scholarly journals PID Control for the Vehicle Suspension Optimized by the PSO Algorithm

2018 ◽  
Author(s):  
Yongdong Xie ◽  
Jie Meng
Keyword(s):  
Pid Control ◽  
Pso Algorithm ◽  
Vehicle Suspension

scholarly journals Position Control of Ultrasonic Motor Using IMC-PID Combined with Tribes Type NN Algorithm

2011 ◽  
Vol 2-3 ◽  
pp. 12-17
Author(s):  
Sheng Lin Mu ◽  
Kanya Tanaka
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Pid Control ◽  
Position Control ◽  
Back Propagation ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Ultrasonic Motor ◽  
Swarm Optimization

In this paper, we propose a novel scheme of IMC-PID control combined with a tribes type neural network (NN) for the position control of ultrasonic motor (USM). In this method, the NN controller is employed for tuning the parameter in IMC-PID control. The weights of NN are designed to be updated by the tribes-particle swarm optimization (PSO) algorithm. This method makes it possible to compensate for the characteristic changes and nonlinearity of USM. The parameter-free tribes-PSO requires no information about the USM beforehand; hence its application overcomes the problem of Jacobian estimation in the conventional back propagation (BP) method of NN. The effectiveness of the proposed method is confirmed by experiments.

scholarly journals A new adaptive PSO-PID control strategy of hybrid energy storage system for electric vehicles

2020 ◽  
Vol 12 (9) ◽  
pp. 168781402095857
Author(s):  
Kanglong Ye ◽  
Peiqing Li
Keyword(s):  
Steady State ◽  
Control Strategy ◽  
Pid Control ◽  
Pid Controller ◽  
Storage System ◽  
Pso Algorithm ◽  
Energy Storage System ◽  
Hybrid Energy ◽  
Hybrid Energy Storage ◽  
Hybrid Energy Storage System

Research on optimization of control strategy for hybrid energy storage system (HESS) of the electric vehicle (EV), a new adaptive control strategy based on particle swarm optimization (PSO) algorithm is proposed in this paper. The steady-state power of the filtered power is used as the ideal output power of the battery. For the steady-state current output of the battery, the output power of the ultracapacitor is dynamically adjusted by the proportional-integral-derivative (PID) controller to construct a power difference control structure. The parameters of PID controller are optimized by PSO algorithm, and the target test is compared and analyzed based on MATLAB/Advisor. The research results show that the proposed PSO-PID control strategy can quickly eliminate the power deviation and achieve the approximate global optimization of the EV energy management strategy. Compared with the pre-optimized PID control strategy, the output current and power of the battery pack are smoother and the total power consumption is reduced by 3.8360% and 0.5125%, respectively. Then, the energy consumption parameters of PSO-PID are compared with the theoretical minimum energy consumption calculated by dynamic programming (DP) algorithm, and the deviation is less than 1% under both conditions.

Design of a New Suspension System Controlled by Fuzzy-PID with Wheelbase Preview

2012 ◽  
Vol 192 ◽  
pp. 106-110 ◽  
Author(s):  
Pak Kin Wong ◽  
Shao Jia Huang ◽  
Tao Xu ◽  
Hang Cheong Wong ◽  
Zheng Chao Xie
Keyword(s):  
Pid Control ◽  
Control Algorithm ◽  
Fuzzy Controller ◽  
Low Noise ◽  
Suspension System ◽  
Vehicle Suspension ◽  
Fuzzy Pid ◽  
Front Suspension ◽  
Fuzzy Pid Controller ◽  
Current State

This paper studies a new active vehicle suspension controlled by Fuzzy-PID controller with wheel base preview. By this new algorithm, the fuzzy controller controls the parameters of the PID in time .Then the wheelbase preview is integrated to ensure the future road information is combined with the current state of the vehicle effectively. A sensor is placed on the front suspension collects and feeds forward the preview information as an input to the rear suspension system . MATLAB simulations show that using such control strategy can obtain a low noise and better robustness performance than the traditional PID control algorithm.

Application Research of PID Control Optimized by GBLB-PSO in HVAC System

2011 ◽  
Vol 219-220 ◽  
pp. 1325-1328 ◽  
Author(s):  
Qi Liu ◽  
Yuan Dong Du
Keyword(s):  
Pid Control ◽  
Hot Spot ◽  
Pso Algorithm ◽  
Research Field ◽  
Good Effect ◽  
Hvac System ◽  
Optimization Strategy ◽  
Control Effect ◽  
Method Performance ◽  
Parameter Values

Parameter optimization of PID control is always a hot spot in the research field of control, and control effect of PID depends on the parameter values: proportion, integral and differentiation. This paper puts forward a global best and local best PSO algorithm, which is an optimization strategy of PID, the result of this method making the system have small overshoot and short adjusting time. The optimization scheme of this paper will be used in the control of HVAC system. through simulation, it is shown that there is good effect, such as non-overshoot and short adjusting time. Compared with the traditional method, performance of this algorithm is well improved and optimized objective function is decreasing.

Study on Fuzzy-PID control for SM-CVT speed ratio based on PSO algorithm

2017 ◽  
Vol 17 (3) ◽  
pp. 431-442 ◽  
Author(s):  
Lan-Chun Zhang ◽  
Sen Liu ◽  
Hong-Sheng Shen ◽  
Shan-Feng Wang ◽  
Shuo Zhang
Keyword(s):  
Pid Control ◽  
Pso Algorithm ◽  
Speed Ratio ◽  
Fuzzy Pid ◽  
Fuzzy Pid Control

scholarly journals PSO-RBF Neural Network PID Control Algorithm of Electric Gas Pressure Regulator

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Yuanchang Zhong ◽  
Xu Huang ◽  
Pu Meng ◽  
Fachuan Li
Keyword(s):  
Neural Network ◽  
Response Time ◽  
Pid Control ◽  
Control Algorithm ◽  
Rbf Neural Network ◽  
Pso Algorithm ◽  
Gas Pressure ◽  
Tracking Performance ◽  
Step Response ◽  
Pressure Regulator

The current electric gas pressure regulator often adopts the conventional PID control algorithm to take drive control of the core part (micromotor) of electric gas pressure regulator. In order to further improve tracking performance and to shorten response time, this paper presents an improved PID intelligent control algorithm which applies to the electric gas pressure regulator. The algorithm uses the improved RBF neural network based on PSO algorithm to make online adjustment on PID parameters. Theoretical analysis and simulation result show that the algorithm shortens the step response time and improves tracking performance.

Study on Control Technology of Active Suspension Based on ADAMS and MATLAB

2014 ◽  
Vol 602-605 ◽  
pp. 1372-1377 ◽  
Author(s):  
Yi Zhang ◽  
Li Li Sun
Keyword(s):  
Pid Control ◽  
Ride Comfort ◽  
Active Suspension ◽  
Vertical Vibration ◽  
Vehicle Suspension ◽  
Control Effect ◽  
Vibration Acceleration ◽  
Suspension Control ◽  
Suspension Model ◽  
Control Principle

In order to improve the control effect of vehicle suspension, the simplified Seven-DOF active suspension model was created in ADAMS/View by applying the dynamics theory, and classical PID control principle was utilized to design an active suspension controller for vehicle. The vehicle model was imported into the PID controller established in MATLAB as a module to create a vehicle active suspension control model. According to the simulation results, compared with passive suspension, the PID control of active suspension can control effectively the vertical vibration acceleration (VVA),roll and pitch acceleration (RAA&PAA) of body ,which improved vehicle ride comfort performance.

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