Robust Control Theory Based Performance Investigation of an Inverted Pendulum System using Simulink

In this paper, the performance of inverted pendulum have been Investigated using robust control theory. The robust controllers used in this paper are H∞ Loop Shaping Design Using Glover McFarlane Method and mixed H∞ Loop Shaping Controllers. The mathematical model of Inverted Pendulum, a DC motor, Cart and Cart driving mechanism have been done successfully. Comparison of an inverted pendulum with H∞ Loop Shaping Design Using Glover McFarlane Method and H∞ Loop Shaping Controllers for a control target deviation of an angle from vertical of the inverted pendulum using two input signals (step and impulse). The simulation result shows that the inverted pendulum with mixed H∞ Loop Shaping Controller to have a small rise time, settling time and percentage overshoot in the step response and having a good response in the impulse response too. Finally the inverted pendulum with mixed H∞ Loop Shaping Controller shows the best performance in the overall simulation result

Comparison of neural network NARMA-L2 model reference and predictive controllers for nonlinear quarter car active suspension system

Recently, active suspension system will become important to the vehicle industries because of its advantages in improving road managing and ride comfort. This paper offers the development of mathematical modelling and design of a neural network control approach. The paper will begin with a mathematical model designing primarily based at the parameters of the active suspension system. A nonlinear three by four-way valve-piston hydraulic actuator became advanced which will make the suspension system under the active condition. Then, the model can be analyzed thru MATLAB/Simulink software program. Finally, the NARMA-L2, model reference and predictive controllers are designed for the active suspension system. The results are acquired after designing the simulation of the quarter-car nonlinear active suspension system. From the simulation end result using MATLAB/Simulink, the response of the system might be as compared between the nonlinear active suspension system with NARMA-L2, model reference and predictive controllers. Besides that, the evaluation has been made between the proposed controllers thru the characteristics of the manage objectives suspension deflection, body acceleration and body travel of the active suspension system. . As a conclusion, designing a nonlinear active suspension system with a nonlinear hydraulic actuator for quarter car model has improved the car performance by using a NARMA-L2 controller. The improvements in performance will improve road handling and ride comfort performance of the active suspension system

Nonlinear autoregressive moving average-L2 model based adaptive control of nonlinear arm nerve simulator system

This paper considers the trouble of the usage of approximate strategies for realizing the neural controllers for nonlinear SISO systems. In this paper, we introduce the nonlinear autoregressive moving average (NARMA-L2) model which might be approximations to the NARMA model. The nonlinear autoregressive moving average (NARMA-L2) model is an precise illustration of the input–output behavior of finite-dimensional nonlinear discrete time dynamical systems in a neighborhood of the equilibrium state. However, it isn't always handy for purposes of neural networks due to its nonlinear dependence on the manipulate input. In this paper, nerves system based arm position sensor device is used to degree the precise arm function for nerve patients the use of the proposed systems. In this paper, neural network controller is designed with NARMA-L2 model, neural network controller is designed with NARMA-L2 model system identification based predictive controller and neural network controller is designed with NARMA-L2 model based model reference adaptive control system. Hence, quite regularly, approximate techniques are used for figuring out the neural controllers to conquer computational complexity. Comparison were made among the neural network controller with NARMA-L2 model, neural network controller with NARMA-L2 model system identification based predictive controller and neural network controller with NARMA-L2 model reference based adaptive control for the preferred input arm function (step, sine wave and random signals). The comparative simulation result shows the effectiveness of the system with a neural network controller with NARMA-L2 model based model reference adaptive control system. Index Terms--- Nonlinear autoregressive moving average, neural network, Model reference adaptive control, Predictive controller DOI: 10.7176/JIEA/10-3-03 Publication date: April 30th 202

Body Travel Performance Improvement of Space Vehicle Electromagnetic Suspension System using LQG and LQI Control Methods

Electromagnetic suspension system (EMS) is mostly used in the field of high-speed vehicle. In this paper, a space exploring vehicle quarter electromagnetic suspension system is modelled, designed and simulated using linear quadratic optimal control problem. Linear quadratic Gaussian and linear quadratic integral 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

Comparison of H∞ and μ-synthesis Control Design for Quarter Car Active Suspension System using Simulink

To improve road dealing with and passenger consolation of a vehicle, a suspension system is supplied. An active suspension system is taken into consideration better than the passive suspension system. In this paper, an active suspension system of a linear quarter vehicle is designed, that's issue to exclusive disturbances on the road. Since the parametric uncertainty within the spring, the shock absorber and the actuator has been taken into consideration, robust control is used. H∞ and µ-Synthesis controllers of are used to improve using consolation and road dealing with potential of the vehicle, in addition to confirm the sturdy stability and overall performance of the system. In the H∞ design, we designed a driving force for passenger consolation and to preserve the deflection of the suspension small and to reduce the disturbance of the road to the deflection of the suspension. For the µ synthesis system, we designed a controller with hydraulic actuator and uncertainty model. We designed a MATLAB / SIMULINK model for the active suspension system with the H∞ and µ-synthesis controllers we tested the use of 4 road disturbance inputs (bump, random, sinusoidal pavement and slope) for deflection of the suspension, body acceleration and body travel for passive, active suspension with controller and active suspension without controller. Finally, we evaluate the H∞ and µ-synthesis controllers with a Simulink model for suspension deflection, body acceleration and body travel simulation, and the result suggests that both designs offer correct overall performance, however the H∞ controller has superior overall performance as compared to the µ-synthesis controller

H ∞ and μ-synthesis Design of Quarter Car Active Suspension System

To improve the street managing and passenger comfort of a vehicle, a suspension system is furnished. An active suspension device is considered to be better than the passive suspension device. In this paper, 2 degree of freedom of an active suspension system of a linear vehicle are designed, that's challenge to oneof-a-kind disturbances on the road. Since the parametric uncertainty inside the spring, the shock absorber, the mass and the actuator has been taken into consideration, robust control is used. In this paper, H∞ and µ-synthesis controllers are used to enhance using consolation and the capability to force the car on the road. For the analysis of the time domain, a MATLAB script software become used and a check with 4 road disturbance inputs (bump, random, sinusoidal and harmonic) became carried out for suspension deflection, body acceleration and travel of the body for the energetic suspension with the H∞ controller and the active suspension with the µ-synthesis controller and the comparative simulation and the reference consequences display the effectiveness of the active suspension system with the µ-synthesis controller

Design and Control of Steam Flow in Cement Production Process using Neural Network Based Controllers

In this paper a NARMA L2, model reference and neural network predictive controller is utilized in order to control the output flow rate of the steam in furnace by controlling the steam flow valve. The steam flow control system is basically a feedback control system which is mostly used in cement production industries. The design of the system with the proposed controllers is done with Matlab/Simulink toolbox. The system is designed for the actual steam flow output to track the desired steam that is given to the system as input for two desired steam input signals (step and sine wave). In order to analyze the performance of the system, comparison of the proposed controllers is done by simulating the system for the two reference signals for the system with and without sensor noise disturbance. Finally the comparison results prove the effectiveness of the presented process control system with model reference controller

DC motor speed control with the presence of input disturbance using neural network based model reference and predictive controllers

In this paper we describe a technical system for DC motor speed control. The speed of DC motor is controlled using Neural Network Based Model Reference and Predictive controllers with the use of Matlab/Simulink. The analysis of the DC motor is done with and without input side Torque disturbance input and the simulation results obtained by comparing the desired and actual speed of the DC motor using random reference and sinusoidal speed inputs for the DC motor with Model Reference and Predictive controllers. The DC motor with Model Reference controller shows almost the actual speed is the same as the desired speed with a good performance than the DC motor with Predictive controller for the system with and without input side disturbance. Finally the comparative simulation result prove the effectiveness of the DC motor with Model Reference controller

Quarter car active suspension system design using optimal and robust control method

This paper offers with the theoretical and computational evaluation of optimal& robust controlproblems, with the goal of providing answers to them with MATLAB simulation.For the robust control, -synthesis controller and for the optimal control, LQR controller are designed for a quarter car active suspension system to maximize the ride comfort and road handling criteria’s of the vehicle. The proposed controllers are designed using Matlab script program using time domain analysis for the four road disturbances (bump, random sine pavement and white noise) for the control targets suspension deflection, body acceleration and body travel. Finally the simulation result proves the effectiveness of the active suspension system with -synthesis controller

Comparison of Mixed H 2 H∞ with Regional Pole Placement Control and H 2 Optimal Control for the Design of Steam Condenser

This paper investigates the comparison between mixed H 2 /H∞ with regional pole placement control and H 2 optimal control for the design of steam condenser. The comparison have been made for a step change in the steam condenser pressure set point for a step change of 10 & 23 seconds using MATLAB/Simulink environment for the steam condenser with mixed H 2 /H∞ with regional pole placement controller, steam condenser with H 2 optimal controller and steam condenser without controller. The steam condenser with mixed H 2 /H∞ with regional pole placement controller presented excellent and superior dynamic performance in response to the two step changes and an improvement in settling time. The overall simulation results demonstrated that the steam condenser with mixed H 2 /H∞ with regional pole placement controller can be an efficient alternative to the steam condenser with H 2 optimal controller for the steam condenser