1025-Modelling and Simulation for Optimal Control of Nonlinear Inverted Pendulum Dynamical System Us

مقاله شبیه سازی شده

کد پروژه: 1025

 

موضوع: کنترل بهینه  Optimal Control

 

شامل:مقاله اصلی + فایل شبیه سازی با نرم افزار متلبMatlab 

 

عنوان مقاله:

Modelling and Simulation for Optimal Control of Nonlinear Inverted Pendulum Dynamical System Using PID Controller and LQR 

 

Address: ieeexplore

Download: PDF

 

Abstract

This paper presents the modelling and simulation for optimal control design of nonlinear inverted pendulum-cart dynamic system using Proportional-Integral-Derivative (PID) controller and Linear Quadratic Regulator (LQR). LQR, an optimal control technique, and PID control method, both of which are generally used for control of the linear dynamical systems have been used in this paper to control the nonlinear dynamical system. The nonlinear system states are fed to LQR which is designed using linear state-space model. Inverted pendulum, a highly nonlinear unstable system is used as a benchmark for implementing the control methods. Here the control objective is to control the system such that the cart reaches at a desired position and the inverted pendulum stabilizes in upright position. The MATLAB-SIMULINK models have been developed for simulation of control schemes. The simulation results justify the comparative advantages of LQR control methods.

 

سفارش پروژه

1024- Optimal Control with Fuzzy state space Modeling using Riccati Equation

مقاله شبیه سازی شده

کد پروژه: 1024

 

موضوع: کنترل فازی  Fuzzy Control

 

شامل:مقاله اصلی + فایل شبیه سازی با نرم افزار متلبMatlab +گزارش کامل از خلاصه ای از مقاله و نتایج شبیه سازی

 

عنوان مقاله:

Optimal Control with Fuzzy state space Modeling using Riccati Equation

 دانلود: PDF
نام ژورنال یا کنفرانس مربوط به مقاله: 

International Journal of Information and Electronics Engineering

 

Abstract

Fuzzy logic has a boon for nonlinear controlsystems. Normal fuzzy logic control with a proportional integral – Derivative (PID) controller is common. Controlsystems can be defined through transfer functions and state-space. relations for linear systems. Optimal control to meet aperformance index is possible only through State Spaceanalysis. Optimal control in state space is centered around theRiccati Equation with state variable functions that has to besolved to yield the control law or trajectory. In the controlscheme of an ozone generator, optimal control with aperformance index had to be implemented. The method forfinding the control functions by solving the equationgraphically is described. The data is used for realizing anembedded control scheme for the generator

 

Index Terms

Fuzzy control, neuro-fuzzy systems, fuzzysystem model, process control

 

سفارش پروژه

 

1006- Aircraft Control System Using LQG and LQR Controller with Optimal Estimation-Kalman Filter Des

مقاله شبیه سازی شده

کد پروژه: 1006

 

موضوع:کنترل کننده های LQR و LQG؛ فیلتر کالمن

 

شامل:مقاله اصلی + فایل شبیه سازی با نرم افزار متلبMatlab +گزارش کامل از خلاصه ای از مقاله و نتایج شبیه سازی

 

عنوان مقاله:

 

Aircraft Control System Using LQG and LQR Controller with

Optimal Estimation-Kalman Filter Design

 

 

آدرس: ScienceDirect

 

خلاصه:

Abstract

This paper, describes a LQG and LQR robust controller for the lateral and longitudinal flight dynamics of an aircraft control system. The controller is used in order to achieve robust stability and good dynamic performance against the variation of aircraft parameters. The application of the proposed LQG and LQR robust control scheme is implemented through the simulation. The proposed robust controller for aircraft stability is designed using Matlab/Simulink program. Simulation results confirm the performance of the proposed controller for aircraft control system. Since the time of its introduction, the Kalman filter has been the subject of extensive research and application, particularly in the area of autonomous or assisted navigation. For example, to determine the velocity of an aircraft or sideslip angle, one could use a Doppler radar, the velocity indications of an inertial navigation system, or the relative wind information in the air data system. Rather than ignore any of these outputs, a Kalman filter could be built to combine all of this data and knowledge of the various systems dynamics to generate an overall best estimate of pitch, roll and sideslip angle.

 

Keywords

Aircraft motion; LQG control; LQR control; lateral stability; longitudinal stability; State estimator Kalman filter.

توضیحات:

برای مقاله موارد زیر انجام شده است:

• طراحی یک کنترل PID
• طراحی یک کنترل کننده فیدبک حالت
• طراحی یک کنترل کننده LQR
• تغییر مقادیر QR و مقایسه.
• مقایسه کنترل کننده بهینه شده را با قبل
• تهیه گزارش به فرمت مقاله

سفارش پروژه

1004- Parameter identification of chaotic dynamic systems throughan improved particle swarm optimiza

مقاله شبیه سازی شده

کد پروژه: 1004

 

موضوع:الگوریتم pso و سیستم آشوب

 

شامل:مقاله اصلی + فایل شبیه سازی با نرم افزار متلبMatlab +گزارش کامل از خلاصه ای از مقاله و نتایج شبیه سازی

 

عنوان مقاله:

Parameter identification of chaotic dynamic systems throughan improved particle swarm optimization

آدرس:sciencedirect

خلاصه:

Abstract

This paper is concerned with the parameter identification problem for chaotic dynamic systems. An improved particle swarm optimization (IPSO), which is a novel evolutionary computation technique, is proposed to solve this problem. The feasibility of this approach is demonstrated through identifying the parameters of Lorenz chaotic system. The performance of the proposed IPSO is compared with the genetic algorithm (GA) and standard particle swarm optimization (SPSO) in terms of parameter accuracy and computational time. It is illustrated in simulations that the proposed IPSO is more successful than the SPSO and GA. IPSO is also improved to detect and determine the variation of parameters. In this case, a sentry particle is introduced to detect any changes in system parameters and if any change is detected, IPSO runs to find new optimal parameters. Hence, the proposed algorithm is a promising particle swarm optimization algorithm for system identification.

Keywords

Collaborative filtering; Recommender systems; Similarity measures; Metrics; Genetic algorithms; Performance

 

سفارش پروژه

1003- Improving collaborative filtering recommender system results and performance using genetic al

مقاله شبیه سازی شده

کد پروژه: 1003

 

موضوع:الگوریتم ژنتیک و کنترل بهینه

 

شامل:مقاله اصلی + فایل شبیه سازی با نرم افزار متلبMatlab +گزارش کامل از خلاصه ای از مقاله و نتایج شبیه سازی

 

عنوان مقاله:

Improving collaborative filtering recommender system results and performance

using genetic algorithms 

آدرس:sciencedirect

خلاصه:

Abstract

This paper presents a metric to measure similarity between users, which is applicable in collaborative filtering processes carried out in recommender systems. The proposed metric is formulated via a simple linear combination of values and weights. Values are calculated for each pair of users between which the similarity is obtained, whilst weights are only calculated once, making use of a prior stage in which a genetic algorithm extracts weightings from the recommender system which depend on the specific nature of the data from each recommender system. The results obtained present significant improvements in prediction quality, recommendation quality and performance.

 

Highlights

 

►Metric formulated via a simple linear combination of values and weights. ► Model-based approach using genetic algorithms to improve results. ► Collaborative filtering predictions accuracy and performance improvements.

 

Keywords

Collaborative filtering; Recommender systems; Similarity measures; Metrics; Genetic algorithms; Performance

سفارش پروژه

1002- Optimal brushless DC motor design using genetic algorithms

مقاله شبیه سازی شده

کد پروژه: 1002

 

موضوع:الگوریتم ژنتیک و کنترل بهینه

 

شامل:مقاله اصلی + فایل شبیه سازی با نرم افزار متلبMatlab +گزارش کامل از خلاصه ای از مقاله و نتایج شبیه سازی

 

عنوان مقاله:

Optimal brushless DC motor design using genetic algorithms

 

آدرس:sciencedirect

خلاصه:

Abstract

This paper presents a method for the optimal design of a slotless permanent magnet brushless DC (BLDC) motor with surface mounted magnets using a genetic algorithm. Characteristics of the motor are expressed as functions of motor geometries. The objective function is a combination of losses, volume and cost to be minimized simultaneously. Electrical and mechanical requirements (i.e. voltage, torque and speed) and other limitations (e.g. upper and lower limits of the motor geometries) are cast into constraints of the optimization problem. One sample case is used to illustrate the design and optimization technique.

Keywords

Optimization;  Brushless DC;  Motor;  Slotless;  Radial flux;  Genetic algorithm;  Surface mounted; Magnet

سفارش پروژه

 

1001_ Application of an improved PSO algorithm to optimal tuning of PID gains for water turbinegover

مقاله شبیه سازی شده

کد پروژه: 1001

موضوع:الگوریتم PSO و کنترل بهینه

شامل:مقاله اصلی + فایل شبیه سازی با نرم افزار متلبMatlab +گزارش کامل از خلاصه ای از مقاله و نتایج شبیه سازی

عنوان مقاله:

Application of an improved PSO algorithm to optimal tuning of PID gains for water turbinegovernor

آدرس:sciencedirect

خلاصه:

Abstract

In this paper, an improved particle swarm optimization (IPSO) algorithm is proposed. Besides the individual best position and the global best position, a nominal average position of the swarm is introduced in IPSO. The performance of IPSO is compared to different PSO variants with five well-known benchmark functions. The experimental results show that the proposed IPSO algorithm improves the searching performance on the benchmark functions. And then, IPSO, as well as other PSO variants, is applied to optimal tuning of Proportional–Integral–Derivative (PID) gains for a typical PID control system of water turbine governor. The computer simulation results of an actual hydro power plant in China show that IPSO algorithm has stable convergence characteristic and good computational ability, and it is an effective and easily implemented method for optimal tuning of PID gains of water turbine governor.

 

سفارش پروژه