PID Simulation Research Articles

EHA System Integration Design Analysis and Position Tracking Performance by PID Controller

Electro-hydraulic actuation is now an emerging and well established technology and currently applicable in almost all fields but most important application of electro-hydrostatic actuator (EHA) is aerospace industry. This paper will discuss the selection and designing of major sub-assemblies and their integration to form an EHA unit. A complete, compact, and high performance EHA will be designed in different sections of paper and dynamic analysis of major assemblies is carried out to meet the designed requirements. In last section position tracking performance of the EHA is analysed by using a conventional PID Controller and simulation results are discussed.

Fuzzy PID Control and Simulation of Piezoelectric Ceramic Nanomanipulation System

With widely application of nanomanipulation, the design requirements of nanomanipulation control system is higher and higher. To improve the control precision of nano-components, it is important to do research on closed-loop control method for nanomanipulation. The piezoelectric ceramic actuator has the inherent characteristic of hysteresis, creep and nonlinear. Attocube is adopted as piezoelectric ceramic actuator in this system. Aiming at the designed nanomanipulation system based on Attocube, the closed-loop nanomanipulation control method based on fuzzy PID is studied. Based on the simplified modeling of piezoelectric devices and its displacement model, the controller of nanomanipulation system is designed and its simulation based on Matlab is conducted. Simulational results prove the effectiveness of the method.

Based on Neural Network PID Controller Design and Simulation

The technologic of PID control is very conventional. There is an extensive application in many fields at present. The PID controller is simple in structure, strong in robustness, and can be understood easily. Then neural networks have great capability in solving complex mathematical problems since they have been proven to approximate any continuous function as accurately as possible. Hence, it has received considerable attention in the field of process control. Due to the complication of modern industrial process and the increase of nonlinearity, time-varying and uncertainty of the practical production processes, the conventional PID controller can no longer meet our requirement. This paper introduces the theoretical foundation of the BP neural network and studying algorithm of the neural network briefly, and designs the PID temperature control system and simulation model based on BP neural network.

Based on Orthogonal Experiment Method PID Simulation Control of Pumping Unit

To study control accuracy of pumping unit system and improve dynamic performance, after ensure every influencing factor, carried out orthogonal test. Permanent magnet synchronous motor of direct torque control was used to model and simulate with experiment parameters based on Matlab/Simulink software, analyzed optimal PID parameter, and can avoid mass and complex test that determine PID parameter combination. The simulation results indicate that it can increase control accuracy clearly and decrease response time, and can reach optimum system performance. Results of simulation analysis have some certain practical value for pumping unit driven by permanent magnet synchronous motor of direct torque control.

Steam Pressure of Foaming Machine of Fuzzy PID Control and Simulation

This paper focus on designing a fuzzy PID controller design about the vapor pressure of the EPE foaming machine parameters, and raise a self-tuning method of PID parameters, and use the fuzzy control toolbox of MATLAB to simulate its control system, which are compared with the simulation of conventional PID controller, the results show the design of fuzzy PID controller have high control precision, small overshoot, good dynamic performance characteristics.

Optimization of PID Controller Based on Particle Swam Algorithm for Automobile ABS

Abstract Optimization of the parameters of PID controller of Automobile Anti-locked Braking System based on Particle swarm algorithm is proposed. The method takes the PID controller parameters of automobile Anti-locked Braking System as optimization variables, the control parameters were be optimized by particle swarm optimization in the parameter space. Then taking the parameters which were not optimized and the optimized parameters as control parameters, the simulation is carried out with substituting the control parameters into the PID simulation model of automobile Anti-locked Braking System, the simulation results show the braking distance significantly reduced , braking time advanced, the braking safety of automotive was Improved. Studies have shown that the optimization algorithm is effective and feasible; it can provide reference for us to develop the brakes of automotive.

A Study on PID Control and Simulation Based on BP Neural Network

This paper introduces the neural network PID control method, in which the parameters of PID controller is adjusted by the use of the self-study ability. And the PID controller can adapt itself actively. The dynamic BP algorithm of the three-layered network realizes the online real-time control, which displays the robustness of the PID control, and the capability of BP neural network to deal with nonlinear and uncertain system. A simulation is made by using of this method. The result of it shows that the neural network PID controller is better than the conventional one, and has higher accuracy and stronger adaptability, which can get the satisfied control result.

Numerical simulation and analysis of fuzzy PID and PSD control methodologies as dynamic energy efficiency measures

Energy efficiency enhancement is achieved by utilizing control algorithms that reduce overshoots and undershoots as well as unnecessary fluctuations in the amount of energy input to energy consuming systems during transient operation periods. It is hypothesized that application of control methodologies with characteristics that change with time and according to the system dynamics, identified as dynamic energy efficiency measures (DEEM), achieves the desired enhancement. The objective of this study is to simulate and analyze the effects of fuzzy logic based tuning of proportional integral derivative (F-PID) and proportional sum derivative (F-PSD) controllers for a heating and cooling energy system while accounting for the dynamics of the major system components. The procedure to achieve the objective includes utilization of fuzzy logic rules to determine the PID and PSD controllers gain coefficients so that the control laws for regulating the heat exchangers heating or cooling energy inputs are determined in each time step of the operation period. The performances of the F-PID and F-PSD controllers are measured by means of two cost functions that are based on quadratic forms of the energy input and deviation from a set point temperature. It is found that application of the F-PID control algorithm, as a DEEM, results in lower costs for energy input and deviation from a set point temperature by 24% and 17% as compared to a PID and 13% and 8% as compared to a PSD, respectively. It is also shown that the F-PSD performance is better than that of the F-PID controller.