Motor Position Research Articles

Variable universe fuzzy inference decided neural network feed-forward compensation for pid control of motor position

To improve the compensation accuracy of neural network decided by traditional fuzzy inference engine (FIE), a variable universe fuzzy inference (VUFI) decided neural network feed-forward compensation method for PID control of motor position is proposed. The method contains four control blocks: fuzzy inference (FI) block, variable universe (VU) block, neural network (NN) control block, and basic control block. The FI block adaptively decides the feed-forward control quantity of NN based on the error and the error change rate of control system to keep the dynamic performance of NN at the beginning of the control or the transient jump signal. The VU block constructs the contraction expansion (CE) factors from the absolute change quantity of NN weights and adjusts the universes of membership functions based on the learning situation of NN in real time so that high-precision inference can be achieved. The NN control block consists of a neural network identifier (NNI) and a neural network controller (NNC) that has the same structure with the NNI, and the NNI dynamically transfers network parameters to the NNC and outputs feed-forward compensation control quantity after learning the dynamic inverse model of DC servo motor online. The basic control block adopts a PID controller, which provides the learning samples for NN online. Experimental results proved that the proposed method can improve the inference accuracy of FIE without sacrificing steady-state accuracy, significantly reduce overshoot, and have better stability and dynamic performance.

Optimizing the Performance of DC Electric Smart Gate Motor using Ziegler Nichols Tuning Methods

One of the primary functions of the DC motor is exemplified in its utilization for the automated operation of irrigation gates, which involves a variety of DC motor models. The DC motor chosen was influenced by the particular operational requirements of the irrigation gate. The ability to support the massive weight of the gate as well as the extreme pressure produced by river water currents are among these requirements. The gate motor utilized functions at a sluggish pace of 20 revolutions per minute. The approach employed involves tuning utilizing the Ziegler-Nichols (ZN) method. The (Proportional-Integral-Derivative) PID controller is designed to precisely manage a DC motor's position, improving the motor's overall performance. The parameters were designed to be Kp = 17, Ti = 1, and Td = 0.1. The Simulation derived from the analysis of MATLAB step response data are side by side with algorithm utilized to certain the dynamic response of the closed-loop configuration. Essential Specifications such as rise time and settling time, both measured in seconds, will be integrated into the simulation outcomes. The utilization of the (ZN) based algorithm for adjusting the gain constants of the PID control system tends to yield superior outcomes in comparison to that under a unity feedback control system .The rising time was 12.6, but when performance improved, it dropped to 0.713. This improvement is also applicable to the settling time, which decreased from 22.4 to 1.13. The holistic evaluation demonstrates that the (ZN) method yields superior performance in contrast to a system employing unity feedback control.

Design and comparative analysis of motor position control based on traditional PID controller and fuzzy PID controller

Abstract. Using the PID algorithm to control electrical elements is a practical industrial application that have been studied for decades. This paper constructs a study on the difference between BLDC motor control and applying traditional PID and fuzzy PID. The simulation is done through Falstad and Matlab/Simulink. The math model of the transfer function of a BLDC motor is constructed by analyzing voltage and torque equilibrium. The parameters are determined approximately by looking up the datasheet of 2338 0006s. Then, Falstad establishes the operational amplifier model of the BLDC motor and traditional PID controller. The parameters are determined through both root locus and step response presented by Falstads scope. Next, Simulink is used to realize the fuzzy PID controller. The comparison study of traditional PID controller and fuzzy PID controller is constructed by looking at the scope and export data. After that, it is concluded that fuzzy PID controller performs better than traditional PID controller, it is robust and the influence of the input parameter is small. Finally, a lead lag compensator is applied to the whole system to improve the performance in the frequency domain.

Wireless PID-Based Control for a Single-Legged Rehabilitation Exoskeleton

The demand for remote rehabilitation is increasing, opening up convenient and effective home-based therapy for the sick and elderly. In this study, we use AnyBody simulations to analyze muscle activity and determine key parameters for designing a rehabilitation exoskeleton, as well as selecting the appropriate motor torque to assist patients during rehabilitation sessions. The exoskeleton was designed with a PID control mechanism for the precise management of motor positions and joint torques, and it operates in both automated and teleoperation modes. Hip and knee movements are monitored via smartphone-based IMU sensors, enabling real-time feedback. Bluetooth communication ensures seamless control during various training scenarios. Our study demonstrates that remotely controlled rehabilitation systems can be implemented effectively, offering vital support not only during global health crises such as pandemics but also in improving the accessibility of rehabilitation services in remote or underserved areas. This approach has the potential to transform the way physical therapy can be delivered, making it more accessible and adaptable to the needs of a larger patient population.

Research on trajectory tracking control method for agricultural robot permanent magnet synchronous motor servo system based on improved EMD threshold wavelet filtering

In agricultural robots, trajectory tracking can be affected by disturbances such as road slopes or bumps, leading to sudden changes in path curvature and reduced control accuracy. To address this issue, we propose a servo motor drive control method based on an improved Empirical Mode Decomposition (EMD) threshold. A mathematical model integrating the drive and control of the servo motor is established, and the driving performance of the servo motor is analyzed. By detecting the speed and position of the servo motor, the specified three-phase current values for motor drive control are derived. The actual current of the motor is collected using Hall current sensors and denoised with an improved EMD threshold wavelet filtering method. Within the servo motor drive control model, the system calculates the difference between the given three-phase current values and the actual motor current, and this difference is then used to adjust the motor control via an input linear amplifier, ensuring integrated drive control. Simulation and experimental results show that the proposed trajectory tracking control method for the agricultural robot’s permanent magnet synchronous motor servo system exhibits high control accuracy, strong anti-interference ability, and good performance, making it more suitable for practical applications.

Mathematical modelling of a flexible steering robot for vehicle trajectory control

Abstract This paper presents a mathematical modelling framework for a novel design of a steering robot to control vehicle trajectory. A systematic approach is adopted to capture the dynamics of a belt transmission mechanical system of the robot. The steering robot is powered by a Brushless Direct Current (BLDC) servo motor which employs a Field Oriented Current (FOC) control for motor position control. The system’s accuracy is demonstrated through simulations conducted using MATLAB SIMULINK, showcasing its capability to effectively track the reference steering angle.

Green fitness tech: A self-powered device for monitoring treadmill metrics

This research presents the development of a self-powered device designed to monitor treadmill metrics, including speed, distance covered, and calories burned, without requiring external power sources. The purpose of this study is to provide a cost-effective and sustainable alternative to conventional treadmill tracking systems that are often expensive and energy-dependent. The device utilizes an Arduino Nano microcontroller, a DC motor functioning as a generator, an TFT display, and an encoder to sense motor speed and position. Experiments demonstrated the system’s ability to accurately capture and display treadmill data in real-time, powered solely by the kinetic energy generated from treadmill operation. Significant findings highlight the system's capacity to reduce power consumption and operational costs while maintaining performance comparable to more expensive integrated tracking systems. The conclusions drawn from this study suggest that the proposed device not only promotes energy efficiency but also offers a practical, low-cost solution for enhancing the fitness experience, making it an attractive alternative for both personal and commercial treadmill applications.

Finite-time adaptive control based on output feedback and auxiliary variables for time-varying parameters and disturbances

This paper presents a finite-time adaptive control scheme tailored for a diverse range of unknown discrete-time systems, with a specific emphasis on BLDC motor position control. The approach leverages an auxiliary variable within an output feedback framework, adeptly circumventing the causality problem without necessitating the use of state or disturbance observers. Through the application of finite-time convergence techniques, the control law demonstrates robust performance even in the presence of time-varying parameters and compact disturbances. Additionally, the proposed time-varying estimator, based on multi-input fuzzy emulated network (MiFREN), offers a practical solution for handling unknown dynamics and disturbances, making it well-suited for real-world applications where accurate mathematical models may be unavailable. Extensive experimental validation is conducted to evaluate the efficacy of the proposed scheme, including comparisons with existing controllers. The results highlight the superiority of the approach, particularly in terms of tracking accuracy and robustness against disturbances.

Comprehensive Analysis and Development of Electric-Drive-Wheel with Idler Gear

This paper provides a comprehensive analysis of the electric-drive-wheel (EDW) with idler gear. From the perspective of automotive engineering, the EDW is an integrated execution unit that combines the function of powertrain and suspension. As a result, the research on EDW involves the intersection of multiple disciplines. The evolution of idler gear configuration requires some geometric constraints. Under this premise, the longitudinal and vertical dynamic characteristics of the system were studied, respectively. There are several factors that influence the system performance, such as the gear parameters, the position of the electric motor, and the suspension K&C value. The principles of each parameter on the output indicators were studied, with an optimized plan and simulation comparison to check the correctness of theory. In the end, the prototype was equipped with a test bench, covering a wide range of working operations to examine the expected performance of EDW design. The step response test of the EDW result showed a balanced performance in transmission smoothness and responsiveness agility.

Fixed-time disturbance observers-based fault-tolerant control for a class of nonlinear systems with prescribed performance

This paper explores the issue of fault-tolerant control for a class of nonlinear systems that encounter the actuator fault and external disturbances. The uncertainties arising from the actuator fault and external disturbances are treated as lumped uncertainties, and fixed-time observers are designed to observe them. The fault-tolerant controller with full-state constraints is then developed using the fixed-time backstepping method, integrating the Improved Prescribed Performance Function (IPPF) method and the Barrier Lyapunov Function (BLF) method. Additionally, command filters with error compensations are devised to resolve the ‘explosion of complexity’ problem during the controller derivation process. The parameters of the designed controller are optimised using the Northern Goshawk Optimization (NGO) algorithm to enhance nonlinear systems’ dynamic performance. Lastly, the validity and practicality of the proposed approach are verified through the comparative study of simulations and experiments, with the permanent magnet synchronous motor (PMSM) position system employed as a case study.

A Study on BLDC Motors Position Control for Reliability Improvement of EMU Door

A Study on BLDC Motors Position Control for Reliability Improvement of EMU Door

Self-Sensing Electromechanical System Integrated with the Embedded Displacement Sensor.

Conventionally, the electromechanical system requires the installation of auxiliary displacement sensors and only the amount on the drive part and motion end, which increases volume, cost, and measurement error in the system. This paper presents an integrated measurement method with a sensing head, which takes the equal division characteristics of mechanical structures as part of the sensor, thus, the so-called self-sensing system. Moreover, the displacement is measured by counting the time pulses. The sensing head is integrated with the entire electromechanical system, including the driving, transmitting, and moving parts. Thus, the integration of the sensing part is greatly improved. Taking the rotary table as a special example, and the sensing head embedded into each part of the system, displacement information is obtained by the common processing system and fused by the adaptive weighted average method. The results of the experiment show that the fusion precision of each component is higher than only the motor position information as the feedback. The proposed method is a practical self-sensing technology with significant volume reduction and intelligent control benefits in the industry, especially suitable for extremely small and narrow spaces.

A double axis synchronous control method for plunger pump based on fuzzy control

Abstract A fuzzy control method is proposed to address the problem of asynchronous motor position caused by uncertain factors, such as changes in motor load parameters and external disturbances in piston pumps driven by servo motors. We simulate three typical inputs and random load disturbances of dual-axis motor position synchronization using SIMULINK and apply this control strategy to high-precision piston pumps. The simulation and experimental results show that using the fuzzy control method can make the synchronous position tracking accuracy of the dual-axis motor better than 0.2%, and the synchronization error converges to zero after load disturbance. This method has certain anti-interference abilities.

A fractional‐order active disturbance rejection control for permanent magnet synchronous motor position servo system

AbstractThis paper introduces a novel control scheme for the permanent magnet synchronous motor (PMSM) position servo system using a fractional‐order active disturbance rejection control (ADRC) approach. Specifically, a first‐order ADRC is employed in the current loop, while a fractional‐order ADRC is designed for the position loop. In the latter, a linear extended state observer (LESO) is utilized to estimate and compensate for disturbances, while a fractional‐order proportional derivative (FO[PD]) feedback controller is employed to enhance the control performance. Moreover, a parameter tuning method is presented based on frequency domain specifications. This technique utilizes the flat phase constraint as a design constraint to ensure that the phase is flat near the gain crossover frequency, thus ensuring that the closed‐loop system remains robust to loop gain variations. Simulation and experimental results demonstrate that the proposed fractional‐order ADRC control scheme yields improved dynamic response, disturbance rejection, and robustness to loop gain variations.

Linear Parameter Varying Observer-Based Adaptive Dynamic Surface Sliding Mode Control for PMSM

This paper presents an adaptive dynamic surface sliding mode control technique to address the issue of system parameter changes in permanent magnet synchronous motor (PMSM) position servo systems. The proposed method involves adopting a linear parameter varying (LPV) observer-based parameter identification algorithm and adaptive control technique. Initially, a mathematical model of the PMSM is established, and the system parameters are divided into nominal and perturbation values. This allows for the reconstruction of the system model into a state space equation that incorporates the unknown perturbation parameters. To accurately estimate these unknown parameters, an LPV observer is designed based on the reconstructed model. Additionally, an adaptive dynamic surface sliding mode control technique is explored to achieve the desired tracking performance. Meanwhile, an exponential reaching law is introduced to expedite the dynamic behavior of the system and mitigate chattering. Finally, a suitable Lyapunov function is selected to ensure the overall stability of the system. The simulation results demonstrate the effectiveness of the parameter identification and control algorithm in achieving good identification and tracking control ability for PMSM systems.

UAV Control Based on Pattern Recognition in Aquaculture Application

This study proposes a drone application for the net cage aquaculture industry. A visual control structure is applied to the drone to obtain water-quality information surrounding the net cages. This study integrates a hexacopter, camera, onboard computer, flight control board, servo motor, and global positioning system’s auto-cruise function to adjust the drone position and control the servo motor retractable sensor to reach the desired target at an accurate location. In object identification, a deep learning neural network is used to identify the net cages. An onboard computer calculates the horizontal distance between the drone and the net cage. A “You only look once” (YOLO) neural network is used to detect the net cage images. Considering the hardware calculation speed and ability, an onboard computer is applied to process the flight control board and control the drone. In the mission, an aerial camera detects targets (net cage) and provides visual information to the drone for the target approaching control process. After executing the water-quality measurement, the drone will end the mission and return to the base. This study modifies the architecture of YOLO, compares it with the original model, and then finds a proper architecture for this mission. This study aims to assist cage aquaculture operators by using drones to measure water quality, which can reduce aquaculture’s labor costs.

Development of an Electronic Actuating Control Mechanism to Operate a Remotely Controlled 2-Wheel Rice Transplanter

Mechanical transplanters are adopted to reduce human drudgery in manual transplanting. Different types of manual and self-propelled paddy transplanters are commercially available. The human involvement is higher for operating a 2-wheel paddy transplanter operator has to walk 10-22 km behind the machine in a day in puddled field conditions under high temperature (38-440 C) and humid conditions (70-80% RH) in summer. Besides that, the operator has to walk within the 30 cm row width, which causes chafing between the thighs of an operator. To reduce the physical and physiological workload of an operator, an integrated control lever actuating mechanism with gear motors was developed to operate a 2-wheel paddy transplanter remotely. The inductive proximity sensors (SN04-N distance detector) were used to control the crank rotation position of the gear motors, motor drives (BTS 7960), and microcontroller (STM32F4) was used to connect with the auto-driver kit to control the motor mechanism and sensors. The auto-driver kit consisted of a variety of accessories, a physical programmable circuit board, and software, that runs on a PC, used to write and upload computer code as a simplified version of C++ to the physical board. Although the initial cost of the machine with the developed system has been increased up to 28%, but at the same time the effective field capacity has been increased from 0.158 ± 0.02 ha h-1 to 0.175 ± 0.04, resulting the area covered per day and per year has also increased up to 11%. With the development of remotely controlled system, the labor requirement has been reduced from 20± 1 to 13± 1 man h ha-1, which results reduction up to 40%. Moreover, the human fatigue in terms of physical and physiological work load involved while walking behind the transplanter has been eliminated at much extent. As compared to the existing walk-behind type paddy transplanter, the net profit with the developed system rise up to 18.50 to 26.61 % per year and a boost of 34.10 to 51.63% in benefit-cost ratio. Field evaluation of a developed remote-controlled system for a two-wheel rice transplanter would be viable, reducing fatigue among machine operators/farm workers while increasing work productivity and safety.

Fork-shaped neural interface with multichannel high spatial selectivity in the peripheral nerve of a rat

Objective. This study aims to develop and validate a sophisticated fork-shaped neural interface (FNI) designed for peripheral nerves, focusing on achieving high spatial resolution, functional selectivity, and improved charge storage capacities. The objective is to create a neurointerface capable of precise neuroanatomical analysis, neural signal recording, and stimulation. Approach. Our approach involves the design and implementation of the FNI, which integrates 32 multichannel working electrodes featuring enhanced charge storage capacities and low impedance. An insertion guide holder is incorporated to refine neuronal selectivity. The study employs meticulous electrode placement, bipolar electrical stimulation, and comprehensive analysis of induced neural responses to verify the FNI’s capabilities. Stability over an eight-week period is a crucial aspect, ensuring the reliability and durability of the neural interface. Main results. The FNI demonstrated remarkable efficacy in neuroanatomical analysis, exhibiting accurate positioning of motor nerves and successfully inducing various movements. Stable impedance values were maintained over the eight-week period, affirming the durability of the FNI. Additionally, the neural interface proved effective in recording sensory signals from different hind limb areas. The advanced charge storage capacities and low impedance contribute to the FNI’s robust performance, establishing its potential for prolonged use. Significance. This research represents a significant advancement in neural interface technology, offering a versatile tool with broad applications in neuroscience and neuroengineering. The FNI’s ability to capture both motor and sensory neural activity positions it as a comprehensive solution for neuroanatomical studies. Moreover, the precise neuromodulation potential of the FNI holds promise for applications in advanced bionic prosthetic control and therapeutic interventions. The study’s findings contribute to the evolving field of neuroengineering, paving the way for enhanced understanding and manipulation of peripheral neural functions.

Dynamic data model and robust controller with high‐frequency component reduction for unknown time‐varying discrete‐time systems

AbstractThis article presents an adaptive controller tailored for discrete‐time systems, emphasizing robustness and addressing time‐varying constraints. An equivalent model is constructed solely from input‐output data collected from an unknown plant. Performance optimization involves determining an appropriate learning rate guided by theoretical analysis. A control law is derived from the equivalent model and a proposed auxiliary controller, with parameter settings guided by theoretical analysis to achieve robust convergence in closed‐loop performance. Comprehensive experimental and comparative assessments confirm the superior performance of both the equivalent model and the proposed controller, especially in brushless DC motor position control. Moreover, compared to similar schemes, the proposed controller generates fewer high‐frequency control effort components. The investigation validates the approach's robustness in real‐world conditions, including uncertainties, disturbances, and common time‐varying constraints in practical plant scenarios.

Hybrid poisson-lagrange approach based on genetic algorithm for improving performance of induction motor

ABSTRACT This article presents an intelligent approach for controlling the mechanical parameters like speed, rotor position, and torque of an induction motor. These parameters have poor performance in terms of settling time, rise time, peak overshoot, and voltage deviation. To conquer such an issue, a hybrid Poisson-Lagrange (HPL) algorithm is proposed. This algorithm is a combination of Poisson distribution and the Lagrange multiplier. It begins with the mathematical modeling of a three-phase induction motor. A multi-objective function is framed with the help of the HPL algorithm. Further, unknown variables of the multi-objective function are trained with a genetic algorithm (GA), which is coined the HPL-GA algorithm. Similar types of control for mechanical parameters are also analyzed with fuzzy logic controllers (FLC) and field-oriented controllers (FOC). It is observed that peak overshoot, rise time, settling time of speed and torque, and sensitivity are found to be minimum with HPL-GA in comparison to FLC and FOC for various loading conditions. The voltage deviation is also found to be lowest with HPL-GA with respect to others for various loading conditions and thus such an approach makes the system highly efficient and robust.