8 research outputs found
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
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
Adaptive Control using Nonlinear Autoregressive-Moving Average-L2 Model for Realizing Neural Controller for Unknown Finite Dimensional Nonlinear Discrete Time Dynamical Systems
This study considers the problem of using
approximate way for realizing the neural supervisor for
nonlinear multivariable systems. The Nonlinear
Autoregressive-Moving Average (NARMA) model is an
exact transformation of the input-output behavior of
finite-dimensional nonlinear discrete time dynamical
organization in a hoodlum of the equilibrium state.
However, it is not convenient for intention of adaptive
control using neural networks due to its nonlinear
dependence on the control input. Hence, quite often,
approximate technique are used for realizing the neural
supervisor to overcome computational complexity. In this
study, we introduce two classes of ideal which are
approximations to the NARMA model and which are
linear in the control input, namely NARMA-L1 and
NARMA-L2. The latter fact substantially simplifies both
the theoretical breakdown as well as the practical request
of the controller. Extensive imitation studies have shown
that the neural controller designed using the proposed
approximate models perform very well and in dozens
situation even better than an approximate controller
designed using the exact NARMA Model. In view of their
mathematical tractability as well as their fate in
simulation studies, a matter is made in this study that such
approximate input-output paragon warrants a detailed
study in their own right
Position Control of a Three Degree of Freedom Gyroscope using Optimal Control
In this paper, a 3 DOF gyrscope position control have been designed and controlled using optimal control
theory. An input torque has been given to the first axis and the angular position of the second axis have been
analyzed while the third axis are kept free from rotation. The system mathematical model is controllable and
observable. Linear Quadratic Integral (LQI) and Linear Quadratic State Feedback Regulator (LQRY) controllers
have been used to improve the performance of the system. Comparison of the system with the proposed controllers
for tracking a desired step and random angular position have been done using Matlab/Simulink Toolbox and a
promising results has been analyzed
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
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
Modeling and Performance Analysis of Shell Tube Surface Condenser under Lumped Parameters using Fuzzy Self-tuning PI Controller
Shell tube surface condenser (STSC) is a heat exchanger system that exchange a high pressure steam into low pressure water and it is widely used in applications like textile industries and nuclear power plants. The modelling of the system has been established based on lumped parameters. In this paper, a fuzzy expert system is developed in order to improve the performance of the condenser. A pressure feedback system has been developed to analyze the effect of the condenser output temperature, circulating water flow and heat developed. An experiment has been made for the closed loop system using fuzzy tuned PI, fuzzy logic and PID controllers and a promising results have been obtained successfully
Comparison of a Nonlinear Magnetic Levitation Train Parameters using Mixed H 2/H infinity and Model Reference Controllers
To improve the riding performance and levitation stability of
a high‐speed magnetic levitation (maglev) train, a control strategy based
on mixed H 2/H4 with regional pole placement and model‐reference
controllers are proposed. First, the nonlinear maglev train model is
established, then the proposed system is designed to observe the
movement of a suspension frame and a control strategy based on mixed
H 2/H4 with regional pole placement and model‐reference control
method are proposed. Test and analysis of the proposed system has
been done using MATLAB toolbox for train levitation height, velocity and
current consume. Comparative simulation results show that the mixed
H 2/H4 with regional pole placement control strategy has a better
performance under the condition of step and random train levitation
height