Comparison of PID and MPC controllers for continuous stirred tank reactor (CSTR) concentration control

Continuous Stirred Tank Reactor (CSTR) is amajorarea in process, chemical and control engineering. In this paper, PID and MPC controllers are designed for CSTR in order to analyze the output concentration of the system by comparing the two proposed systems using Matlab/Simulink. Comparison have been made using two desired concentration input (Random reference and step) signals with and without input side disturbance (Flow rate error). The simulation result shows that the continuous stirred tank reactor with MPC controller have better response in minimizing the overshoot and tracking the desired concentration for the system without input disturbance and with the effect of the disturbance makes the continuous stirred tank reactor with MPC controller output with small fluctuations and still better than the continuous stirred tank reactor with PID controller. Finally the comparative analysis and simulation results prove the effectiveness of the continuous stirred tank reactor with MPC controller

Comparison of Neural Network Based Controllers for Nonlinear EMS Magnetic Levitation Train

Magnetic levitation system is operated primarily based at the principle of magnetic attraction and repulsion to levitate the passengers and the train. However, magnetic levitation trains are rather nonlinear and open loop unstable which makes it hard to govern. In this paper, investigation, design and control of a nonlinear Maglev train based on NARMA-L2, model reference and predictive controllers. The response of the Maglev train with the proposed controllers for the precise role of a Magnetic levitation machine have been as compared for a step input signal. The simulation consequences prove that the Maglev teach system with NARMA-L2 controller suggests the quality performance in adjusting the precise function of the system and the device improves the experience consolation and street managing criteria

State and disturbance estimation of a linear systems using proportional integral observer

This paper offers a short survey of linear systems Proportional-Integral-Observer design. This observer has the capacity to estimate simultaneously the states and unknown inputs which include disturbances or model uncertainties appearing on the system. The design of state and output estimation using PO and state, output and disturbance estimation using PIO is done using Matlab/Simulink successfully. The simulation is done for estimating using PO and PIO and the results proved that estimates the state variables and output correctly when there is no disturbance in the plant and there is a constant steady-state error in estimation after leading a constant disturbance into the plant for both state variables and plant output for the Proportional Observer and there is ability to estimate state variables, disturbance and system output correctly with or without the disturbance in plant for the Proportional Integral Observer

Design and Control of EMS Magnetic Levitation Train using Fuzzy MRAS and PID Controllers

In this paper, a Magnetic Levitation (MAGLEV) train is designed with a first degree of freedom electromagnetbased totally system that permits to levitate vertically up and down. Fuzzy logic, PID and MRAS controllers are used to improve the Magnetic Levitation train passenger comfort and road handling. A Matlab Simulink model is used to compare the performance of the three controllers using step input signals. The stability of the Magnetic Levitation train is analyzed using root locus technique. Controller output response for different time period and change of air gap with different time period is analyzed for the three controllers. Finally the comparative simulation and experimental results demonstrate the effectiveness of the presented fuzzy logic controller

Performance Investigation of AC Servomotor Position Control using Fuzzy Logic and Observer Based Controllers

An AC servomotor which is mostly a two-phase induction motor with two stator field coils placed 90 electrical degrees apart used for controlling position, speed and acceleration in manufacturing industries. In this paper, a two-phase induction motor has been designed with a fuzzy logic and observer based controllers to improve the performance of the system. Comparison of the AC servomotor with the proposed controllers for tracking a step and a square desired position signal input has been done using Matlab/Simulink toolbox and a promising result obtained

Comparison of Neural Network NARMA-L2 Model Reference and Predictive Controllers for Electromagnetic Space Vehicle Suspension System

Electromagnetic Suspension System (EMS) is mostly used in the field of high-speed vehicle. In this study, a space exploring vehicle quarter electromagnetic suspension system is modelled, designed and simulated using Neural network-based control problem. NARMA-L2, Model reference and predictive controllers are designed to improve the body travel of the vehicle using bump road profile. Comparison between the proposed controllers is done and a promising simulation result have been analyzed

Nonlinear Active Suspension System Control using Fuzzy Model Predictive Controller

Recent years, active suspension system has been widely used in automobiles to improve the road holding ability and the riding comfort. This study presents a new fuzzy model predictive control for a nonlinear quarter car active suspension system. A nonlinear dynamical model of active suspension is established, where the nonlinear dynamical characteristic of the spring and damper are considered. Based on the proposed fuzzy model predictive control method is presented to stabilize the displacement of the active suspension in the presence of different road profiles. Parameters of the model predictive and fuzzy logic control laws are designed to estimate the (Bump and Sinusoidal)road profile input in the active suspension. At last, the reliability of the fuzzy model predictive control method is evaluated by the MATLAB simulation tool. Simulation result shows that the fuzzy model predictive control method obtained the satisfactory control performance for the active suspension system

Loudspeaker Noise Disturbance Control using Optimal and Robust Controllers

Noise reduction is the major issue in the loudspeaker for the application of the musical instruments and related areas. In this study, a noise disturbance control of a loudspeaker with optimal and robust controllers has been done successfully. The noise of the loudspeaker has been analyzed by simply track a reference cone displacement with the actual cone displacement. Static output feedback and H4 optimal loop shaping controllers have been used to compare the actual and reference cone displacements by using a sine wave and random cone displacement signals and a promising results have been analyzed

Inverted Pendulum Control using NARMA-l2 with Resilient Backpropagation and Levenberg Marquardt Backpropagation Training Algorithm

In this study, the performance of inverted pendulum has been Investigated using neural network control theory. The proposed controllers used in this study are NARMA-L2 with Resilient backpropagation and Levenberg Marquardt backpropagation algorithm controllers. The mathematical model of Inverted Pendulum on a Cart driving mechanism have been done successfully. Comparison of an inverted pendulum with NARMA-L2 with Resilient backpropagation and Levenberg Marquardt backpropagation algorithm controllers for a control target deviation of an angle from vertical of the inverted pendulum using two input signals (step and random). The simulation result shows that the inverted pendulum with NARMA-L2 with resilient backpropagation controller to have a small rise time, settling time and percentage overshoot in the step response and having a good response in the random response too. Finally, the inverted pendulum with with NARMA-L2 with resilient backpropagation controller shows the best performance in the overall simulation result

INTELLIGENT LIQUID LEVEL CONTROL OF A COUPLED NONLINEAR THREE TANK SYSTEM SUBJECTED TO VARIABLE FLOW PARAMETERS

In this paper, an intelligent control system technique is proposed to model and control of a nonlinear coupled three tank system. Two pumps fed the tank 1 and tank 2 and a fractional flow of these two pumps fed tank 3. The main aim of this paper is to make a set point tracking experiments of the tanks level using a nonlinear autoregressive moving average L-2 (NARMA L-2) and neural network predictive controllers. The proposed controllers are designed with the same neural network architecture and algorithm. Comparison of the system with the proposed controllers for tracking a step and random level set points for a fixed and variable flow parameter and some good results have been obtained