction motors (IMs) are among the types of multiphase motors one can consider for the traction system of electric vehicles (EVs). By distributing the required power in a large number of phases, the power load of each individual phase is reduced. The cumulative rates of power in multiphase machines can be raised without stressing the connected converters. Multiphase motors are also fault tolerant because such machines remain functional even if failures affect certain phases.

A novel nonlinear optimal control approach has been developed for five-phase IMs. The dynamic model of the five-phase IM undergoes approximate linearization using Taylor series expansion and the computation of the associated Jacobian matrices. The linearization takes place at each sampling instance. For the linearized model of the motor, an H-infinity feedback controller is designed. This controller achieves the solution of the optimal control problem under model uncertainty and disturbances.

To select the feedback gains of the nonlinear optimal (H-infinity) controller, an algebraic Riccati equation has to be solved repetitively at each time-step of the control method. The global stability properties of the control loop are demonstrated through Lyapunov analysis. Under moderate conditions, the global asymptotic stability properties of the control scheme are proven. The proposed nonlinear optimal control method achieves fast and accurate tracking of reference setpoints under moderate variations of the control inputs.

Comparing to other nonlinear control methods that one could have considered for five-phase IMs, the presented nonlinear optimal (H-infinity) control approach avoids complicated state-space model transformations, is of proven global stability and its use does not require the model of the motor to be brought into a specific state-space form. The nonlinear optimal control method has clear implementation stages and moderate computational effort.

In the transportation sector, there is progressive transition to EVs. The use of five-phase IMs in EVs exhibits specific advantages, by achieving a more balanced distribution of power in the multiple phases of the motor and by providing fault tolerance. The study’s nonlinear optimal control method for five-phase IMs enables high performance for such motors and their efficient use in the traction system of EVs.

Nonlinear optimal control for five-phase IMs supports the deployment of their use in EVs. Therefore, it contributes to the net-zero objective that aims at eliminating the emission of harmful exhaust gases coming from human activities. Most known manufacturers of vehicles have shifted to the production of all-electric cars. The study’s findings can optimize the traction system of EVs thus also contributing to the growth of the EV industry.

The proposed nonlinear optimal control method is novel comparing to past attempts for solving the optimal control problem for nonlinear dynamical systems. It uses a novel approach for selecting the linearization points and a new Riccati equation for computing the feedback gains of the controller. The nonlinear optimal control method is applicable to a wider class of dynamical systems than approaches based on the solution of state-dependent Riccati equations.

The electromechanical brake system is leading the latest development trend in railway braking technology. The tolerance stack-up generated during the assembly and production process catalyzes the slight geometric dimensioning and tolerancing between the motor stator and rotor inside the electromechanical cylinder. The tolerance leads to imprecise brake control, so it is necessary to diagnose the fault of the motor in the fully assembled electromechanical brake system. This paper aims to present improved variational mode decomposition (VMD) algorithm, which endeavors to elucidate and push the boundaries of mechanical synchronicity problems within the realm of the electromechanical brake system.

The VMD algorithm plays a pivotal role in the preliminary phase, employing mode decomposition techniques to decompose the motor speed signals. Afterward, the error energy algorithm precision is utilized to extract abnormal features, leveraging the practical intrinsic mode functions, eliminating extraneous noise and enhancing the signal’s fidelity. This refined signal then becomes the basis for fault analysis. In the analytical step, the cepstrum is employed to calculate the formant and envelope of the reconstructed signal. By scrutinizing the formant and envelope, the fault point within the electromechanical brake system is precisely identified, contributing to a sophisticated and accurate fault diagnosis.

This paper innovatively uses the VMD algorithm for the modal decomposition of electromechanical brake (EMB) motor speed signals and combines it with the error energy algorithm to achieve abnormal feature extraction. The signal is reconstructed according to the effective intrinsic mode functions (IMFS) component of removing noise, and the formant and envelope are calculated by cepstrum to locate the fault point. Experiments show that the empirical mode decomposition (EMD) algorithm can effectively decompose the original speed signal. After feature extraction, signal enhancement and fault identification, the motor mechanical fault point can be accurately located. This fault diagnosis method is an effective fault diagnosis algorithm suitable for EMB systems.

By using this improved VMD algorithm, the electromechanical brake system can precisely identify the rotational anomaly of the motor. This method can offer an online diagnosis analysis function during operation and contribute to an automated factory inspection strategy while parts are assembled. Compared with the conventional motor diagnosis method, this improved VMD algorithm can eliminate the need for additional acceleration sensors and save hardware costs. Moreover, the accumulation of online detection functions helps improve the reliability of train electromechanical braking systems.

In this paper, a new front end converter (FEC) topology has been proposed for the switched reluctance (SR) motor drive. This study aims to present the performance analysis of FEC-based SR motor drive using various types of control schemes like conventional proportional integral (PI) controller, fuzzy logic controller (FLC) and fuzzy-tuned proportional integral controller (Fuzzy-PI).

The proposed FEC-based SR motor drive with various control strategies is derived for the torque ripple minimization and speed control.

The steady state and the dynamic response of the FEC-based SR motor drive are analyzed using three different controllers under change in speed and loading conditions. The Fuzzy-PI-based control scheme improves the dynamic response of the system when compared with the FLC and the conventional PI controller.

The hardware prototype has been implemented for the FEC-based SR motor drive by using the Xilinx SPARTAN 6 FPGA processor. The experimental verification has been conducted and the results have been measured under steady state and dynamic conditions.

Permanent magnet synchronous spherical motors can have wide use in robotics and industrial automation. They enable three-DOF omnidirectional motion of their rotor. They are suitable for several applications, such as actuation in robotics, traction in electric vehicles and use in several automation systems. Unlike conventional synchronous motors, permanent magnet synchronous spherical motors consist of a fixed inner shell, which is the stator, and a rotating outer shell, which is the rotor. Their dynamic model is multivariable and strongly nonlinear. The treatment of the associated control problem is important.

In this paper, the multivariable dynamic model of permanent magnet synchronous spherical motors is analysed, and a nonlinear optimal (H-infinity) control method is developed for it. Differential flatness properties are proven for the spherical motors’ state-space model. Next, the motors’ state-space description undergoes approximate linearization with the use of first-order Taylor series expansion and through the computation of the associated Jacobian matrices. The linearization process takes place at each sampling instance around a time-varying operating point, which is defined by the present value of the motors’ state vector and by the last sampled value of the control input vector. For the approximately linearized model of the permanent magnet synchronous spherical motors, a stabilizing H-infinity feedback controller is designed. To compute the controller’s gains, an algebraic Riccati equation has to be repetitively solved at each time-step of the control algorithm. The global stability properties of the control scheme are proven through Lyapunov analysis. Finally, the performance of the nonlinear optimal control method is compared against a flatness-based control approach implemented in successive loops.

Due to the nonlinear and multivariable structure of the state-space model of spherical motors, the solution of the associated nonlinear control problem is a nontrivial task. In this paper, a novel nonlinear optimal (H-infinity) control approach is proposed for the dynamic model of permanent magnet synchronous spherical motors. The method is based on approximate linearization of the motor’s state-space model with the use of first-order Taylor series expansion and the computation of the associated Jacobian matrices. Furthermore, the paper has introduced a different solution to the nonlinear control problem of the permanent magnet synchronous spherical motor, which is based on flatness-based control implemented in successive loops.

The presented control approaches do not exhibit any limitations, but on the contrary, they have specific advantages. In comparison to global linearization-based control schemes (such as Lie-algebra-based control), they do not make use of complicated changes of state variables (diffeomorphisms) and transformations of the system's state-space description. The computed control inputs are applied directly to the initial nonlinear state-space model of the permanent magnet spherical motor without the intervention of inverse transformations and thus without coming against the risk of singularities.

The motion control problem of spherical motors is nontrivial because of the complicated nonlinear and multivariable dynamics of these electric machines. So far, there have been several attempts to apply nonlinear feedback control to permanent magnet-synchronous spherical motors. However, due to the model’s complexity, few results exist about the associated nonlinear optimal control problem. The proposed nonlinear control methods for permanent magnet synchronous spherical motors make more efficient, precise and reliable the use of such motors in robotics, electric traction and several automation systems.

The treated research topic is central for robotic and industrial automation. Permanent magnet synchronous spherical motors are suitable for several applications, such as actuation in robotics, traction in electric vehicles and use in several automation systems. The solution of the control problem for the nonlinear dynamic model of permanent magnet synchronous spherical motors has many industrial applications and therefore contributes to economic growth and development.

The proposed nonlinear optimal control method is novel compared to past attempts to solve the optimal control problem for nonlinear dynamical systems. Unlike past approaches, in the new nonlinear optimal control method, linearization is performed around a temporary operating point, which is defined by the present value of the system's state vector and by the last sampled value of the control inputs vector and not at points that belong to the desirable trajectory (setpoints). Besides, the Riccati equation which is used for computing the feedback gains of the controller is new, and so is the global stability proof for this control method. Compared to nonlinear model predictive control, which is a popular approach for treating the optimal control problem in industry, the new nonlinear optimal (H-infinity) control scheme is of proven global stability, and the convergence of its iterative search for the optimum does not depend on initial conditions and trials with multiple sets of controller parameters. It is also noteworthy that the nonlinear optimal control method is applicable to a wider class of dynamical systems than approaches based on the solution of state dependent Riccati equations (SDRE). The SDRE approaches can be applied only to dynamical systems which can be transformed into the linear parameter varying form. Besides, the nonlinear optimal control method performs better than nonlinear optimal control schemes, which use approximation of the solution of the Hamilton–Jacobi–Bellman equation by Galerkin series expansions. Furthermore, the second control method proposed in this paper, which is flatness-based control in successive loops, is also novel and demonstrates substantial contribution to nonlinear control for robotics and industrial automation.

The purpose of this study is to create an extended equivalent circuit model for a compound DC motor, consisting completely of electrical parameters and quantities.

The dynamic model of the compound DC motor is obtained by establishing the voltage equations for the armature and excitation circuit and the mechanical equation for the mechanical part. The mechanical parameters in the dynamic model are converted into electrical parameters with an electrical circuit proposed for the mechanical part. By combining the armature and excitation circuits with the electrical circuit created for the mechanical part, the extended equivalent circuit model of the compound DC motor is obtained. Because the proposed extended equivalent model is completely an electrical circuit, simulations can be made in the circuit simulation programme. Simulations of the proposed compound DC motor circuit were carried out, and the accuracy of the proposed circuit was verified by performing experimental studies with an existing compound motor.

When comparing speed and current profiles in experiments and simulations, it is seen that compound DC motor can be modelled with the proposed equivalent circuit including completely electrical elements in a simulation programme for the circuits. The results show that the proposed equivalent circuit satisfies the dynamic model of the compound motor.

In DC machine models, armature and excitation circuits are given as an electrical circuit, and mechanical part of the machine is modelled by only mechanical equations. The originality of this study is converting the dynamic model of an electrical machine consisting of electrical and mechanical equations into a completely electrical circuit. With the proposed method, the dynamic model of many motors can be converted into a completely electrical circuit. In this way, motors can be simulated as an electrical circuit in simulation programmes for the circuits, and the dynamic behaviour of motors can be obtained.

The purpose of this study is to propose an extended reliability method for an industrial motor drive by integrating the physics of failure (PoF).

Industrial motor drive systems (IMDS) are currently expected to perform beyond the desired operating conditions to meet the demand. The PoF of the subsystem affects its reliability under such harsh operating circumstances. It is crucial to estimate reliability by integrating PoF, which helps in understanding its impact and to develop a fault-tolerant design, particularly in such an integrated drive system. An integrated PoF extended reliability method for industrial drive system is proposed to address this issue. In research, the numerical failure rate of each component of industrial drive is obtained first with the help of the MIL-HDBK-217 military handbook. Furthermore, the mathematically deduced proposed approach is modeled in the GoldSim Monte Carlo reliability workbench.

From the results, for a 15% rise in integrated PoF, the reliability and availability of the entire IMDS dropped by 23%, resulting in an impact on mean time to failure (MTTF).

The integrated PoF of the motor and motor controller affects industrial drive reliability, which falls to 0.18 with the least MTTF (2.27 years); whose overall reliability of industrial drive drops to 0.06 if it is additionally integrated with communication protocol.

The study aims to show the influence of selected mechanical parameters of the rotor on the maximum speed and parameters of the electric motor.

A simplified mechanical analysis of the rotor of the electric motor was conducted, determining the safety factor of the motor. An analysis of the impact of key rotor parameters (significant from the mechanical strength perspective) on the electromagnetic parameters and the safety factor of the selected high-speed electric motor was carried out. The influence of changes in the rotor’s geometrical dimensions (centrifugal force) on the electromagnetic parameters of the electric motor was shown.

The study shows the impact of changes in selected rotor parameters on electromagnetic parameters and the safety factor of a high-speed electric motor (at its required operating point of 45,000 rpm). The dependence of the safety factor as a function of the maximum motor speed was determined for the proposed rotor modifications.

The proposed modifications can be used in larger drive systems. They have practically no impact on increasing the value of the motor’s moment of inertia (they do not degrade the dynamics of the motor’s operation).

It was proposed to use a new design coefficient which is in relation to the motor’s safety coefficient. It has been shown that a minimal modification of the motor rotor allows to increase its maximum speed by several dozen per cent (while maintaining the safety factor). It has also been shown that when operating at maximum speed within the safe range, the change in the geometrical dimensions of the rotor hardly influences the change in the value of the centrifugal force.

In this paper, an improved multiobjective particle swarm optimization (PSO) algorithm is proposed to optimize a three-phase, 12-slot, 19-pole, yokeless axial-field flux-switching permanent magnet (YASA-AFFSPM) motor.

Based on the structural characteristics of the YASA-AFFSPM, a mathematical model is established to calculate the main size of the YASA-AFFSPM motor. The split ratio, stator axial length, sandwiching pole angle, rotor pole angle, PM arc and number of conductors per slot are selected as optimization variables. Also, the efficiency, power factor, cogging torque and average torque are considered as the optimization objectives. The objectives are optimized by combining the improved multiobjective PSO algorithm with electromagnetic calculation.

Based on the proposed algorithm, the investigated motor is optimized. The on-load efficiency, power factor and average torque of the motor performance have increased by 0.87%, 3.13% and 10.39%, respectively. Moreover, the cogging torque and slot fill factor have undergone decreases of 8.57% and 3.34%, respectively. Finally, the effectiveness of the algorithm is verified using experiment results.

So far, no comprehensive report has been observed on the optimization of the YASA-AFFSPM motor using evolutionary algorithms and the study of the effect of the motor parameters. Therefore, in this paper, the authors decided to investigate the effect of YASA-AFFSPM motor parameters and improve motor performance with the improved PSO method.

The purpose of this paper is to take second-hand vehicles at judicial auctions in China as the primary research direction and to explore the impact of purchasing restriction policy and city size on the relationship between the appraisal price and transaction price of second-hand motor vehicles in the context of auto purchase restriction in China from a microscopic angle. It attempts to broaden the pricing ideas of judicial appraisal enterprises in providing appraisal prices of second-hand motor vehicles and to put forward suggestions for the optimization of appraisal prices and appraisal standards of judicial appraisal enterprises.

With the help of Python, this paper crawls 59,038 lines of valid data from three leading internet judicial auction platforms, namely “Ali Auction,” “China Beijing Equity Exchange” and “Gong Pai Wang,” as research samples. Besides, this paper forms a database containing judicial auction used car appraisal prices, transaction prices, motor vehicle purchase restrictions and whether the motor vehicle carries a license plate. By constructing a multiple regression model, the impact of automobile purchase restriction policy on the price of motor vehicles appraised by judicial appraisal enterprises is investigated.

With the help of the multivariate regression model, it found that under the same condition, the city where the auction took place implemented the automobile purchase restriction before the end of the auction. The court has specified that the buyer could directly obtain the license plate after the auction. The transaction price and the evaluation price ratio will be statistically larger, which proves that the license plate has an evident value in the transaction and is traded as subject matter by the residents, and consequently brings a higher premium to the price of automobile transaction in internet judicial auction. Meanwhile, the purchase restriction policy in the first-tier cities has resulted in a significant premium on automobile license plates, which is much higher than the automobile license plate premium level in non-first-tier cities under the same conditions.

Car ownership continues to rise with rapid economic development worldwide. Control the growth of car ownership, some countries and regions mainly restrict the issuance of motor vehicle license plates, which indirectly leads to vehicle license plate indicators becoming a scarce resource. National laws permit judicial auction as a means for the people's courts to settle creditors' claims in enforcement procedures of civil cases. In the judicial auction process, the People's Court introduces third-party evaluation enterprises to appraise, assess and audit the subject and obtain the appraisal price, which guides the bidding behavior of used car buyers and indirectly affects the transaction price of used cars.

As the only subject capable of assessing the value of used cars at judicial auctions, judicial appraisal enterprises have received widespread attention for their appraisal results. This paper researches this field by screening the factors affecting the ratio of motor vehicle transaction price to the appraised price. It also analyzes how the ratio of motor vehicle transaction price to appraised price is affected by motor vehicle purchase restrictions and the situation with license plates. This paper examines the existence of premiums for motor vehicle transactions with license plates, evaluates the purchase restrictions in cities with motor vehicle purchase restrictions and verifies that the premiums for motor vehicles at judicial auctions are affected by purchase restriction policies as well as the influence of city class. These studies have important implications for judicial appraisal enterprises to establish reasonable appraisal mechanisms and optimize appraisal prices. They also provide new ideas and methods for appraisal enterprises to assess the value of used vehicles at judicial auctions.

Recently, the convolutional neural network (ConvNet) has a wide application in the classification of motor imagery EEG signals. However, the low signal-to-noise electroencephalogram (EEG) signals are collected under the interference of noises. However, the conventional ConvNet model cannot directly solve this problem. This study aims to discuss the aforementioned issues.

To solve this problem, this paper adopted a novel residual shrinkage block (RSB) to construct the ConvNet model (RSBConvNet). During the feature extraction from EEG signals, the proposed RSBConvNet prevented the noise component in EEG signals, and improved the classification accuracy of motor imagery. In the construction of RSBConvNet, the author applied the soft thresholding strategy to prevent the non-related motor imagery features in EEG signals. The soft thresholding was inserted into the residual block (RB), and the suitable threshold for the current EEG signals distribution can be learned by minimizing the loss function. Therefore, during the feature extraction of motor imagery, the proposed RSBConvNet de-noised the EEG signals and improved the discriminative of classification features.

Comparative experiments and ablation studies were done on two public benchmark datasets. Compared with conventional ConvNet models, the proposed RSBConvNet model has obvious improvements in motor imagery classification accuracy and Kappa coefficient. Ablation studies have also shown the de-noised abilities of the RSBConvNet model. Moreover, different parameters and computational methods of the RSBConvNet model have been tested on the classification of motor imagery.

Based on the experimental results, the RSBConvNet constructed in this paper has an excellent recognition accuracy of MI-BCI, which can be used for further applications for the online MI-BCI.