scholarly journals Performance Evaluation of Pole Placement and Linear Quadratic Regulator Strategies Designed for Mass-Spring-Damper System Based on Simulated Annealing and Ant Colony Optimization

2021 ◽  
Vol 27 (11) ◽  
pp. 15-31
Author(s):  
Huthaifa Al-Khazraji ◽  
Luay T. Rasheed
Keyword(s):  
Performance Evaluation ◽  
Simulated Annealing ◽  
State Feedback ◽  
Linear Quadratic Regulator ◽  
The State ◽  
Ant Colony ◽  
System Stability ◽  
Pole Placement ◽  
Linear Quadratic ◽  
Mass Spring

This paper investigates the performance evaluation of two state feedback controllers, Pole Placement (PP) and Linear Quadratic Regulator (LQR). The two controllers are designed for a Mass-Spring-Damper (MSD) system found in numerous applications to stabilize the MSD system performance and minimize the position tracking error of the system output. The state space model of the MSD system is first developed. Then, two meta-heuristic optimizations, Simulated Annealing (SA) optimization and Ant Colony (AC) optimization are utilized to optimize feedback gains matrix K of the PP and the weighting matrices Q and R of the LQR to make the MSD system reach stabilization and reduce the oscillation of the response. The Matlab software has been used for simulations and performance analysis. The results show the superiority of the state feedback based on the LQR controller in improving the system stability, reducing settling time, and reducing maximum overshoot. Furthermore, AC optimization shows significant advantages for optimizing the parameters of PP and LQR and reducing the fitness value in comparison with SA optimization

scholarly journals Mathematical Modelling and Control of a Two-Wheeled PUMA-LikeVehicle

2016 ◽  
Vol 6 (2) ◽  
pp. 11 ◽  
Author(s):  
Khaled M Goher
Keyword(s):  
Mathematical Modelling ◽  
Sliding Mode ◽  
Impact Load ◽  
State Space Model ◽  
Linear Quadratic Regulator ◽  
The Body ◽  
Pole Placement ◽  
General Motors ◽  
Linear Quadratic ◽  
And Control

<p class="1Body">This paper presents mathematical modelling and control of a two-wheeled single-seat vehicle. The design of the vehicle is inspired by the Personal Urban Mobility and Accessibility (PUMA) vehicle developed by General Motors® in collaboration with Segway®. The body of the vehicle is designed to have two main parts. The vehicle is activated using three motors; a linear motor to activate the upper part in a sliding mode and two DC motors activating the vehicle while moving forward/backward and/or manoeuvring. Two stages proportional-integral-derivative (PID) control schemes are designed and implemented on the system models. The state space model of the vehicle is derived from the linearized equations. Controller based on the Linear Quadratic Regulator (LQR) and the pole placement techniques are developed and implemented. Further investigation of the robustness of the developed LQR and the pole placement techniques is emphasized through various experiments using an applied impact load on the vehicle.</p>

An automatic system for a helicopter autopilot performance evaluation

2019 ◽  
Vol 91 (6) ◽  
pp. 880-885 ◽  
Author(s):  
Antoni Kopyt ◽  
Sebastian Topczewski ◽  
Marcin Zugaj ◽  
Przemyslaw Bibik
Keyword(s):  
Performance Evaluation ◽  
Flight Control ◽  
Automatic System ◽  
Control Algorithm ◽  
Linear Quadratic Regulator ◽  
Algorithm Analysis ◽  
Evaluation Methodology ◽  
Reference Trajectory ◽  
Linear Quadratic ◽  
Content Type

Purpose The purpose of this paper is to elaborate and develop an automatic system for automatic flight control system (AFCS) performance evaluation. Consequently, the developed AFCS algorithm is implemented and tested in a virtual environment on one of the mission task elements (MTEs) described in Aeronautical Design Standard 33 (ADS-33) performance specification. Design/methodology/approach Control algorithm is based on the Linear Quadratic Regulator (LQR) which is adopted to work as a controller in this case. Developed controller allows for automatic flight of the helicopter via desired three-dimensional trajectory by calculating iteratively deviations between desired and actual helicopter position and multiplying it by gains obtained from the LQR methodology. For the AFCS algorithm validation, the objective data analysis is done based on specified task accomplishment requirements, reference trajectory and actual flight parameters. Findings In the paper, a description of an automatic flight control algorithm for small helicopter and its evaluation methodology is presented. Necessary information about helicopter dynamic model is included. The test and algorithm analysis are performed on a slalom maneuver, on which the handling qualities are calculated. Practical implications Developed automatic flight control algorithm can be adapted and used in autopilot for a small helicopter. Methodology of evaluation of an AFCS performance can be used in different applications and cases. Originality/value In the paper, an automatic flight control algorithm for small helicopter and solution for the validation of developed AFCS algorithms are presented.

Based on the State Feedback Optimal Controler Design

2012 ◽  
Vol 241-244 ◽  
pp. 1265-1268
Author(s):  
Wei Zhang
Keyword(s):  
State Feedback ◽  
Low Cost ◽  
State Equation ◽  
The State ◽  
Linear Quadratic ◽  
Linear Quadratic Optimal Control ◽  
Control Scheme ◽  
Simulation And Experiment ◽  
Quadratic Optimal Control ◽  
Real State

The study on linear quadratic optimal control based on state feedback has been achieved fruitful results. This paper design a linear quadratic optimal controler based on the state feedback, and used it in a real state equation. The control scheme proposed has advantage of simple cotroller structure,low cost and easy to implement. Through simulation and compare with the performance target ,the Simulation and experiment show that the designed controller is effective, to the system dynamic response has good tracking performance and anti-interference ability.

scholarly journals Stabilized controller of a two wheels robot

2020 ◽  
Vol 9 (4) ◽  
pp. 1357-1363
Author(s):  
Ahmad Fahmi ◽  
Marizan Sulaiman ◽  
Indrazno Siradjuddin ◽  
I Made Wirawan ◽  
Abdul Syukor Mohamad Jaya ◽  
...  
Keyword(s):  
Control Method ◽  
Control Function ◽  
Balance Control ◽  
Linear Quadratic Regulator ◽  
Zero Point ◽  
Stability Control ◽  
Pole Placement ◽  
Linear Quadratic ◽  
Balancing Robot ◽  
The Stability

The Segway Human Transport (HT) robot, it is dynamical self-balancing robot type. The stability control is an important thing for the Segway robot. It is an indisputable fact that Segway robot is a natural instability framework robot. The case study of the Segway robot focuses on running balance control systems. The roll, pitch, and yaw balance of this robot are obtained by estimating the Kalman Filter with a combination of the pole placement and the Linear Quadratic Regulator (LQR) control method. In our system configuration, the mathematical model of the robot will be proved by Matlab Simulink by modelling of the stabilizing control system of all state variable input. Furthermore, the implementation of this system modelled to the real-time test of the Segway robot. The expected result is by substitute the known parameters from Gyro, Accelero and both rotary encoder to initial stabilize control function, the system will respond to the zero input curve. The coordinate units of displacement response and inclination response pictures are the same. As our expected, the response of the system can reach the zero point position. 

Robust eigenstructure assignment using the genetic algorithm and constrained state feedback

1997 ◽  
Vol 211 (1) ◽  
pp. 53-61 ◽  
Author(s):  
T Clarke ◽  
R Davies
Keyword(s):  
Genetic Algorithm ◽  
State Feedback ◽  
Linear Quadratic Regulator ◽  
Eigenstructure Assignment ◽  
Dynamic Feedback ◽  
Linear Quadratic ◽  
Stability Margins ◽  
Lateral Dynamics ◽  
Genetic Algorithm Approach ◽  
Handling Quality

This paper describes a robust eigenstructure assignment methodology for a constrained state feedback problem. The method, which is based upon the linear quadratic regulator and involves the minimization, via the genetic algorithm, of a multiobjective cost function, is applied to L1011 Tristar aircraft lateral dynamics. The design example generates a fixed-gain state feedback solution which shows independent phase margins of 51· in each channel, while exhibiting an eigenstructure close to that desired, lying well within specified handling quality requirements. If two states are made unavailable for feedback, the robustness properties are seriously eroded. When a dynamic feedback compensator is then used, there is a substantial recovery of the robustness. It is concluded that the genetic algorithm approach described here is easy to use and generates good multivariable stability margins.

ACTIVE VIBRATION CONTROL OF SEISMICALLY EXCITED STRUCTURES BY ATMDS: STABILITY AND PERFORMANCE ROBUSTNESS PERSPECTIVE

2010 ◽  
Vol 10 (03) ◽  
pp. 501-527 ◽  
Author(s):  
ARASH MOHTAT ◽  
AGHIL YOUSEFI-KOMA ◽  
EHSAN DEHGHAN-NIRI
Keyword(s):  
Vibration Control ◽  
Structural Damage ◽  
Fuzzy Logic Controller ◽  
Control Strategies ◽  
Linear Quadratic Regulator ◽  
Pole Placement ◽  
Structural Vibration ◽  
Linear Quadratic ◽  
And Performance ◽  
Performance Robustness

This paper demonstrates the trade-off between nominal performance and robustness in intelligent and conventional structural vibration control schemes; and, proposes a systematic treatment of stability robustness and performance robustness against uncertainty due to structural damage. The adopted control strategies include an intelligent genetic fuzzy logic controller (GFLC) and reduced-order observer-based (ROOB) controllers based on pole-placement and linear quadratic regulator (LQR) conventional schemes. These control strategies are applied to a seismically excited truss bridge structure through an active tuned mass damper (ATMD). Response of the bridge-ATMD control system to earthquake excitation records under nominal and uncertain conditions is analyzed via simulation tests. Based on these results, advantages of exploiting heuristic intelligence in seismic vibration control, as well as some complexities arising in realistic conventional control are highlighted. It has been shown that the coupled effect of spill-over (due to reduction and observation) and mismatch between the mathematical model and the actual plant (due to uncertainty and modeling errors) can destabilize the conventional closed-loop system even if each is alone tolerated. Accordingly, the GFLC proves itself to be the dominant design in terms of the compromise between performance and robustness.

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