Single Neuron PID Research Articles

A single neuron PID control method based on Smith predictor for active balancing control of rotor with time-delay

Time-delay is a key problem affecting the performance of active balancing control. In this paper, a single neuron PID control method based on Smith predictor for active balancing control of rotor with time-delay is proposed. The dynamic model of rotor is built by simplification firstly in the paper. Based on this dynamic model, a compensation model of time-delay rotor system is constructed, and a single neuron PID control method is designed to reduce rotor vibration. The parameters of controller can be adjusted adaptively and active balancing control of rotor with time-delay is realized efficiently. The simulation shows that the proposed method is superior to the traditional Smith predictor method in control performance. Particularly the proposed method still has good effect on control of rotor with large time-delay.

A novel decoupling control method based on neural network for EV’s Driving PMSM

Permanent Magnet Synchronous Motor (PMSM) is often used in Electrified Vehicle (EV), while its Id (direct-axis current) and Iq (quadrature-axis current) are coupled. Traditional FOC (Field Oriented Control) method can’t get an accurate decoupling control of them when the motor speed changed with the vehicle’s driving condition, especially at high speed. This coupling issue leads an unstable torque output. And further, it deteriorates the NVH (noise vibration and harshness) performance of EV. This paper focuses on the decoupling solution and puts forward a new control strategy which combines the neural network control idea with FOC method. The novel method gives a neural network controller based on four single neuron PID controllers, which its function is to realize the d-q axis interacted adjustment and decoupling. Four single neurons PID controllers achieve the negative feedback control of Id to Ud (direct-axis voltage), Iq to Ud, Iq to Uq (quadrature-axis voltage), and Id to Uq respectively. Creatively, it takes PMSM speed as one of the neuron inputs to adjust the feedback weight of Id and Iq dynamically. A comparing simulation which is set up in the Simulink platform is given in this paper. Simulation results show that this method gives a good self-adaptiveness and decouples the influence of Id and Iq, as well as improve the motor control quality at high speed.

A Novel Fuzzy Self Tuning Technique of Single Neuron PID Controller for Brushless DC Motor

In this paper, a combination ANN/Fuzzy technique is used to design a Novel Fuzzy Single Neuron PID (NFSNPID) controller to achieve high performance brushless DC motor. The design steps include two parts. The first part uses the genetic algorithm (GA) to find the optimum parameters of Single Neuron PID (SNPID) controller, while the former deals with the design of fuzzy logic control to update the weights of SNPID control online. To demonstrate the designed controller effectiveness, a comparative study is made with between the NFSNPID, Conventional Fuzzy Single Neuron PID CFSNPID and SNPID. All controllers were used to drive, separately, the brushless DC motor against the sudden change of load and operating speed. The performed simulations show better results that motivate for further investigations.

Rotor dynamic balancing control method based on fuzzy auto-tuning single neuron PID

Rotor dynamic balancing control method based on fuzzy auto-tuning single neuron PID

基于单神经元PID控制器的四旋翼飞行器航迹控制

四旋翼飞行器从起点平滑旋进飞向终点，为控制飞行器稳定飞行，首先建立数学模型和运动力学模型，在此基础上设计双闭环单神经元PID控制器，并根据其动力学特性分析加速度对航迹跟踪，使飞行器飞行轨迹稳定。模型的建立与仿真实验都采用Matlab/Simulink平台，仿真结果证实此方法的可行性。 Quadrotor is from initial point flying to target point smoothly, keeping hovering motion. In order to control the stability of flying, the mathematical model and the dynamic model are established firstly. On the basis, the single neuron PID controller is designed. According to the characteristics of the dynamic model, acceleration is analyzed to track path so as to achieve the stability of flight. The establishment of model and the simulation experiment of model are based on Matlab/Simulink platform, and the result of simulation proves the practicability of this method.

Aeroservoelastic Pitch Control of Stall-Induced Flap/Lag Flutter of Wind Turbine Blade Section

The aim of this paper is to analyze aeroelastic stability, especially flutter suppression for aeroelastic instability. Effects of aeroservoelastic pitch control for flutter suppression on wind turbine blade section subjected to combined flap and lag motions are rarely studied. The work is dedicated to solving destructive flapwise and edgewise instability of stall-induced flutter of wind turbine blade by aeroservoelastic pitch control. The aeroelastic governing equations combine a flap/lag structural model and an unsteady nonlinear aerodynamic model. The nonlinear resulting equations are linearized by small perturbation about the equilibrium point. The instability characteristics of stall-induced flap/lag flutter are investigated. Pitch actuator is described by a second-order model. The aeroservoelastic control is analyzed by three types of optimal PID controllers, two types of fuzzy PID controllers, and neural network PID controllers. The fuzzy controllers are developed based on Sugeno model and intuition method with good results achieved. A single neuron PID control strategy with improved Hebb learning algorithm and a radial basic function neural network PID algorithm are applied and performed well in the range of extreme wind speeds.

Research on Loading System of Landing Gear Turning Test Based on Single Neuron PID Controller

This paper designs a single neuron PID controller for the loading system which can simulate the load in the process of landing gear turning. As artificial neurons have the adaptive, self-learning and more fault-tolerant characteristics, the controller based on single neuron PID can improve performance of loading system. To assess the effectiveness of controller, united simulation between Matlab/Simulink and AMESim was conducted. Obtained results show the proposed approach is satisfactory in fast response, small overshoot, high control accuracy, strong anti-interference ability and robustness when compared with traditional PID controller.

Stability Analysis of Bridge Crane Hoists

Hoist bridge crane swinging phenomenon often occurs during the operation, making the work more difficult, there is a certain danger. To solve this problem, anti-sway system for overhead cranes depth study to identify the main factors affecting heavy lifting swinging; proposed single neuron PID control strategy, while using MATLAB simulation used to verify the reliability of the method.

Dual Hydraulic Cylinders Synchronized Control Strategy

Through the double cylinder synchronous lifting system in-depth study presents a master-slave control method and the single neuron PID control strategy for controlling synchronization accuracy. Simulation results show that this method can achieve higher precision synchronization control.

Advanced emergency braking controller design for pedestrian protection oriented automotive collision avoidance system.

Automotive collision avoidance system, which aims to enhance the active safety of the vehicle, has become a hot research topic in recent years. However, most of the current systems ignore the active protection of pedestrian and other vulnerable groups in the transportation system. An advanced emergency braking control system is studied by taking into account the pedestrians and the vehicles. Three typical braking scenarios are defined and the safety situations are assessed by comparing the current distance between the host vehicle and the obstacle with the critical braking distance. To reflect the nonlinear time-varying characteristics and control effect of the longitudinal dynamics, the vehicle longitudinal dynamics model is established in CarSim. Then the braking controller with the structure of upper and lower layers is designed based on sliding mode control and the single neuron PID control when confronting deceleration or emergency braking conditions. Cosimulations utilizing CarSim and Simulink are finally carried out on a CarSim intelligent vehicle model to explore the effectiveness of the proposed controller. Results display that the designed controller has a good response in preventing colliding with the front vehicle or pedestrian.

Design of Single Neuron Adaptive PID Based on Quadratic Performance Index for Linear DC Motor

In this paper, a new control strategy is put forward, which is a single neuron PID control based on the quadratic performance index (QPI) learning algorithm. This single neuron PID control is self-learning and self-adjusting. Through the study of system performance, the weights of the neuron are adjusted by the QPI learning algorithm. The new strategy solves the contradiction between fast tracking performance and robustness in the positioning servo system. In addition, the mathematic model of voice coil motor is analyzed. In order to improve the fast response performance and disturbance suppression of the position loop, the Two-Degree-of-Freedom (TDOF) control is proposed. The simulation results show that disturbance rejection and dynamic performance of the VCM servo control system have been improved. Compared with the utilizing traditional PID system, this new control strategy has stronger self-adapting and robustness.

Velocity Tracking Control for Hydraulic Lifting System Based on Adaptive PSD Controller

The single neutron adaptive PID controller is applied to the angular velocity tracking control of the hydraulic lifting system. The angle velocity tracking control strategy of the lifting process is proposed, and the lifting angle velocity is designed based on the sine acceleration function, and the lifting angle velocity dynamic programming based on the real-time angle is proposed. The single neutron adaptive PID control method is studied, and in order to improve its performance, a method utilizing genetic algorithm to optimize these parameters of single neuron PID controller is presented. The control algorithm is applied to the large mechanical lifting process successfully, and the simulation results show that the control performance of the Adaptive PSD Controller is more effective.

Speed Control of PM Linear Synchronous Motor Based on Disturbance Observer and Single Neuron PID Controller

In permanent magnet linear synchronous motor (PMLSM) direct drive system, disturbance caused by load disturbance and perturbation of motor parameters would reduce the control precision in a certain extent. In order to restrict the disturbance in PMLSM direct drive system, a vector control (VC) strategy based on the disturbance observer (DOB) was proposed in this investigation. By using the observed disturbance of DOB to adjust the given value of current in speed loop, the compensation for a sudden disturbance is optimized and a high precision of PMLSM drive system is obtained. For the sake of improving the dynamic performance under unknown disturbances, the traditional PID controller in speed loop was replaced by a single neuron PID controller, which leads to a good adaptive capability and a better starting performance in a given speed. Finally, a higher steady precision and robustness would be realized.

Position Tracking System of Filling Machine Based on Compound Control Strategy

In view of the complexity and periodic motion of automatic filling machine, a novel compound control strategy based on single neuron PID model reference adaptive control and repetitive control is proposed. Diagonal recurrent neural network (DRNN) is used as on-line identifier of system for the single neuron PID controller to adjust its weights and PID parameters by self-learning and self-adapting. The dynamic state performance can be improved by adaptive PID controller based on DRNN on-line Identification and the steady state performance is improved by modified repetitive controller. Simulation results show that the control system has good ability of restraining disturbances and high position tracking precision and good robustness.

Based on Single Neuron PID Control of Vehicle Active Suspension System

Relative to the passive suspension, automotive active suspension car driving more ride comfort and stability, has a vital role to further improve the performance of the vehicle. For such a typically complex active suspension system research, the key issue is the selection of control strategies. The problems in the currently active suspension control strategy, the principle of a simple, effective, this paper, a single neuron PID control strategy used in the automotive active suspension system. The results show that compared with other control strategies, single neuron PID control strategy is reliable, has more advantages.

Single Neuron PID Control of a Turbofan Engine with Inlet Pressure Distortion

In this paper, a preliminary exploration and research about the adaptive control law has been carried on for the turbofan engine with inlet pressure distortion. First, a real-time dynamic component-based model which contains the inlet distortion effects was established. Then, we select a single neuron PID controller with adaptive parameters to complete the dynamic acceleration process. The results illustrate that the single neuron control algorithm with gain adaptive has strong robustness, self-learning and anti-jamming capability and the performance of a turbofan engine can be effectively improved by using the turbine expansion ratio adaptive control law.

Neuron Network Model-Based Control System Model

When the control object complicate conventional PID control accuracy will be significantly reduced. In recent years, with the gradual improvement of the people of artificial intelligence theory, analog neural networks has been rapid development, the emergence of a large number of excellent algorithm and the means of achieving, from single neuron PID algorithm and with gain control neuron system PID algorithm, two aspects discusses the process of adaptive neuron PID algorithm to achieve accuracy improved adaptive neuron system controller PID algorithm based on this analysis.

Reduction of Wind Turbine Torque Fluctuation Using Individual Pitch Control Based on Edgewise Moment

In order to eliminate the nonuniform force on the wind rotor caused by wind shear, tower shadow and turbulence and smooth the torque fluctuation of the wind turbine and the unbalanced loads on the wind turbine, a new individual pitch control strategy based on edgewise moment using single neuron PID controller is proposed. That is, the presented control strategy directly controls the blade edgewise moment generated by aerodynamic force. At the same time, to simulate the wind turbine loads, a dynamic model of three-bladed upwind horizontal axis wind turbine is built. Thus, the influence rules of the wind turbine torque fluctuation are deduced at length. Finally, to verify the effectiveness of the proposed individual pitch control system with the single neuron PID controller, the 2MW wind turbine generator system is modeled and simulated. The performance of the controller is illustrated its capability of not only reducing the wind turbine fluctuation, but also smoothing the fluctuation of the flapwise moment, the yaw moment and the tilt moment.

Single Neuron PID Control of Aircraft Deicing Fluids Rapid Heating System

Aircraft deicing fluids rapid heating system is widely used in aircraft ground deicing to ensure that the operation of flights can be safe and efficient. Aiming at the temperature turbulence problem of aircraft deicing system, this paper presents the single neuron PID control strategy which combine the advantage of conventional PID control with artificial neuron control. The aircraft deicing fluids rapid heating system and the scheme and working principle of the system is introduced. Simulation is executed on the basis of the mathematical model of aircraft deicing fluids rapid heating system, which is built in this paper, according to a number of data collected by experiments which are operated on the experimental platform of deicing fluids rapid heating system. The simulation results show that the single neuron PID control strategy perform effectively on the temperature turbulence problem of aircraft deicing fluids rapid heating system. Experiments are conducted to vertify the single neuron PID control strategy, the results of which show that the single neuron PID control strategy can achieve the request in practical application of the aircraft deicing fluids rapid heating system.

Stable Adaptive Control Strategy in Thrust System of Shield Machine

In this paper, a stable adaptive control strategy was proposed to improve the adaptive ability of thrust system of shield machine. The control strategy is composed of internal pressure feedback loop control and external advanced single neuron PID loop control. Its effectiveness was finally demonstrated by the result of AMESim-Matlab/Simulink Co-simulation.