scholarly journals A Hybrid Linear Quadratic Regulator Controller for Unmanned Free-Swimming Submersible

2021 ◽  
Vol 11 (19) ◽  
pp. 9131
Author(s):  
Hassan Tariq ◽  
Muhammad Rashid ◽  
Muhammad Asfand Hafeez ◽  
Saud S. Alotaibi ◽  
Mohammed H. Sinky
Keyword(s):  
Water Waves ◽  
Hybrid Approach ◽  
Linear Quadratic Regulator ◽  
Mathematical Structure ◽  
Sensor Data ◽  
Step Response ◽  
Linear Quadratic ◽  
Free Swimming ◽  
Noise Data ◽  
Linear Quadratic Regulators

An unmanned free-swimming submersible (UFSS) is designed to perform certain tasks in water without interposing humans. The vehicle’s control is achieved by integrating mathematical (analog) and non-mathematical (embedded) controllers. The main goal of integrated controllers is to overcome the environmental disturbances and noise of the sensor data. These disturbances, as well as the noise data, are generated during steering, diving, and speed control. The amplitude of disturbances and noise varies with the depth and intensity of water waves. This article presents a robust hybrid linear quadratic regulator (HLQR) controller for UFSS. The presented controller targets the desired state of the UFSS in the presence of a disturbing environment. The hybrid approach is achieved by employing: (1) two linear quadratic regulators or controllers and (2) a mathematical structure of the Riccati equation. Consequently, the proposed HLQR controller is integrated into the UFSS system to evaluate the response in terms of settling time, rise time, overshoot, and steady-state error. Furthermore, the robustness of the HLQR is investigated by considering the feedback to step response and hydrodynamic disturbances. The implementation results reveal that the proposed controller outperforms state of the art controllers, such as proportional-integral-derivative and lead-compensator controllers.

scholarly journals Synthesis of A Digital PID/LQR Control System for Duty-Cycle Modulation Buck Converters

2020 ◽  
pp. 185-189
Author(s):  
Paul Owoundi Etouke ◽  
Jean Mbihi ◽  
Leandre Nneme Nneme
Keyword(s):  
Control System ◽  
Transfer Function ◽  
Duty Cycle ◽  
Linear Quadratic Regulator ◽  
Buck Converter ◽  
Step Response ◽  
Buck Converters ◽  
Linear Quadratic ◽  
Lqr Control ◽  
Digital Pid

<p>This research paper presents a synthesis approach of a digital optimal PID/LQR control system for DCM (duty-cycle cycle modulation) Buck converters. The step response of the DCM Buck converter is obtained under Multisim virtual simulation framework. The related data file is saved as *.SCP format, and imported into EditPad Lite7 editor, then exported as Matlab file to be processed. The transfer function of the DCM Buck converter is computed from the imported step response data. Then, using the zoh (zero order holder) discretization method with 100 ms resampling period, the z-transfer function of the DCM Buck converter is computed, and that of the analog optimal PID/LQR(linear quadratic regulator) controller is calculated using Tustin’s discretization technique. Furthermore, the step response of the related closed loop digital PID control system is simulated and compared to that of the original analog PID/LQR control system. The simulation results obtained are presented in order to show the high precision as well as the reliability of Matlab-based synthesis of digital optimal PID/LQR control systems for DCM Buck converters.</p>

Weighting Matrix Selection Method for LQR Design Based on a Multi-Objective Evolutionary Algorithm

2011 ◽  
Vol 383-390 ◽  
pp. 1047-1054 ◽  
Author(s):  
Mohammad Ali Nekoui ◽  
Hassan Heidari Jame Bozorgi
Keyword(s):  
Evolutionary Algorithm ◽  
Linear Quadratic Regulator ◽  
State Feedback Control ◽  
Linear Quadratic ◽  
Pendulum System ◽  
Multi Objective ◽  
Weighting Matrix ◽  
Inverted Pendulum System ◽  
Performance Indexes ◽  
Linear Quadratic Regulators

This paper introduces an application of Multi-Objective Evolution Algorithm (MOEA) to design Q and R weighting matrices in Linear Quadratic regulators (LQR). Considering the difficulty of designing weighting matrices for a linear quadratic regulator, a multi-objective evolutionary algorithm based approach is proposed. The LQR weighting matrices, state feedback control rate and consequently the optimal controller are obtained by means of establishing the multi-objective optimization model of LQR weighting matrices and applying MOEA to it, which makes control system meet multiple performance indexes simultaneously. Controller of double inverted pendulum system is designed using the proposed approach. Simulation results show that it has shorter adjusting time and smaller amplitude value deviating from steady-state than a Non-dominated Sorting Genetic Algorithm LQR ( NSGA- LQR )weighting matrices design approach.

Identifying the Optimal Controller Strategy for DC Motors

2017 ◽  
Vol 6 (4) ◽  
pp. 252
Author(s):  
M. R. Qader
Keyword(s):  
Transfer Function ◽  
Angular Rate ◽  
Linear Quadratic Regulator ◽  
System Stability ◽  
Step Response ◽  
Phase Lag ◽  
Linear Quadratic ◽  
Loop Control ◽  
Dc Motors ◽  
Tuning Methods

<p class="Default"><span>The aim of this study is to design a control strategy for the angular rate (speed) of a DC motor by varying the terminal voltage. This paper describes various designs for the control of direct current (DC) motors. We derive a transfer function for the system and connect it to a controller as feedback, taking the applied voltage as the system input and the angular velocity as the output. Different strategies combining proportional, integral, and derivative controllers along with phase lag compensators and lead integral compensators are investigated alongside the linear quadratic regulator. For each controller transfer function, the step response, root locus, and bode plot are analysed to ascertain the behaviour of the system, and the results are compared to identify the optimal strategy. It is found that the linear quadratic controller provides the best overall performance in terms of steady-state error, response time, and system stability. The purpose of the study that took place was to design the most appropriate controller for the steadiness of DC motors. Throughout this study, analytical means like tuning methods, loop control, and stability criteria were adopted. The reason for this was to suffice the preconditions and obligations. Furthermore, for the sake of verifying the legitimacy of the controller results, modelling by MATLAB and Simulink was practiced on every controller.</span></p>

A Study on Linear Quadratic Regulator with the Desired Low-Pass Property

2013 ◽  
Vol 133 (12) ◽  
pp. 2167-2175 ◽  
Author(s):  
Katsuhiko Fuwa ◽  
Satoshi Murayama ◽  
Tatsuo Narikiyo
Keyword(s):  
Linear Quadratic Regulator ◽  
Linear Quadratic ◽  
Low Pass

scholarly journals Neural Network Based Contact Force Control Algorithm for Walking Robots

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 287
Author(s):  
Byeongjin Kim ◽  
Soohyun Kim
Keyword(s):  
Neural Network ◽  
Contact Force ◽  
Force Control ◽  
Control Algorithm ◽  
Position Control ◽  
Linear Quadratic Regulator ◽  
Walking Robots ◽  
Test Model ◽  
Linear Quadratic ◽  
Contact Force Control

Walking algorithms using push-off improve moving efficiency and disturbance rejection performance. However, the algorithm based on classical contact force control requires an exact model or a Force/Torque sensor. This paper proposes a novel contact force control algorithm based on neural networks. The proposed model is adapted to a linear quadratic regulator for position control and balance. The results demonstrate that this neural network-based model can accurately generate force and effectively reduce errors without requiring a sensor. The effectiveness of the algorithm is assessed with the realistic test model. Compared to the Jacobian-based calculation, our algorithm significantly improves the accuracy of the force control. One step simulation was used to analyze the robustness of the algorithm. In summary, this walking control algorithm generates a push-off force with precision and enables it to reject disturbance rapidly.

scholarly journals Distributed Linear Quadratic Regulator Robust to Communication Dropouts

2020 ◽  
Vol 53 (2) ◽  
pp. 3072-3078
Author(s):  
C. Amo Alonso ◽  
D. Ho ◽  
J.M. Maestre
Keyword(s):  
Linear Quadratic Regulator ◽  
Linear Quadratic
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