scholarly journals Trajectory Tracking and Stabilization of a Quadrotor Using Model Predictive Control of Laguerre Functions

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Mapopa Chipofya ◽  
Deok Jin Lee ◽  
Kil To Chong
Keyword(s):  
State Space ◽  
Predictive Control ◽  
Trajectory Tracking ◽  
Linear Quadratic Regulator ◽  
Laguerre Functions ◽  
Linear Quadratic ◽  
Model Predictive Controller ◽  
Predictive Controller ◽  
Linear State ◽  
Quadrotor System

This paper presents a solution to stability and trajectory tracking of a quadrotor system using a model predictive controller designed using a type of orthonormal functions called Laguerre functions. A linear model of the quadrotor is derived and used. To check the performance of the controller we compare it with a linear quadratic regulator and a more traditional linear state space MPC. Simulations for trajectory tracking and stability are performed in MATLAB and results provided in this paper.

State-Space Dynamic Performance Preview-Predictive Controller

2006 ◽  
Vol 129 (2) ◽  
pp. 144-153 ◽  
Author(s):  
Andrzej W. Ordys ◽  
Masayoshi Tomizuka ◽  
Michael J. Grimble
Keyword(s):  
State Space ◽  
Predictive Control ◽  
Performance Index ◽  
Dynamic Performance ◽  
Optimization Procedure ◽  
Control Algorithms ◽  
Optimization Approach ◽  
Linear Quadratic ◽  
Preview Control ◽  
Predictive Controller

The paper discusses state-space generalized predictive control and the preview control algorithms. The optimization procedure used in the derivation of predictive control algorithms is considered. The performance index associated with the generalized predictive controller (GPC) is examined and compared with the linear quadratic (LQ) optimal control formulation used in preview control. A new performance index and consequently a new algorithm is proposed dynamic performance predictive controller (DPPC) that combines the features of both GPC and preview controller. This algorithm minimizes the performance index through a dynamic optimization. A simple example illustrates the features of the three algorithms and prompts a discussion on what is actually minimized in predictive control. The DPPC algorithm, derived in this paper, provides for a minimum of the predictive performance index. The differences and similarities between the preview control and the predictive control have been discussed and optimization approach of predictive control has been explained.

scholarly journals A Model and State-Space Controllers for an Intercooled, Regenerated (ICR) Gas Turbine Engine

1992 ◽  
Author(s):  
J. W. Watts ◽  
T. E. Dwan ◽  
R. W. Garman
Keyword(s):  
State Space ◽  
Gas Turbine ◽  
High Efficiency ◽  
Linear Quadratic Regulator ◽  
Gas Turbine Engine ◽  
Linear Quadratic ◽  
Turbine Engine ◽  
Simulation Language ◽  
Linear State ◽  
High Level

A two-and-one-half spool gas turbine engine was modeled using the Advanced Computer Simulation Language (ACSL), a high level simulation environment based on FORTRAN. A possible future high efficiency engine for powering naval ships is an intercooled, regenerated (ICR) gas turbine engine and these features were incorporated into the model. Utilizing sophisticated instructions available in ACSL linear state-space models for this engine were obtained. A high level engineering computational language, MATLAB, was employed to exercise these models to obtain optimal feedback controllers characterized by the following methods: (1) state feedback; (2) linear quadratic regulator (LQR) theory; and (3) polygonal search. The methods were compared by examining the transient curves for a fixed off-load, and on-load profile.

scholarly journals KONTROL SUDUT ATTITUDE MENGGUNAKAN LINEAR QUADRATIC REGULATOR (LQR) UNTUK QUADROTOR DENGAN PAYLOAD

ROTASI ◽  
2013 ◽  
Vol 15 (4) ◽  
pp. 16 ◽  
Author(s):  
Mochammad Ariyanto ◽  
Joga Dharma Setiawan ◽  
Ismoyo Haryanto
Keyword(s):  
Steady State ◽  
State Space ◽  
Linear Quadratic Regulator ◽  
Linear Quadratic ◽  
Aerial Manipulation ◽  
Step Input ◽  
Object Interaction ◽  
Integral Action ◽  
Linear State ◽  
State Error

Quadrotor telah dikembangkan oleh peneliti di dunia ini sebagai aerial object interaction/aerial manipulation. Agar quadrotor bisa terbang mengangkat beban, maka dibutuhkan sistem kontrol yang dapat mengkompensasi perubahan pusat gravitasi. Pada penelitian ini, model matematika quadrotor dengan efek beban/payload akan dikembangkan. Untuk mengkompensasi perubahan lokasi pusat gravitasi karena efek beban, Linear Quadratic Regulator (LQR) akan digunakan untuk stabilisasi sudut Euler.Model linear state space dihitung menggunakan persmaan gerak onlinear quadrotor tanpa beban, kemudian disintesis sistem kontrol   LQR. Kontrol tersebut diterapkan dalam model matematika nonlinear dengan beban/payload. Berdasarkan hasil simulasi integral action dari kontrol LQR dapat menghilangkan steady state error setelah diberikan step input, dan kondisi sudut awal sebesar 50. Variasi beban dari 0 gr sampai dengan 200 gr tidak akan memberikan steady state error

Model Predictive Control of Spacecraft Relative Motion Maneuvers Using the IPA-SQP Approach

2013 ◽  
Author(s):  
Hyeongjun Park ◽  
Ilya Kolmanovsky ◽  
Jing Sun
Keyword(s):  
Performance Evaluation ◽  
Predictive Control ◽  
Relative Motion ◽  
Sequential Quadratic Programming ◽  
Nonlinear Constraints ◽  
Linear Quadratic ◽  
Model Predictive Controller ◽  
Polyhedral Norm ◽  
Predictive Controller ◽  
Performance Evaluation And Comparison

In this paper, a Model Predictive Controller (MPC) based on the Integrated Perturbation Analysis and Sequential Quadratic Programming (IPA-SQP) is designed and analyzed for spacecraft relative motion maneuvering. To evaluate the effectiveness of the IPA-SQP MPC, the results are compared with the linear quadratic MPC algorithm developed in [4, 13–15]. It is shown that the IPA-SQP algorithm can handle directly nonlinear constraints on thrust magnitude without resorting to saturation or polyhedral norm approximations. Spacecraft fuel consumption related metrics are examined for performance evaluation and comparison.

scholarly journals Improving the Performance of Single-Intersection Urban Traffic Networks Based on a Model Predictive Controller

2021 ◽  
Vol 13 (10) ◽  
pp. 5630
Author(s):  
Sadiqa Jafari ◽  
Zeinab Shahbazi ◽  
Yung-Cheol Byun
Keyword(s):  
Model Predictive Control ◽  
State Space ◽  
Predictive Control ◽  
Urban Traffic ◽  
Capital City ◽  
Traffic Network ◽  
Model Predictive Controller ◽  
Traffic Volumes ◽  
Predictive Controller ◽  
Control Traffic

The use of a Model Predictive Controller (MPC) in an urban traffic network allows for controlling the infrastructure of a traffic network and errors in its operations. In this research, a novel, stable predictive controller for urban traffic is proposed and state-space dynamics are used to estimate the number of vehicles at an isolated intersection and the length of its queue. This is a novel control strategy based on the type of traffic light and on the duration of the green-light phase and aims to achieve an optimal balance at intersections. This balance should be adaptable to the unchanging behavior of time and to the randomness of traffic situations. The proposed method reduces traffic volumes and the number of crashes involving cars by controlling traffic on an urban road using model predictive control. A single intersection in Tehran, the capital city of Iran, was considered in our study to control traffic signal timing, and model predictive control was used to reduce traffic. A model of traffic systems was extracted at the intersection, and the state-space parameters of the intersection were designed using the model predictive controller to control traffic signals based on the length of the vehicle queue and on the number of inbound and outbound vehicles, which were used as inputs. This process demonstrates that this method is able to reduce traffic volumes at each leg of an intersection and to optimize flow in a road network compared to the fixed-time method.

Mobile Robot Motion Control Using Laguerre-Based Model Predictive Control

2015 ◽  
Vol 776 ◽  
pp. 403-410 ◽  
Author(s):  
Mapopa Chipofya ◽  
Deok Jin Lee ◽  
Kil To Chong
Keyword(s):  
Mobile Robot ◽  
Linear Model ◽  
Motion Control ◽  
Predictive Control ◽  
Robot Motion ◽  
Laguerre Functions ◽  
Model Predictive Controller ◽  
Cartesian System ◽  
Predictive Controller ◽  
Simulation Results

This paper presents a method of solving the problem of mobile robot motion control using a model predictive controller designed using Laguerre functions. A linear model of the two-wheeled nonholonomic robot is used. This linear model is obtained by converting the nonlinear model in the Cartesian system to a polar one. This change is preferred because it is easier to work with the linear model than its corresponding nonlinear one. Simulation results obtained from MATLAB showing that Laguerre-based MPC (LMPC) performs well are presented.

Subspace-Based Indirect Adaptive State-Space Model Predictive Control

2012 ◽  
Vol 246-247 ◽  
pp. 311-316
Author(s):  
Xiao Suo Luo
Keyword(s):  
Model Predictive Control ◽  
State Space ◽  
Predictive Control ◽  
Control Method ◽  
State Space Model ◽  
System Model ◽  
Subspace Identification ◽  
Space Model ◽  
Predictive Controller ◽  
Linear State

In order to deal with nonlinear, time-varying and disturbance-involved characteristics in the practical industrial processes, an indirect adaptive state-space MPC (model predictive control) method based on subspace identification is proposed. The state-space model, obtained through the POMOESP (Past Output MOESP, MOESP is one form of the subspace identification methods) algorithm, is regarded as the system model. Then, this model is used to design the model predictive controller that involves the solution of a quadratic programming problem to constraints. This controller is applied to the process control simulation on a 2-CSTR. Through comparisons of performance with a linear state-space MPC scheme, the superiority of the proposed control method is illustrated.

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