Automatic Equipment Design of Intelligent Manufacturing Flexible Production Line Based on Industrial Motorized Spindle

Based on the previous research on the production line automation, this paper carries out further research and further design and development on the basis of the original production line automation equipment. In this paper, the overall design of the automatic production line is carried out, and the various systems in the automatic production line are optimized, and the backward instruments are eliminated, and then some more advanced and convenient instruments are applied. Then, the hardware and software of the automatic production line are studied respectively, and the human-computer interaction module and real-time main control circuit module are re developed, and the electric shaft is applied to the automatic production line. Finally, the fuzzy PID controller of the stepping motor is designed. The experiment shows that the fuzzy PID control scheme is better than the traditional PID control scheme. After the rationalization of the system, the quality robustness of proactive planning is improved obviously. Then, the temperature of motorized spindle was tested.

Research and Application of Precision Fertilizer Application Algorithm Based on PSO Optimized Fuzzy PID Control

In irrigation’s process and fertilizer application in production of agriculture, the accuracy of fertilizer application and water maintains at a relatively low level, which results in waste of soil slabbing and resources. In this research, a fuzzy PID algorithm based on PSO optimization is designed to control the fertilizer application process and irrigation of the fertilizer applicator. Firstly, a mathematical model of the fertilizer applicator is established according to the relevant modules and corresponding parameters. Based on the MATLAB/Simulink platform, the PID controller, the fuzzy PID controller and the controller proposed in this article are constructed respectively, which can be applied to the established transfer functions. The simulation outcomes demonstrate that the response time of the control algorithm proposed in this research is shortened to 30s, compared to fuzzy PID and PID, which is 62.5% and 50% shorter respectively, and the overshoot of the control algorithm in this article is nearly 0 of apart from the early oscillation. In order to verify the algorithm’s reliability in practical application, this research designs groups of different pressure for the accuracy control test, the test consequences illustrate that the fuzzy PID control based on PSO optimization has excellent control effect under each pressure. The control accuracy is concentrated at around 2%, while PID control maintains around 20% and fuzzy PID control distributed at 10%.The results show that the control algorithm proposed in this research enhances the irrigation accuracy in the practical application process.

Research on nonlinear model and fuzzy fractional order PIλDμ control of air suspension system

To improve the ride comfort of wheeled armored vehicles, air springs are used. To describe the vehicle motion more accurately, a nine-degree-of-freedom air suspension system for the whole vehicle was established, and its equations of motion were derived. Through theoretical analysis of the stiffness characteristics and forces on the air springs, the nonlinear restoring force was obtained as a cubic polynomial of the air spring displacement. The simulation results obtained by fitting the polynomial and radial basis function curves with MATLAB/Simulink software are consistent with the actual test results, thus verifying the correctness of the nonlinear air spring polynomial model. Finally, a fuzzy fractional order PIλDμ controller is designed and simulated for the vehicle-seat-body model in terms of wheel dynamic load, suspension dynamic deflection, body acceleration, and other indicators. The simulation results show that the fuzzy fractional order PIλDμ Proportion Integral Differential (PID) control strategy has better overall performance than the PID control strategy, fuzzy control strategy, and fuzzy PID control strategy.

Wheel Deflection Control of Agricultural Vehicles with Four-Wheel Independent Omnidirectional Steering

Due to the Fharsh working environment of wheeled agricultural vehicles in the field, it is difficult to ensure that all wheels make contact with the ground at the same time, which is easy to unequally distribute the yaw moments of each independent wheel. The commonly used vehicle lateral control methods are mostly controlled by coordinating the individual torque between different wheels. Obviously, this control method is not suitable for agricultural four-wheeled vehicles. The goal of this study was to provide a wheel steering angle control method that uses electric push rods as actuators that can cope with this problem. The design of a four-wheel steering controller generally adopts the linear PID control method, but the research object of this paper is difficult to establish an accurate and linear mathematical model due to the complex working environment. Therefore, fuzzy adjustment is added on the basis of PID control, which can meet the requirements of model difficulty and control accuracy at the same time. In order to verify the feasibility and rationality of the designed wheel steering mechanism, the model dynamics simulation based on ADAMS software and the response analysis of the electric linear actuator thrust were completed. Based on the kinematics model of the controlled object, the rotation angle of the actuator motor is used as the control target, the lateral deviation e and deviation variation ec are taken as input variables and the parameters KP, KI and KD are taken as output variables, thereby establishing a fuzzy PID controller. Then, this controller is constructed in the Matlab/ Simulink simulation environment to analyze the lateral deviation and response stability during the process of vehicle path tracking. From the verification results of the linear path walking test under the fuzzy PID control method, the maximum lateral deviation of vehicle chassis is 2.7 cm when the driving speed is set as 1 m/s, and the deviation adjustment stable time of the system is 0.15 s. It can be seen that the proposed steering control strategy has good response performance and effectively increases the steering stability.

Stability control of a vehicle with tire blowout based on active steering and differential braking

The vehicle with tire blowout will have dangerous working conditions such as yaw and tail flick, which will seriously endanger the safety of driving. A tire blowout model was established based on the UniTire model and the change of tire blowout mechanical characteristics. A Carsim/Simulink joint simulation platform was built to study the dynamic response of the vehicle after the front wheel tire blowout under curve driving. A combined control strategy of outer-loop trajectory control and inner-loop differential braking control based on sliding mode fuzzy control algorithms and fuzzy PID control algorithms was proposed to ensure that the vehicle can still follow the original trajectory stably after tire blowout. The results show that the tire blowout of the front wheel on the same side as the turning direction has a great influence on the instability and yaw of the vehicle, and the designed control strategy can effectively control the running track of the vehicle with tire blowout and the vehicle stability.

Control Performance Improvement of Hydro-Viscous Clutch Based on Fuzzy-PID Controller

As a rotational speed controller, a hydro-viscous clutch (HVC) is usually used in the constant pressure water supply system to maintain the needed water pressure constant. However, when the hydro-viscous clutch is working, it often suffers from the problem of output rotational speed fluctuation since the spool of proportional relief valve can easily get stuck. Consequently, water pressure will fluctuate too. A special pump control system of HVC was proposed based on the Fuzzy-PID controller for the purpose of reducing the fluctuation rate. The MATLAB simulation was carried out according to the mathematical model and the results show that the Fuzzy-PID control strategy is superior to traditional PID control. The corresponding experiment was performed and the result indicate that through applying the Fuzzy-PID controller based pump control system, the rotational output speed fluctuation of HVC can be inhibited from ±60π to ±6π rad/min, and the water pressure fluctuation is dropped from ±0.1 to ±0.002 MPa.

Design of and Research into a Multiple-Fuzzy PID Suspension Control System Based on Road Recognition

By analyzing the shortcomings of the traditional fuzzy PID(Abbreviation for Proportional, Integral and Differential) control system (FPID), a multiple fuzzy PID suspension control system based on road recognition (MFRR) is proposed. Compared with the traditional fuzzy PID control system, the multiple fuzzy control system can identify the road grade and take changes in road conditions into account. Based on changes in road conditions and the variable universe and secondary adjustment of the control parameters of the PID controller were carried out, which makes up for the disadvantage of having too many single input parameters in the traditional fuzzy PID control system. A two degree of freedom 1/4 vehicle model was established. Based on the suspension dynamic parameters, a road elevation algorithm was designed. Road grade recognition was carried out based on a BP neural network algorithm. The experimental results showed that the sprung mass acceleration (SMA) of the MFRR was much smaller than that of the passive suspension system (PS) and the FPID on single-bump and sinusoidal roads. The SMA, suspension dynamic deflection (SDD) and tire dynamic load (TDL) of the MFRR were significantly less than those of the other two systems on roads of each grade. Taking grade B road as an example, compared with the PS, the reductions in the SMA, SDD and TDL of the MFRR were 40.01%, 34.28% and 32.64%, respectively. The control system showed a good control performance.

A Temperature Control Method for Microaccelerometer Chips Based on Genetic Algorithm and Fuzzy PID Control

External temperature changes can detrimentally affect the properties of a microaccelerometer, especially for high-precision accelerometers. Temperature control is the fundamental method to reduce the thermal effect on microaccelerometer chips, although high-performance control has remained elusive using the conventional proportional-integral-derivative (PID) control method. This paper proposes a modified approach based on a genetic algorithm and fuzzy PID, which yields a profound improvement compared with the typical PID method. A sandwiched microaccelerometer chip with a measurement resistor and a heating resistor on the substrate serves as the hardware object, and the transfer function is identified by a self-built measurement system. The initial parameters of the modified PID are obtained through the genetic algorithm, whereas a fuzzy strategy is implemented to enable real-time adjustment. According to the simulation results, the proposed temperature control method has the advantages of a fast response, short settling time, small overshoot, small steady-state error, and strong robustness. It outperforms the normal PID method and previously reported counterparts. This design method as well as the approach can be of practical use and applied to chip-level package structures.

Research on Optimization of Fuzzy Network Control System Based on New Smith Predictive Time Delay Compensation

Aiming at the problem of network delay when the network control system transmits data, this paper adopts a new type of fuzzy control method of Smith predictor to compensate for the delay. In actual application scenarios, it is difficult to accurately match the Smith prediction model with the actual model. At the same time, the quantization factor and scale factor in the fuzzy PID controller are too dependent on experience, which makes the system’s adaptability to actual working conditions very poor. In this paper, genetic algorithm is used to optimizes fuzzy PID. According to the simulation results, when the Smith predictive model does not match the actual model, the steady-state performance and dynamic performance of the system under the fuzzy PID control optimized by the genetic algorithm are improved.