scholarly journals Designing a Robust Controller Using SMC and Fuzzy Artificial Organic Networks for Brushed DC Motors

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3091 ◽  
Author(s):  
Pedro Ponce ◽  
J. Antonio Rosales ◽  
Arturo Molina ◽  
Hiram Ponce ◽  
Brian MacCleery
Keyword(s):  
Engineering Students ◽  
Sliding Mode ◽  
Dc Motor ◽  
Sliding Mode Controller ◽  
Robust Controller ◽  
Load Torque ◽  
Dc Drive ◽  
The Stability ◽  
Command Signal ◽  
Organic Networks

Electric direct-current (DC) drives based on DC motor are extremely important in the manufacturing process, so it must be crucial to increase their performance when they are working on load disturbances or the DC motor’s parameters change. Usually, several load torque suddenly appears when electric drives are operating in a speed closed-loop, so robust controllers are required to keep the speed high-performance. One of the most well-known robust strategies is the sliding mode controller (SMC), which works under discontinue operation. This controller can handle disturbances and variations in the plant’s parameters, so the controller has robust performance. Nevertheless, it has some disadvantages (chattering). Therefore, this paper proposed a fuzzy logic controller (FLC) that includes an artificial organic network for adjusting the command signal of the SMC. The proposed controller gives a smooth signal that decrements the chattering in the SMC. The stability condition that is based on Lyapunov of the DC motor is driven is evaluated; besides, the stability margins are calculated. The proposed controller is designed using co-simulation and a real testbed since co-simulation is an extremely useful tool in academia and industry allows to move from co-simulation to real implementation in short period of time. Moreover, there are several universities and industries that adopt co-simulation as the main step to design prototypes. Thus, engineering students and designers are able to achieve excellent results when they design rapid and functional prototypes. For instance, co-simulation based on Multisim leads to design directly printed circuit boards so engineering students or designers could swiftly get an experimental DC drive. The experimental results using this platform show excellent DC-drive performance when the load torque disturbances are suddenly applied to the system. As a result, the proposed controller based on fuzzy artificial organic and SMC allows for adjusting the command signal that improves the dynamic response in DC drives. The experimental response using the sliding-mode controller with fuzzy artificial organic networks is compared against an auto-tuning, Proportional-Integral-Derivative (PID), which is a conventional controller. The PID controller is the most implemented controller in several industries, so this proposal can contribute to improving manufacturing applications, such as micro-computer numerical control (CNC) machines. Moreover, the proposed robust controller achieves a superior-speed response under the whole tested scenarios. Finally, the presented design methodology based on co-simulation could be used by universities and industry for validating and implementing advanced control systems in DC drives.

Robust Hybrid Fractional Order Proportional Derivative Sliding Mode Controller for Robot Manipulator Based on Extended Grey Wolf Optimizer

Robotica ◽  
2019 ◽  
Vol 38 (4) ◽  
pp. 605-616 ◽  
Author(s):  
Hossein Komijani ◽  
Mojtaba Masoumnezhad ◽  
Morteza Mohammadi Zanjireh ◽  
Mahdi Mir
Keyword(s):  
Fractional Order ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Lyapunov Theory ◽  
Grey Wolf Optimizer ◽  
Sliding Mode Controller ◽  
Grey Wolf ◽  
Robust Controller ◽  
The Stability ◽  
Proportional Derivative Controller

SUMMARYThis paper presents a novel robust hybrid fractional order proportional derivative sliding mode controller (HFOPDSMC) for 2-degree of freedom (2-DOF) robot manipulator based on extended grey wolf optimizer (EGWO). Sliding mode controller (SMC) is remarkably robust against the uncertainties and external disturbances and shows the valuable properties of accuracy. In this paper, a new fractional order sliding surface (FOSS) is defined. Integrating the fractional order proportional derivative controller (FOPDC) and a new sliding mode controller (FOSMC), a novel robust controller based on HFOPDSMC is proposed. The bounded model uncertainties are considered in the dynamics of the robot, and then the robustness of the controller is verified. The Lyapunov theory is utilized in order to show the stability of the proposed controller. In this paper, the EGWO is developed by adding the emphasis coefficients to the typical grey wolf optimizer (GWO). The GWO and EGWO, then, are applied to optimize the proposed control parameters which result in the optimized GWO-HFOPDSMC and EGWO-HFOPDSMC, respectively. The effectivenesses of the optimized controllers (GWO-HFOPDSMC and EGWO-HFOPDSMC) are completely verified by comparing the simulation results of the optimized controllers with the typical FOSMC and HFOPDSMC.

An adaptive sliding mode controller for the lateral control of articulated long vehicles

2018 ◽  
Vol 233 (3) ◽  
pp. 487-515 ◽  
Author(s):  
Naser Esmaeili ◽  
Reza Kazemi ◽  
S Hamed Tabatabaei Oreh
Keyword(s):  
High Speed ◽  
Sliding Mode ◽  
Sliding Mode Controller ◽  
Lane Change ◽  
Lateral Velocity ◽  
Low Speed ◽  
Adaptive Sliding Mode ◽  
Adaptive Sliding Mode Controller ◽  
Articulated Vehicle ◽  
The Stability

Today, use of articulated long vehicles is surging. The advantages of using large articulated vehicles are that fewer drivers are used and fuel consumption decreases significantly. The major problem of these vehicles is inappropriate lateral performance at high speed. The articulated long vehicle discussed in this article consists of tractor and two semi-trailer units that widely used to carry goods. The main purpose of this article is to design an adaptive sliding mode controller that is resistant to changing the load of trailers and measuring the noise of the sensors. Control variables are considered as yaw rate and lateral velocity of tractor and also first and second articulation angles. These four variables are regulated by steering the axles of the articulated vehicle. In this article after developing and verifying the dynamic model, a new adaptive sliding mode controller is designed on the basis of a nonlinear model. This new adaptive sliding mode controller steers the axles of the tractor and trailers through estimation of mass and moment of inertia of the trailers to maintain the stability of the vehicle. An articulated vehicle has been exposed to a lane change maneuver based on the trailer load in three different modes (low, medium and high load) and on a dry and wet road. Simulation results demonstrate the efficiency of this controller to maintain the stability of this articulated vehicle in a low-speed steep steer and high-speed lane change maneuvers. Finally, the robustness of this controller has been shown in the presence of measurement noise of the sensors. In fact, the main innovation of this article is in the designing of an adaptive sliding mode controller, which by changing the load of the trailers, in high-speed and low-speed maneuvers and in dry and wet roads, has the best performance compared to conventional sliding mode and linear controllers.

scholarly journals Backstepping sliding mode controller improved with fuzzy logic: Application to the quadrotor helicopter

2012 ◽  
Vol 22 (3) ◽  
pp. 315-342 ◽  
Author(s):  
Samir Zeghlache ◽  
Djamel Saigaa ◽  
Kamel Kara ◽  
Abdelghani Harrag ◽  
Abderrahmen Bouguerra
Keyword(s):  
Fuzzy Logic ◽  
Control Method ◽  
Sliding Mode ◽  
Design Method ◽  
Stability And Convergence ◽  
Sliding Mode Controller ◽  
Quadrotor Helicopter ◽  
Nonlinear Control Method ◽  
The Stability ◽  
Yaw Angle

Abstract In this paper we present a new design method for the fight control of an autonomous quadrotor helicopter based on fuzzy sliding mode control using backstepping approach. Due to the underactuated property of the quadrotor helicopter, the controller can move three positions (x;y; z) of the helicopter and the yaw angle to their desired values and stabilize the pitch and roll angles. A first-order nonlinear sliding surface is obtained using the backstepping technique, on which the developed sliding mode controller is based. Mathematical development for the stability and convergence of the system is presented. The main purpose is to eliminate the chattering phenomenon. Thus we have used a fuzzy logic control to generate the hitting control signal. The performances of the nonlinear control method are evaluated by simulation and the results demonstrate the effectiveness of the proposed control strategy for the quadrotor helicopter in vertical flights.

scholarly journals Optimal New Sliding Mode Controller Combined with Modified Supertwisting Algorithm for a Perturbed Quadrotor UAV

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yassine El Houm ◽  
Ahmed Abbou ◽  
Moussa Labbadi ◽  
Mohamed Cherkaoui
Keyword(s):  
Sliding Mode ◽  
External Disturbance ◽  
Lyapunov Theory ◽  
Sliding Mode Controller ◽  
Time Varying ◽  
Sliding Surface ◽  
State Variables ◽  
Quadrotor Uav ◽  
The Stability ◽  
Quadrotor System

This paper deals with the design of a novel modified supertwisting fast nonlinear sliding mode controller (MSTFNSMC) to stabilize a quadrotor system under time-varying disturbances. The suggested control strategy is based on a modified supertwisting controller with a fast nonlinear sliding surface to improve the tracking performance. The paper suggests a simple optimization tool built-in MATLAB/Simulink to tune the proposed controller parameters. Fast convergence of state variables is established by using a nonlinear sliding surface for rotational and translational subsystems. The modified supertwisting controller is developed to suppress the effect of chattering, reject disturbances, and ensure robustness against external disturbance effect. The stability of the proposed controller (MSTFNSMC) is proved using the Lyapunov theory. The performance of the proposed MSTFNSMC approach is compared with the supertwisting sliding mode controller (STSMC) by numerical simulations to verify its effectiveness.

Neuro-Fuzzy Sliding Mode Control of Inverted Pendulum

2013 ◽  
Vol 711 ◽  
pp. 432-439
Author(s):  
Khedoudja Kherraz ◽  
Mustapha Hamerlain ◽  
Nouara Achour
Keyword(s):  
Function Method ◽  
Inverted Pendulum ◽  
Sliding Mode ◽  
Robust Controller ◽  
Fuzzy Sliding Mode Control ◽  
Neuro Fuzzy ◽  
Lyapunov Function Method ◽  
Fuzzy Sliding Mode ◽  
Simulation Results ◽  
The Stability

In this paper we develop a robust controller based on sliding mode, neural network and fuzzy logic for the control of a class of under-actuated systems. The stability of the proposed controller is proved with the Lyapunov function method. Simulation results are made on an inverted pendulum.

Analysis and Stabilization of Chaos in Permanent Magnet DC Motor Driver

2017 ◽  
Vol 27 (11) ◽  
pp. 1750173
Author(s):  
Fadhil Rahma Tahir ◽  
Khalid M. Abdul-Hassan ◽  
Mohammed Abbas Abdullah ◽  
Viet-Thanh Pham ◽  
Thang Manh Hoang ◽  
...  
Keyword(s):  
Nonlinear Dynamics ◽  
Permanent Magnet ◽  
Sliding Mode ◽  
Dynamical Behavior ◽  
Dc Motor ◽  
Drive System ◽  
Motor Drive ◽  
The Stability ◽  
Dc Motor Driver ◽  
Motor Drive System

In this paper, the nonlinear dynamics of permanent magnet (PM) DC motor drive with proportional (P) controller have been investigated. The drive system shows different dynamical behaviors; periodic, quasi-periodic, and chaotic behaviors, and those are characterized by using bifurcation diagram, phase portrait, and time series. The stability analysis of period-1 behavior is studied by using Filippov’s method, the analytic results show good agreement with simulation ones. Then, the stabilization of chaos to fixed point is carried out by using the sliding mode control (SMC). In addition, experimentally the nonlinear dynamics and the proposed stabilization method to PM DC motor drive system have been achieved by using a microcontroller. For the first time, it is noted that when the system is in chaotic dynamics, the vibration of the motor is increased approximately 400% compared with the system in periodic dynamical behavior.

scholarly journals Robust Wheel Slip for Vehicle Anti-lock Braking System with Fuzzy Sliding Mode Controller (FSMC)

2020 ◽  
Vol 10 (5) ◽  
pp. 6368-6373
Author(s):  
S. Latreche ◽  
S. Benaggoune
Keyword(s):  
Sliding Mode ◽  
Sliding Mode Controller ◽  
Closed Loop System ◽  
Wheel Slip ◽  
Ground Vehicle ◽  
Strongly Nonlinear ◽  
Braking System ◽  
Fuzzy Sliding Mode ◽  
The Stability ◽  
New Strategies

Anti-lock Braking System (ABS) is used in automobiles to prevent slipping and locking of wheels after the brakes are applied. Its control is a rather complicated problem due to its strongly nonlinear and uncertain characteristics. The aim of this paper is to investigate the wheel slip control of the ground vehicle, comprising two new strategies. The first strategy is the Sliding Mode Controller (SMC) and the second one is the Fuzzy Sliding Mode Controller (FSMC), which is a combination of fuzzy logic and sliding mode, to ensure the stability of the closed-loop system and remove the chattering phenomenon introduced by classical sliding mode control. The obtained simulation results reveal the efficiency of the proposed technique for various initial road conditions.

scholarly journals A neural network combined with sliding mode controller for the two-wheel self-balancing robot

2021 ◽  
Vol 10 (3) ◽  
pp. 592
Author(s):  
Duc-Minh Nguyen ◽  
Van-Tiem Nguyen ◽  
Trong-Thang Nguyen
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Sliding Mode ◽  
Lyapunov Theory ◽  
Sliding Mode Controller ◽  
Control Laws ◽  
Noise Compensation ◽  
Simulation Results ◽  
Balancing Robot ◽  
The Stability

This article presents the sliding control method combined with the selfadjusting neural network to compensate for noise to improve the control system's quality for the two-wheel self-balancing robot. Firstly, the dynamic equations of the two-wheel self-balancing robot built by Euler–Lagrange is the basis for offering control laws with a neural network of noise compensation. After disturbance-compensating, the sliding mode controller is applied to control quickly the two-wheel self-balancing robot reached the desired position. The stability of the proposed system is proved based on the Lyapunov theory. Finally, the simulation results will confirm the effectiveness and correctness of the control method suggested by the authors.

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