Data-Driven Backstepping Control of Underactuated Mechanical Systems

2019 ◽  
Vol 141 (9) ◽  
Author(s):  
Jingwen Huang ◽  
Tingting Zhang ◽  
Jian-Qiao Sun
Keyword(s):  
Mechanical Systems ◽  
Linear Quadratic Regulator ◽  
Backstepping Control ◽  
Data Driven ◽  
Underactuated Mechanical Systems ◽  
Linear Quadratic ◽  
Lqr Control ◽  
Whole Process ◽  
Model Free ◽  
Low Pass

This paper studies control problems of underactuated mechanical systems with model uncertainties. The control is designed with the method of backstepping. The first-order low-pass filters are used to estimate the unknown quantities and to avoid the “explosion of terms.” A novel method is also proposed to implement the control without the knowledge of the control coefficient, which makes the whole process of backstepping control data-driven. The stability of the proposed control in the Lyapunov sense is studied. It is numerically and experimentally validated, and compared with the well-known model-based linear quadratic regulator (LQR) control. The data-driven backstepping control is found to provide comparable performances to that of the LQR control with the advantage of being model-free and robust.

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

Lateral Stability Analysis for Car-Trailer Combinations

2016 ◽  
Author(s):  
Eungkil Lee ◽  
Tao Sun ◽  
Yuping He
Keyword(s):  
Stability Analysis ◽  
Linear Stability ◽  
Linear Stability Analysis ◽  
Parametric Study ◽  
Degrees Of Freedom ◽  
Linear Quadratic Regulator ◽  
Lateral Stability ◽  
Linear Quadratic ◽  
Lqr Control ◽  
Ct System

This paper presents a parametric study of linear lateral stability of a car-trailer (CT) combination in order to examine the fidelity, complexity, and applicability for control algorithm development for CT systems. Using MATLAB software, a linear yaw-roll model with 5 degrees of freedom (DOF) is developed to represent the CT combination. In the case of linear stability analysis, a parametric study was carried out using eigenvalue analysis based on a linear yaw-roll CT model with varying parameters. Built upon the linear stability analysis, an active trailer differential braking (ATDB) controller was designed for the CT system using the linear quadratic regulator (LQR) technique. The simulation study presented in this paper shows the effectiveness of the proposed LQR control design and the influence of different trailer parameters.

scholarly journals Data-Driven Model-Free Adaptive Control of Z-Source Inverters

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7438
Author(s):  
Yasin Asadi ◽  
Amirhossein Ahmadi ◽  
Sasan Mohammadi ◽  
Ali Moradi Amani ◽  
Mousa Marzband ◽  
...  
Keyword(s):  
State Feedback ◽  
Adaptive Controller ◽  
Green Energy ◽  
Data Driven ◽  
Control Action ◽  
Linear Quadratic ◽  
Model Based ◽  
Model Free ◽  
Quadratic Integral ◽  
Lyapunov Sense

The universal paradigm shift towards green energy has accelerated the development of modern algorithms and technologies, among them converters such as Z-Source Inverters (ZSI) are playing an important role. ZSIs are single-stage inverters which are capable of performing both buck and boost operations through an impedance network that enables the shoot-through state. Despite all advantages, these inverters are associated with the non-minimum phase feature imposing heavy restrictions on their closed-loop response. Moreover, uncertainties such as parameter perturbation, unmodeled dynamics, and load disturbances may degrade their performance or even lead to instability, especially when model-based controllers are applied. To tackle these issues, a data-driven model-free adaptive controller is proposed in this paper which guarantees stability and the desired performance of the inverter in the presence of uncertainties. It performs the control action in two steps: First, a model of the system is updated using the current input and output signals of the system. Based on this updated model, the control action is re-tuned to achieve the desired performance. The convergence and stability of the proposed control system are proved in the Lyapunov sense. Experiments corroborate the effectiveness and superiority of the presented method over model-based controllers including PI, state feedback, and optimal robust linear quadratic integral controllers in terms of various metrics.

scholarly journals Signature PSO: A novel inertia weight adjustment using fuzzy signature for LQR tuning

2021 ◽  
Vol 10 (1) ◽  
pp. 308-318
Author(s):  
Achmad Komarudin ◽  
Novendra Setyawan ◽  
Leonardo Kamajaya ◽  
Mas Nurul Achmadiah ◽  
Zulfatman Zulfatman
Keyword(s):  
Linear Quadratic Regulator ◽  
Absolute Error ◽  
Control Parameters ◽  
Optimum Control ◽  
Exploration And Exploitation ◽  
Linear Quadratic ◽  
Lqr Control ◽  
Inertia Weight ◽  
Adjustment Strategy ◽  
Complete Optimization

Particle swarm optimization (PSO) is an optimization algorithm that is simple and reliable to complete optimization. The balance between exploration and exploitation of PSO searching characteristics is maintained by inertia weight. Since this parameter has been introduced, there have been several different strategies to determine the inertia weight during a train of the run. This paper describes the method of adjusting the inertia weights using fuzzy signatures called signature PSO. Some parameters were used as a fuzzy signature variable to represent the particle situation in a run. The implementation to solve the tuning problem of linear quadratic regulator (LQR) control parameters is also presented in this paper. Another weight adjustment strategy is also used as a comparison in performance evaluation using an integral time absolute error (ITAE). Experimental results show that signature PSO was able to give a good approximation to the optimum control parameters of LQR in this case.

scholarly journals DYNAMICS BASED CONTROL OF A SKID STEERING MOBILE ROBOT

2016 ◽  
Vol 9 (2) ◽  
pp. 70 ◽  
Author(s):  
Osama Elshazly ◽  
Hossam Abbas ◽  
Zakarya Zyada
Keyword(s):  
Mobile Robot ◽  
Inverse Dynamics ◽  
Linear Quadratic Regulator ◽  
Nonlinear Controller ◽  
Linear Quadratic ◽  
Reduced Order ◽  
Lqr Control ◽  
Control Schemes ◽  
Skid Steering ◽  
Drive Model

In this paper, development of a reduced order, augmented dynamics-drive model that combines both the dynamics and drive subsystems of the skid steering mobile robot (SSMR) is presented. A Linear Quadratic Regulator (LQR) control algorithm with feed-forward compensation of the disturbances part included in the reduced order augmented dynamics-drive model is designed. The proposed controller has many advantages such as its simplicity in terms of design and implementation in comparison with complex nonlinear control schemes that are usually designed for this system. Moreover, the good performance is also provided by the controller for the SSMR comparable with a nonlinear controller based on the inverse dynamics which depends on the availability of an accurate model describing the system. Simulation results illustrate the effectiveness and enhancement provided by the proposed controller.

Harmonic Current Distortion Using the Linear Quadratic Regulator for a Grid-Connected Photovoltaic System

2021 ◽  
Vol 29 (1) ◽  
Author(s):  
Oscar Andrew Zongo ◽  
Anant Oonsivilai
Keyword(s):  
Control Strategies ◽  
Harmonic Distortion ◽  
Linear Quadratic Regulator ◽  
Photovoltaic System ◽  
Voltage Source ◽  
Linear Quadratic ◽  
Grid Current ◽  
Harmonic Currents ◽  
Low Pass ◽  
Low Pass Filters

This paper presents a comparison between a proportional-integral controller, low pass filters, and the linear quadratic regulator in dealing with the task of eliminating harmonic currents in the grid-connected photovoltaic system. A brief review of the existing methods applied to mitigate harmonic currents is presented. The Perturb & Observe technique was employed for maximum power point tracking. The PI control, low pass filters, and the linear quadratic regulator are discussed in detail in terms of their control strategies. The grid current was analyzed in the system with all three of the controllers applied to control the voltage source inverter of the solar photovoltaic system connected to the grid through an L filter and LCL filter and simulated in MATLAB/SIMULINK. The simulation results obtained have proven the robustness of the linear quadratic regulator over other methods. The technique lowers the grid current total harmonic distortion from 7.85% to 2.13%.

FULL-AND REDUCED- ORDER COMPENSATOR FOR INNOSAT ATTITUDE CONTROL

2015 ◽  
Vol 76 (12) ◽  
Author(s):  
Fadzilah Hashim ◽  
Mohd Yusoff Mashor ◽  
Siti Maryam Sharun
Keyword(s):  
State Space ◽  
Attitude Control ◽  
Space Form ◽  
Transfer Functions ◽  
Linear Quadratic Regulator ◽  
Linear Quadratic ◽  
Reduced Order ◽  
Lqr Control ◽  
Best Value ◽  
Control Scheme

This paper presents a study on the estimator based on Linear Quadratic Regulator (LQR) control scheme for Innovative Satellite (InnoSAT). By using LQR control scheme, the controller and the estimator has been derived for state space form in all three axes to stabilize the system’s performance. This study starts by converting the transfer functions of attitude control into state space form.  Then, the step continues by finding the best value of weighting matrices of LQR in order to obtain the best value of controller gain, K. After that, the best value of L is obtained for the estimator gain. The value of K and L is combined in forming full order compensator and in the same time the reduced order compensator is also formed. Lastly, the performance of full order compensator is compared to reduced order compensator. From the simulation, results indicate that both types of estimators have presented good stability and tracking performance. However, reduced order estimator has simpler equation and faster convergence to zero than the full order estimator. This property is very important in developing a satellite attitude control for real-time implementation.

scholarly journals System design for inverted pendulum using LQR control via IoT

2020 ◽  
Vol 11 ◽  
pp. 12
Author(s):  
Dechrit Maneetham ◽  
Petrus Sutyasadi
Keyword(s):  
Inverted Pendulum ◽  
Control Method ◽  
Linear Quadratic Regulator ◽  
Linear Quadratic ◽  
Lqr Control ◽  
Lqr Controller ◽  
Model Tuning ◽  
And Performance ◽  
Performance Results ◽  
And Control

This research proposes control method to balance and stabilize an inverted pendulum. A robust control was analyzed and adjusted to the model output with real time feedback. The feedback was obtained using state space equation of the feedback controller. A linear quadratic regulator (LQR) model tuning and control was applied to the inverted pendulum using internet of things (IoT). The system's conditions and performance could be monitored and controlled via personal computer (PC) and mobile phone. Finally, the inverted pendulum was able to be controlled using the LQR controller and the IoT communication developed will monitor to check the all conditions and performance results as well as help the inverted pendulum improved various operations of IoT control is discussed.

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>

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