Control Of Linear Motor Research Articles

Modeling and Control of a Novel Permanent Magnet Linear Motor

This paper concerns on modeling and linear controller design of a novel permanent magnet DC linear motor. The paper first introduces the novelty of designed and implemented motor, and then presents its dynamical model. By using the developed model, a linear controller has been designed and the results of the numerical simulation of the designed controller have been presented to show the effectiveness and stability of the designed controller. Some future prospects for the designed and simulated controller have also been provided.

Speed-sensorless model predictive thrust control of linear induction motor based on improved extended Kalman filter observer

This paper puts forth a strategy for sensorless finite-set model predictive thrust control (FS-MPTC) of linear induction motors (LIM) based on an improved extended Kalman filter (IEKF) observer. It is designed to address the shortcomings of existing LIM sensorless vector control methodologies, namely their lack of robustness and ineffective decoupling due to dynamic end effects. First, a fifth-order nonlinear discrete model of LIM is established in order to meet the requirements of sensorless FS-MPTC. An extended Kalman filter (EKF) observer based on the state-space equations is derived with the objective of achieving synchronous online observation of current, flux linkage and speed. In order to address the limitations of the traditional EKF, which employs a fixed system noise covariance matrix, this paper introduces an adaptive adjustment mechanism based on the difference between the theoretical predictions and actual measurements of the observer. This mechanism allows for the covariance matrix Q to be adjusted in real-time. The enhanced IEKF observer is then put forth. Simulation and experimental outcomes illustrate that, in comparison to the conventional EKF, the IEKF is capable of effectively adapting to internal noise fluctuations resulting from disparate operational conditions, system disturbances and LIM dynamic end effects. It is able to accurately estimate speed and flux linkage, operating stably under both high and low-speed conditions with commendable dynamic and robust performance.

The direct thrust control of permanent magnet linear synchronous motor based on dynamic candidate vector set

The direct thrust control of permanent magnet linear synchronous motor based on dynamic candidate vector set

Improved Interconnected MRAS Parameter Identification for Speed Sensorless Control of Linear Induction Motor

After eliminating the speed sensor in the linear induction motor (LIM) high-performance closed-loop control system, the speed feedback information is missing in the speed closed loop. The accuracy of speed observation results is affected by changes in magnetizing inductance and primary resistance. This effect can cause significant oscillations in the results of the speed sensorless control system, preventing them from converging. An enhanced model reference adaptive system (MRAS) multi-parameter parallel identification methodology based on the interconnected second-order super-twisting algorithm (SOSTA) is proposed. To enhance the system’s dynamic performance, we designed an improvement to the MRAS observer based on the SOSTA, with a focus on the LIM state-space equation that considers dynamic edge-end effects. The impact of parameter alterations on the LIM system is examined. To improve speed observation accuracy and system stability, a two-parameter MRAS identification model was created. The Popov hyperstability principle was used to formulate control laws for these two parameters, ultimately enabling the identification of these two parameters. The identified values were fed back to the speed observation and control system, which reduces the coupling of these two parameters and speed. Simulation and hardware-in-the-loop experiments demonstrate that the observation system estimates speed accurately when these two parameters undergo abrupt changes within the rated speed range, enhancing the precision and robustness of the speed sensorless control system.

Model-Free Control of Permanent Magnet Synchronous Linear Motor Based on Ultra-local Model

Model-Free Control of Permanent Magnet Synchronous Linear Motor Based on Ultra-local Model

Adaptive Fault-Tolerant Control of Linear Motors Under Sensor and Actuator Faults

Adaptive Fault-Tolerant Control of Linear Motors Under Sensor and Actuator Faults

Optimal tuning of PID controller for V/f control of linear induction motor using artificial biological intelligence

In order to improve the performance of a Proportional-Integral-Derivative (PID) controller used in the control of Linear Induction Motor (LIM) V/f speed, this research presents a bio-inspired meta-heuristic soft computing approach. A PID controller specifically designed for the LIM system is described in detail, with a focus on how to optimize the controller using an evolutionary strategy that makes use of the Nutcracker Optimizer. Settling time, rise time, maximum overshoot, and ITAE (Integral-Time Absolute Error) are examples of transient response specifications that are achieved in MATLAB/Simulink by using a step input to the LIM and the optimal set of PID parameters chosen from the optimization. In order to determine the most effective technique for obtaining the optimum response in LIM, these outcomes are then contrasted with outcomes from other soft computing techniques.

Robust feature design for early detection of ball screw preload loss

The ball screw is a critical device for precision linear motion control that has widespread applications in industrial robots, computer numerical control (CNC) machines, and high-precision leveling systems, among others. Because high-precision positioning is ensured by the addition of a preload to the ball screw system, it is crucial to detect and monitor the loss of preload at the earliest possible stage of degradation. The degradation process of the ball screw can be characterized in two stages: the initial reduction of preload without backlash, followed by a loss of preload with an emergence of backlash. To explore the change of the ball screw dynamics caused by degradation, a novel fixed cycle feature test (FCFT) is implemented in combination with multi-level mass experiments and a run-to-failure (RTF) test. The relationships of the ball screw dynamics with preload, worktable mass, and axis position are investigated, with a focus on the axial natural frequency as an indicator of preload loss. Experimental results validate the axial natural frequency’s role as a reliable early detector of preload loss for interventive use in a prognostic system.

Locomotion Control of a Bipedal Wheeled Robot Using Virtual Model Control and Linear Quadratic Regulator Techniques

This paper aims to develop a balance control technique and investigates its impact on the stability and disturbance rejection capability of a bipedal wheeled robot. The bipedal wheeled robot is equivalent to a wheeled inverted pendulum nonlinear model with a legs-airframe centroid variable rod. The nonlinear model is linearized and decoupled into two subsystems: straight-line control using linear-quadratic regulator (LQR) for balance and speed, and steering control employing proportional integral derivative (PID). Height control adjusts the virtual force with PID-Feedforward, while hip torque is determined by virtual model control (VMC). MATLAB simulation confirms effective control of height, linear motion, and steering, with decoupling enhancing steering performance.

Double-Sided Single-Set Winding Coordinated Active Levitation Control of Linear Permanent Magnet Motor Considering Mover Air-Gap Offset

Double-Sided Single-Set Winding Coordinated Active Levitation Control of Linear Permanent Magnet Motor Considering Mover Air-Gap Offset

Current decoupling control of linear synchronous motor based on improved extended state observer

Current decoupling control of linear synchronous motor based on improved extended state observer

Online Optimization-Based Time-Optimal Adaptive Robust Control of Linear Motors With Input and State Constraints

Online Optimization-Based Time-Optimal Adaptive Robust Control of Linear Motors With Input and State Constraints

Compensation adaptive robust control for a linear motor–driven stage system with state and input constraints based on gated recurrent unit architecture

Motion control of mechatronic systems with uncertainties and physical constraints, while ensuring robustness and achieving better performance, such as high tracking accuracy and fast response, has always been a hot topic. However, the most current related works only focus on how to guarantee system stability under constraints, and few consider comprehensive performance. This paper investigates gated recurrent unit (GRU)-based compensation adaptive robust control (ARC) for uncertain linear motor–driven stage system with state and input constraints. To achieve rapid and precise motion control, a dual-loop control structure is employed, where GRU and ARC are the outer loop and the inner loop, respectively. First, the ARC control law is used to deal with the parameters uncertainty and external disturbances in the system, which further improves the tracking accuracy. A GRU neural network is then constructed and capable of implementing precise prediction ahead of the actual system output. Through choosing suitable loss function and training model, it can effectively minimize prediction error under state and input constraints. Comparative experiment results demonstrate the superiority and validity of the proposed scheme on the basis of GRU and ARC.

Fixed-time disturbance observer-based funnel control for controllable excitation linear synchronous motor with state constraints

This paper proposes a fixed-time disturbance observer-based funnel control method for a controllable excitation linear synchronous motor (CELSM) with state constraints and disturbances. This approach simultaneously resolves the issues of prescribed tracking performance and state constraints, taking into account disturbances. We employ a fixed-time disturbance observer (DO) to estimate both matched and mismatched disturbances. Additionally, we design a modified funnel variable to confine the tracking error within a prescribed region and the singularity problem is avoided. An integral barrier Lyapunov function (iBLF) is utilized to directly tackle state constraints. Furthermore, we employ a fixed-time differentiator (FTD) to address the “explosion of complexity” issue in backstepping control. Theoretical analysis demonstrates that the tracking error asymptotically converges to the origin within a prescribed region while satisfying state constraints. Finally, simulation results demonstrate the efficacy of the proposed method.

Loss Minimization Model Predictive Current Control for Linear Induction Motors

To further improve the operating efficiency of linear induction motor, this paper proposes a loss minimization model predictive current control method based on governable loss online calculation. First, the method derives the equivalent circuit of linear induction motor containing independent iron loss branches, and establishes the loss model containing iron loss. Second, the expressions of stator d‐axis current with governable losses and secondary flux amplitude are derived from theoretical analysis, and then a given value of stator d‐axis current is derived when the governable losses are minimal. Then, a chain observer is designed to observe the secondary flux and calculate the governable loss and stator d‐axis current under the current operation. Finally, a method is proposed to introduce the stator d‐axis current observation results into the model predictive current control, which ensures the loss suppression effect and improves the dynamic performance at the same time. In addition, this article also compares the loss minimization controllers with or without iron loss. The simulation results are consistent with the theory, and the optimal control of the secondary flux of the linear induction motor can be successfully realized, thus effectively reducing the energy loss of the motor in the dynamic operation process. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Double-Closed-Loop Model Predictive Control Based on a Linear Induction Motor

The conventional PI control, which has been extensively employed in the high-performance control of linear induction motors, has the benefit of a straightforward concept, quick dynamic response, and simple control. Nevertheless, the linear induction motor is more susceptible to outside disturbances and mismatched parameters since it is a highly linked, high-order, nonlinear, time-varying, complex system. Because of this, this paper proposes a double-closed-loop control of the linear induction motor, whose speed and current loops are the model-predictive speed controller and the model-predictive current controller, respectively, to solve the problems of speed overshoot and oscillation when the linear induction motor is operated under PI control. The findings demonstrate the benefits of the suggested control strategy, which significantly enhances the linear induction motor’s speed control performance. These benefits include improved dynamic performance, limited overshooting, and robust resistance to load disturbance.

Control of linear motors with gas lubrication of the coordinate measurement system

The problem of the influence of non-Gaussian disturbance on the control system during the measurement of coordinate measuring system of mechanical quantities of objects with a complex spatial surface is described in the article. It is shown that the accuracy of the filtering of the random process of measuring mechanical quantities is significantly affected if to take into account the disturbing factors with non-Gaussian probability distribution density.

Beta Maximum Power Extraction Operation-Based Model Predictive Current Control for Linear Induction Motors

There is an increasing interest in achieving global climate change mitigation targets that target environmental protection. Therefore, electric vehicles (as linear metros) were developed to avoid greenhouse gas emissions, which negatively impact the climate. Hence, this paper proposes a finite set-model predictive-based current control (FS-MPCC) strategy of linear induction motor (LIM) for linear metro drives fed by solar cells with a beta maximum power extraction (B-MPE) control approach to achieve lower thrust ripples and eliminate a selection of weighting factors, the main limitation of conventional model predictive-based thrust control (which can be time consuming and challenging). The B-MPE control approach ensures that the solar cells operate at their maximum power output, maximizing the energy harvested from the sun. Considering a single cost function of primary current errors between the predicted values and their references in αβ-axes, the proposed method eliminates the need for weighting factor selection, thus simplifying the control process. A comparison between the conventional and the presented control method is conducted using MATLAB/Simulink under different scenarios. Comprehensive simulation results of the presented system on a 3 kW LIM prototype revealed that the introduced system based on FS-MPCC surpasses the conventional technique, resulting in a maximum power extraction from solar cells and a suppression of the thrust ripples as well as an avoidance of weighting factor tuning, leading to fewer computational steps.

Sliding-Mode Control of Linear Induction Motor Based on Exponential Reaching Law

As a strongly coupled, multivariable, high-order nonlinear, time-varying complex system, a linear induction motor is susceptible to outside disturbances and mismatched parameters. Because of its straightforward control mechanism and fixed PI value, the traditional PI regulator is frequently utilized in linear induction motor speed control systems. However, because of these limitations, it frequently falls short of meeting the high-performance control needs of the motor in complex operating conditions. In order to solve the contradiction between the sliding-mode motion reaching time and vibration, the sliding-mode variable structure control is applied to the linear induction motor speed control system in this paper. The sliding-mode controller is designed using the exponential reaching law method, and the designed system is analyzed through simulation. The findings demonstrate how the controller enhances the system’s robustness and disturbance resistance by bringing about the advantages of rapidity, stability, no overshooting, and a strong resistance to load disturbance.

Integrated Control of Linear Motors for Coordination and Synchronization

Integrated Control of Linear Motors for Coordination and Synchronization