This study describes the direct torque control-DTC approach, based on the Sliding Mode Control (SMC) technology with chattering reduction, for reducing the torque ripple of the Switched Reluctance Motor (SRM). The SRM torque control loop has been given the SMC treatment to account for the low-frequency fluctuations in the torque output. To maintain a consistent motor speed, the sliding mode controller modifies the value of the reference current. The findings demonstrate that the constant sliding mode controller is superior to PI controllers at lowering the motor's torque ripple, compensating for its nonlinear torque characteristics, and rendering the drive insensitive to parameter changes. MATLAB/SIMULINK simulation has been used to show how well this SMC performs. The performance of the proposed SMC method has been demonstrated by simulation in MATLAB/SIMULINK with a three-phase 8/6 pole, and a 2kW SRM.

This study provides simulation and experimental results on techniques for avoiding static and dynamic obstacles using a deep Q-learning (DQL) reinforcement learning algorithm for a two-wheel mobile robot with independent control. This method integrates the Q-learning (QL) algorithm with a neural network, where the neural networks in the DQL algorithm act as approximators for the Q matrix table for each pair (state–action). The effectiveness of the proposed solution was confirmed through simulations, programming, and practical experimentation. A comparison was drawn between the DQL algorithm and the QL algorithm. Initially, the mobile robot was connected to the control script using the Robot Operating System (ROS). The mobile robot was programmed in Python within the ROS operating system, and the DQL controller was programmed in Gazebo software. The mobile robot underwent testing in a workshop with various experimental scenarios considered. The DQL controller displayed improvements in computation time, convergence time, trajectory planning accuracy, and obstacle avoidance. As a result, the DQL controller surpassed the QL algorithm in terms of performance.

In this article, the research team systematically developed a method to model the kinematics and dynamics of a 3-wheeled robot subjected to external disturbances and sideways wheel sliding. These models will be used to design control laws that compensate for wheel slippage, model uncertainties, and external disturbances. These control algorithms were developed based on dynamic surface control (DSC). An adaptive trajectory tracking DSC algorithm using a fuzzy logic system (AFDSC) and a radial neural network (RBFNN) with a fuzzy logic system were used to overcome the disadvantages of DSC and expand the application domain for non-holonomic wheeled mobile robots with lateral slip (WMR). However, this adaptive fuzzy neural network dynamic surface control (AFNNDSC) adaptive controller ensures the closed system is stable, follows the preset trajectory in the presence of wheel slippage model uncertainty, and is affected by significant amplitude disturbances. The stability and convergence of the closed-loop system are guaranteed based on the Lyapunov analysis. The AFNNDSC adaptive controller is evaluated by simulation on the Matlab/simulink software R2022b and in a steady state. The maximum position error on the right wheel and left wheel is 0.000572 (m) and 0.000523 (m), and the angular velocity tracking error in the right and left wheels of the control method is 0.000394 (rad/s). The experimental results show the theoretical analysis’ correctness, the proposed controller’s effectiveness, and the possibility of practical applications. Orbits are set as two periodic functions of period T as follows.

The actual and anticipated battlefield creates a model capable of accurately estimating the lifetime of lithium-ion batteries used in electric cars. This inquiry uses a technique known as supervised machine learning, more particularly linear regression. In lithium-ion batteries, modeling temperature-dependent per-cells is the basis for capacity calculation. When a sufficient quantity of test data is accessible, a linear regression learning method will be utilized to train this model, ensuring a positive outcome in forecasting battery capacity. The conclusions drawn in the article are derived from the attributes of the initial one hundred charging and discharging cycles of the battery, enabling the determination of its remaining power. This determination facilitates the swift identification of battery manufacturing procedures and empowers consumers to detect flawed batteries when signs of performance degradation and reduced longevity are observed. MATLAB simulations have demonstrated the accuracy of the projected results, exhibiting a margin of error of approximately 9.98%. With its capacity to provide a reliable and precise means of estimating battery lifespan, the developed model holds the potential to revolutionize the electric vehicle industry.

Tension Sensorless Control For Web Transport Systems

This work offers the sliding mode control (SMC) based control scheme for a railway traction transmission system fed by a five-level T-type inverter. This nonlinear control approach is created for the speed and torque loop control of the permanent magnet synchronous motor (PMSM) railway traction drive system supplied by a multi-level inverter. The article also includes a mathematical model of a PMSM motor and torque load to design controllers. The paper expressed the vector voltage modulation design incorporating a five-level T-Type inverter. The research proposes a control scheme for the railway traction drive system that enhances transmission quality by lowering the torque ripper and stator current harmonic distortion and extending converter life. Through MATLAB simulation, the study findings are validated.