Mechatronic control system for accelerator operation in the ginning machine chambe

Аннотация

This study proposes a mechatronic system Sodikjanov Jaxongirbek Shukhratbek oglu. (2025). Mechatronic control concept for real-time control of the accelerator’s speed system for accelerator operation in the ginning machine chambe. The within the gin’s working chamber. The system uses an American Journal of Engineering and Technology, 7(06), 153–156. https://doi.org/10.37547/tajet/Volume07Issue06-17 ultrasonic sensor to measure the density of cotton fractions and adjusts the motor’s rotational speed via a COPYRIGHT frequency converter, employing PID control based on © 2025 Original content from this work may be used under the terms motor current analysis. This approach reduces jamming of the creative commons attributes 4.0 License. and mechanical damage during cotton transport and enhances seed separation efficiency. Keywords: Ginning machine, accelerator, speed control, mechatronic system, PID control. Introduction: The ginning machine is the main equipment for carding and cleaning cotton fibers from seeds. The speed of rotation of the accelerator roller in the working chamber directly affects the density of cotton pieces, the degree of mechanical damage to the fibers, and the efficiency of seed separation [1]. Traditional control systems are based on open-loop storage, which increases the risk of jamming and seed damage when the density changes [2]. It has been found that the pulse dispersion is minimal in the 60° configuration [1,3], but the issue of automatic speed adjustment in real time has not been fully resolved. Objective: 1. Determining the relationship between cotton density and spinning speed [2]. 2. Optimization of rotation speed using closed- loop PID [3]. 3. Selection and integration of mechatronic system components. The American Journal of Engineering and Technology 153 https://www.theamericanjournals.com/index.php/tajet The American Journal of Engineering and Technology 4. Improve the efficiency of jamming and seed real-time module. Umarov [2] defined a density– separation. current–speed model; advantage – experimental regression, disadvantage – details of PID/inverter Nurmatov and Kadyrov [1] presented an analysis of integration. Smith et al. [3] described a mechatronic momentum dispersion and 60° configuration; block diagram and PID stabilization; advantage – advantage – theoretical model, disadvantage – lack of architecture, disadvantage – real-time examples. System architecture Blocks: Density sensor: ultrasonic, 0–5 V analog, 12-bit ADC. Current meter: 0–100 A, 4 mV/A, differential ADC. Control unit: ARM Cortex-M4, FreeRTOS, PID module, RS-485. VFD: 0–10 V / Modbus, 0–150 Hz, vector control. Electric motor: 5 kW, sensorless vector, 3000 rpm. Figure 1. Block diagram Theoretical basis. Linear relationship: PID control: The American Journal of Engineering and Technology 154 https://www.theamericanjournals.com/index.php/tajet The American Journal of Engineering and Technology Picture 2. it accumulates the error around the desired value The graph is interpreted as follows: Yellow line – error throughout the entire period - it appears on the graph signal e(t). Initially e=0, at t=1 s it jumps to e=1 when as a linear increase after t>1. the system reaches the set density. Orange line – PID control signal u(t) Effect of the D (derivative) term: at As a result, the signal u(t)u(t)u(t): Immediately adapts t=1 the error increases suddenly, so it appears as a very the system with a large initial impulse, Then steadily large pulse. Effect of the P (proportional) term: it compensates for the error over time, Bringing the covers the actual value of the error relatively quickly, accelerator speed to the desired level. which is noticeable in the figure as a rapid rise after the pulse. Effect of the I (integrative) term: as time passes, Picture 2. This graph clearly shows the visual response of the PID control. Mechatronic design and integration Implementation and testing Sensor interface: Ultrasonic sensor 2 kHz, Chebyshev Calibration: Density: 300–600 kg/m³, ±0.5; Current: 0– filter; shunt CT differential measurement. ADC 1 Msps. 100 A, ±0.5; VFD: 0.1; PID tuning Ziegler–Nichols:Kp= 0.6Kcr, Test protocol: 300–650 kg/m³, 10 points, 200 s Who= 2Kp/Tcr, Kd = KpTcr/8.Anti-windup and zonal observation. PID. VFD integration 0–10 V analog / Modbus, 0–150 Information:ρ(t),ω(t),u(t),e(t). Optimization: PID Hz, sensorless vector control, 200 ms dynamic parameters with Particle Swarm Optimization. response. RESULTS AND DISCUSSION The test results showed that the system maintained the The American Journal of Engineering and Technology 155 https://www.theamericanjournals.com/index.php/tajet The American Journal of Engineering and Technology density in the static mode with an absolute error of *IEEE Trans. on Mechatronics*, 2020, vol.25, no.4, ±1.8% in the range of 300-650 kg/m³, with a standard pp.567–578. deviation of 0.7% and a 95% confidence interval of ±1.4%. In dynamic tests, the step response of the PID algor