scholarly journals System Identification and LQR Controller Design with Incomplete State Observation for Aircraft Trajectory Tracking

Energies ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 5354
Author(s):  
Piotr Lichota ◽  
Franciszek Dul ◽  
Andrzej Karbowski
Keyword(s):  
Simulation Model ◽  
Trajectory Tracking ◽  
Controller Design ◽  
Linear Quadratic Regulator ◽  
Atmospheric Conditions ◽  
Nonlinear Transport ◽  
Linear Quadratic ◽  
Likelihood Principle ◽  
Tracking Errors ◽  
Lqr Controller

This paper presents a controller design process for an aircraft tracking problem when not all states are available. In the study, a nonlinear-transport aircraft simulation model was used and identified through Maximum Likelihood Principle and Extended Kalman Filter. The obtained mathematical model was used to design a Linear–Quadratic Regulator (LQR) with optimal weighting matrices when not all states are measured. The nonlinear aircraft simulation model with LQR controller tracking abilities were analyzed for multiple experiments with various noise levels. It was shown that the designed controller is robust and allows for accurate trajectory tracking. It was found that, in ideal atmospheric conditions, the tracking errors are small, even for unmeasured variables. In wind presence, the tracking errors were proportional to the wind velocity and acceptable for small and moderate disturbances. When turbulence was present in the experiment, state variable oscillations occurred that were proportional to the turbulence intensity and acceptable for small and moderate disturbances.

scholarly journals Simulation of a Steam Injected Gas Turbine Plant Control System

1997 ◽  
Author(s):  
Shusheng Zang ◽  
Jaqiang Pan
Keyword(s):  
Gas Turbine ◽  
Flow Rate ◽  
Controller Design ◽  
Dynamic Performance ◽  
Linear Quadratic Regulator ◽  
Linear Quadratic ◽  
Turbine Plant ◽  
Fuel Flow ◽  
Lqr Controller ◽  
Gas Turbine Plant

The design of a modern Linear Quadratic Regulator (LQR) is described for a test steam injected gas turbine (STIG) unit. The LQR controller is obtained by using the fuel flow rate and the injected steam flow rate as the output parameters. To meet the goal of the shaft speed control, a classical Proportional Differential (PD) controller is compared to the LQR controller design. The control performance of the dynamic response of the STIG plant in the case of rejection of load is evaluated. The results of the computer simulation show a remarkable improvement on the dynamic performance of the STIG unit.

Real Time Modeling and Control of Three Tank Hybrid System

2018 ◽  
Vol 13 (1) ◽  
Author(s):  
K. Sathishkumar ◽  
V. Kirubakaran ◽  
T. K. Radhakrishnan
Keyword(s):  
Real Time ◽  
Controller Design ◽  
Linear Quadratic Regulator ◽  
Estimation Model ◽  
Linear Quadratic ◽  
Loop Control ◽  
Modeling And Control ◽  
Time Modeling ◽  
Lqr Controller ◽  
Servo Tracking

AbstractThis study discusses the modeling and linear quadratic regulator (LQR) controller based closed loop control of a three tank hybrid (TTH) process. A pseudo random binary signal (PRBS) based excitation data obtained from a real time TTH setup is utilized in validating its first principle model (FPM). Based on top and bottom interactions, various modes prevalent are considered based on steady state physical reachability analysis of the TTH for a given input range for controller design. The FPM is linearized using nominal values of process parameters using Jacobians from each existing mode. LQR controllers are designed for each mode. A supervisory structure is designed for selecting estimation model and controller for each appropriate mode. Results from real time servo tracking and disturbance rejection experiments are discussed.

scholarly journals Rotor-Flying Manipulator: Modeling, Analysis, and Control

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Bin Yang ◽  
Yuqing He ◽  
Jianda Han ◽  
Guangjun Liu
Keyword(s):  
Controller Design ◽  
Linear Quadratic Regulator ◽  
Linear Quadratic ◽  
Combined System ◽  
Underwater Robots ◽  
Modeling Analysis ◽  
Full State Feedback ◽  
Lqr Controller ◽  
Linearized Model ◽  
And Control

Equipping multijoint manipulators on a mobile robot is a typical redesign scheme to make the latter be able to actively influence the surroundings and has been extensively used for many ground robots, underwater robots, and space robotic systems. However, the rotor-flying robot (RFR) is difficult to be made such redesign. This is mainly because the motion of the manipulator will bring heavy coupling between itself and the RFR system, which makes the system model highly complicated and the controller design difficult. Thus, in this paper, the modeling, analysis, and control of the combined system, called rotor-flying multijoint manipulator (RF-MJM), are conducted. Firstly, the detailed dynamics model is constructed and analyzed. Subsequently, a full-state feedback linear quadratic regulator (LQR) controller is designed through obtaining linearized model near steady state. Finally, simulations are conducted and the results are analyzed to show the basic control performance.

Design Optimization of Car-Trailer Combinations With Electronic Stability Systems

2014 ◽  
Author(s):  
Tao Sun ◽  
Yuping He
Keyword(s):  
Controller Design ◽  
Linear Quadratic Regulator ◽  
Lateral Stability ◽  
Trial And Error ◽  
Linear Quadratic ◽  
Weighting Factors ◽  
Lqr Controller ◽  
Ct System ◽  
Trial And Error Method ◽  
Error Method

To date, Linear Quadratic Regulator (LQR) controllers based on linear vehicle models have been researched and developed for improving the lateral stability of car-trailer (CT) combinations. However, in the LQR controller design, there is no a systematic way to determine the weighting factors of the performance index. Generally, the weighting factors are selected using trial and error based on designer’s experience. In order to facilitate the LQR controller design, a new method based on a genetic algorithm (GA) is presented to determine the appropriate weighting factors in the LQR controller design. To examine the proposed method, a controller for an active trailer differential braking (ATDB) system of a car-trailer (CT) system is designed and examined. The simulation results indicate that compared with the LQR controller based on the weighting factors derived from the conventional trial and error method, the controller developed using the proposed method exhibits better performance.

Robust stabilization control of a spatial inverted pendulum using integral sliding mode controller

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Ishan Chawla ◽  
Vikram Chopra ◽  
Ashish Singla
Keyword(s):  
Inverted Pendulum ◽  
Sliding Mode ◽  
Robust Stabilization ◽  
Linear Quadratic Regulator ◽  
Humanoid Robots ◽  
Sliding Mode Controller ◽  
Linear Quadratic ◽  
Integral Sliding Mode ◽  
Wide Range ◽  
Lqr Controller

AbstractFrom the last few decades, inverted pendulums have become a benchmark problem in dynamics and control theory. Due to their inherit nature of nonlinearity, instability and underactuation, these are widely used to verify and implement emerging control techniques. Moreover, the dynamics of inverted pendulum systems resemble many real-world systems such as segways, humanoid robots etc. In the literature, a wide range of controllers had been tested on this problem, out of which, the most robust being the sliding mode controller while the most optimal being the linear quadratic regulator (LQR) controller. The former has a problem of non-robust reachability phase while the later lacks the property of robustness. To address these issues in both the controllers, this paper presents the novel implementation of integral sliding mode controller (ISMC) for stabilization of a spatial inverted pendulum (SIP), also known as an x-y-z inverted pendulum. The structure has three control inputs and five controlled outputs. Mathematical modeling of the system is done using Euler Lagrange approach. ISMC has an advantage of eliminating non-robust reachability phase along with enhancing the robustness of the nominal controller (LQR Controller). To validate the robustness of ISMC to matched uncertainties, an input disturbance is added to the nonlinear model of the system. Simulation results on two different case studies demonstrate that the proposed controller is more robust as compared to conventional LQR controller. Furthermore, the problem of chattering in the controller is dealt by smoothening the controller inputs to the system with insignificant loss in robustness.

scholarly journals FULL CAR ACTIVE DAMPING SYSTEM FOR VIBRATION CONTROL

2020 ◽  
Vol 6 (4) ◽  
pp. 1-17
Author(s):  
G. Yakubu ◽  
G. Sani ◽  
S. B. Abdulkadir ◽  
A. A.Jimoh ◽  
M. Francis
Keyword(s):  
Mathematical Model ◽  
Linear Quadratic Regulator ◽  
Active Damping ◽  
Settling Time ◽  
Linear Quadratic ◽  
Body Acceleration ◽  
The Road ◽  
Road Profile ◽  
Lqr Controller ◽  
Mass Displacement

Full car passive and active damping system mathematical model was developed. Computer simulation using MATLAB was performed and analyzed. Two different road profile were used to check the performance of the passive and active damping using Linear Quadratic Regulator controller (LQR)Road profile 1 has three bumps with amplitude of 0.05m, 0.025 m and 0.05 m. Road profile 2 has a bump with amplitude of 0.05 m and a hole of -0.025 m. For all the road profiles, there were 100% amplitude reduction in Wheel displacement, Wheel deflection, Suspension travel and body displacement, and 97.5% amplitude reduction in body acceleration for active damping with LQR controller as compared to the road profile and 54.0% amplitude reduction in body acceleration as compared to the passive damping system. For the two road profiles, the settling time for all the observed parameters was less than two (2) seconds. The present work gave faster settling time for mass displacement, body acceleration and wheel displacement.

scholarly journals The linear quadratic regular algorithm-based control system of the direct current motor

2019 ◽  
Vol 10 (2) ◽  
pp. 768
Author(s):  
Trong-Thang Nguyen
Keyword(s):  
Control System ◽  
Direct Current ◽  
State Space Model ◽  
Linear Quadratic Regulator ◽  
Response Speed ◽  
Dc Motor ◽  
Linear Quadratic ◽  
Lqr Controller ◽  
Mathematical Equations ◽  
Feed Forward Controller

<span>This research aims to propose an optimal controller for controlling the speed of the Direct Current (DC) motor. Based on the mathematical equations of DC Motor, the author builds the equations of the state space model and builds the linear quadratic regulator (LQR) controller to minimize the error between the set speed and the response speed of DC motor. The results of the proposed controller are compared with the traditional controllers as the PID, the feed-forward controller. The simulation results show that the quality of the control system in the case of LQR controller is much higher than the traditional controllers. The response speed always follows the set speed with the short conversion time, there isn't overshoot. The response speed is almost unaffected when the torque impact on the shaft is changed.</span>

Real-Time Control of a Rotary Inverted Pendulum using Robust LQR-based ANFIS Controller

2018 ◽  
Vol 19 (3-4) ◽  
pp. 379-389 ◽  
Author(s):  
Ishan Chawla ◽  
Ashish Singla
Keyword(s):  
Fuzzy Inference System ◽  
Inverted Pendulum ◽  
Fuzzy Inference ◽  
Sliding Mode ◽  
Linear Quadratic Regulator ◽  
Linear Quadratic ◽  
Inference System ◽  
Wide Range ◽  
Lqr Controller ◽  
Rotary Inverted Pendulum

AbstractFrom the last five decades, inverted pendulum (IP) has been considered as a benchmark problem in the control literature due to its inherit nature of instability, non-linearity and underactuation. Its applicability in wide range of practical systems, demands the need of a robust controller. It is found in the literature that wide range of controllers had been tested on this problem, out of which the most robust being sliding mode controller while the most optimal being linear quadratic regulator (LQR) controller. The former has a problem of discontinuity and chattering, while the latter lacks the property of robustness. To address the robustness issue in LQR controller, this paper proposes a novel robust LQR-based adaptive neural based fuzzy inference system controller, which is a hybrid of LQR and fuzzy inference system. The proposed controller is designed and implemented on rotary inverted pendulum. Further, to validate the robustness of proposed controller to parametric uncertainties, pendulum mass is varied. Simulation and experimental results show that as compared to LQR controller, the proposed controller is robust to variations in pendulum mass and has shown satisfactory performance.

scholarly journals Cross Regulation Reduced Optimal Multivariable Controller Design for Single Inductor DC-DC Converters

Energies ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 477 ◽  
Author(s):  
S. Augusti Lindiya ◽  
N. Subashini ◽  
K. Vijayarekha
Keyword(s):  
Pid Controller ◽  
Controller Design ◽  
Multiple Input Multiple Output ◽  
Linear Quadratic Regulator ◽  
Prototype Model ◽  
Linear Quadratic ◽  
Load Current ◽  
Practical Applications ◽  
Input Multiple Output ◽  
Single Input

Single Inductor (SI) converters with the advantage of using one inductor for any number of inputs/outputs find wide applications in portable electronic gadgets and electrical vehicles. SI converters can be used in Single Input Multiple Output (SIMO) and Multiple Input Multiple Output (MIMO) configurations but they need controllers to achieve good transient and steady state responses, to improve the stability against load and line disturbances and to reduce cross regulation. Cross regulation is the change in an output voltage due to change in the load current at another output and it is an added constraint in SI converters. In this paper, Single Input Dual Output (SIDO) and Dual Input Dual Output (DIDO) converters with applications capable of handling high load current working in Continuous Conduction Mode (CCM) of operation are taken under study. Conventional multivariable PID and optimal Linear Quadratic Regulator (LQR) controllers are developed and their performances are compared for the above configurations to meet the desired objectives. Generalized mathematical models for SIMO and MIMO are developed and a Genetic Algorithm (GA) is used to find the parameters of a multivariable PID controller and the weighting matrices of optimal LQR where the objective function includes cross regulation as a constraint. The simulated responses reveal that LQR controller performs well for both the systems over multivariable PID controller and they are validated by hardware prototype model with the help of DT9834® Data Acquisition Module (DAQ). The methodologies used here generate a fresh dimension for the case of such converters in practical applications.

Design and Analysis of Linear Quadratic Regulator for a Non-Linear Positioning System

2015 ◽  
Vol 761 ◽  
pp. 227-232 ◽  
Author(s):  
Tang Teng Fong ◽  
Zamberi Jamaludin ◽  
Ahmad Yusairi Bani Hashim ◽  
Muhamad Arfauz A. Rahman
Keyword(s):  
Physical System ◽  
Inverted Pendulum ◽  
Linear Quadratic Regulator ◽  
Upright Position ◽  
System Model ◽  
Optimum Control ◽  
Control Vector ◽  
Linear Quadratic ◽  
Non Linear ◽  
Lqr Controller

The control of rotary inverted pendulum is a case of classical robust controller design of non-linear system applications. In the control system design, a precise system model is a pre-requisite for an enhanced and optimum control performance. This paper describes the dynamic system model of an inverted pendulum system. The mathematical model was derived, linearized at the upright equilibrium points and validated using non-linear least square frequency domain identification approach based on measured frequency response function of the physical system. Besides that, a linear quadratic regulator (LQR) controller was designed as the balancing controller for the pendulum. An extensive analysis was performed on the effect of the weighting parameter Q on the static time of arm, balance time of pendulum, oscillation, as well as, response of arm and pendulum, in order to determine the optimum state-feedback control vector, K. Furthermore, the optimum control vector was successfully applied and validated on the physical system to stabilize the pendulum in its upright position. In the experimental validation, the LQR controller was able to keep the pendulum in its upright position even in the presence of external disturbance forces.

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