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.

A novel robust finite-time tracking control of uncertain robotic manipulators with disturbances

2020 ◽  
pp. 107754632098244
Author(s):  
Hamid Razmjooei ◽  
Mohammad Hossein Shafiei ◽  
Elahe Abdi ◽  
Chenguang Yang
Keyword(s):  
Finite Time ◽  
Control Method ◽  
Sliding Mode ◽  
Robotic Manipulators ◽  
Sliding Mode Controller ◽  
Time Varying ◽  
State Variables ◽  
Terminal Sliding Mode ◽  
Control Laws ◽  
Finite Time Boundedness

In this article, an innovative technique to design a robust finite-time state feedback controller for a class of uncertain robotic manipulators is proposed. This controller aims to converge the state variables of the system to a small bound around the origin in a finite time. The main innovation of this article is transforming the model of an uncertain robotic manipulator into a new time-varying form to achieve the finite-time boundedness criteria using asymptotic stability methods. First, based on prior knowledge about the upper bound of uncertainties and disturbances, an innovative finite-time sliding mode controller is designed. Then, the innovative finite-time sliding mode controller is developed for finite-time tracking of time-varying reference signals by the outputs of the system. Finally, the efficiency of the proposed control laws is illustrated for serial robotic manipulators with any number of links through numerical simulations, and it is compared with the nonsingular terminal sliding mode control method as one of the most powerful finite-time techniques.

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.

scholarly journals CentrifugationSynchronization Control of Fractional Order Chaotic Systems Based on Memristor and Its Hardware Implementation

2021 ◽  
pp. 289-297
Author(s):  
Zhaohan zhang, Huiling Jin
Keyword(s):  
Fractional Order ◽  
Hardware Implementation ◽  
Chaotic Systems ◽  
Control Method ◽  
Sliding Mode ◽  
Sliding Mode Controller ◽  
Controlled System ◽  
Integer Order ◽  
The Stability ◽  
Dynamic Phenomena

This paper studies the synchronization control of fractional order chaotic systems based on memristor and its hardware implementation. This paper takes the complex dynamic phenomena of memristor turbidity system as the research background. Starting with the integer order memristor system, the fractional order form is derived based on the integer order turbid system, and its dynamics is deeply studied. At the same time, the turbidity phenomenon is applied to the watermark encryption algorithm, which effectively improves the confidentiality of the algorithm. Finally, in order to suppress the occurrence of turbidity, a fractional order sliding mode controller is proposed. In this paper, the sliding mode controller under the function switching control method is established, and the conditions for the parameters of the sliding mode controller are derived. Finally, the experimental results analyze the stability of the controlled system under different parameters, and give the corresponding time-domain waveform to verify the correctness of the theoretical analysis.

scholarly journals Adaptive robust control for free-floating space robot with unknown uncertainty based on neural network

2018 ◽  
Vol 15 (6) ◽  
pp. 172988141881151
Author(s):  
Zhang Wenhui ◽  
Li Hongsheng ◽  
Ye Xiaoping ◽  
Huang Jiacai ◽  
Huo Mingying
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Robust Control ◽  
Control Method ◽  
External Disturbance ◽  
Adaptive Robust Control ◽  
Lyapunov Theory ◽  
Space Robot ◽  
Neural Network Controller ◽  
The Stability

It is difficult to obtain a precise mathematical model of free-floating space robot for the uncertain factors, such as current measurement technology and external disturbance. Hence, a suitable solution would be an adaptive robust control method based on neural network is proposed for free-floating space robot. The dynamic model of free-floating space robot is established; a computed torque controller based on exact model is designed, and the controller can guarantee the stability of the system. However, in practice, the mathematical model of the system cannot be accurately obtained. Therefore, a neural network controller is proposed to approximate the unknown model in the system, so that the controller avoids dependence on mathematical models. The adaptive learning laws of weights are designed to realize online real-time adjustment. The adaptive robust controller is designed to suppress the external disturbance and compensate the approximation error and improve the robustness and control precision of the system. The stability of closed-loop system is proved based on Lyapunov theory. Simulations tests verify the effectiveness of the proposed control method and are of great significance to free-floating space robot.

Sliding Mode Control with Adaptive Approaching Law for Bioreactor

2011 ◽  
Vol 378-379 ◽  
pp. 521-524
Author(s):  
Li Ping Fan ◽  
Ying Song ◽  
Jun Zhang
Keyword(s):  
Sliding Mode Control ◽  
Control Method ◽  
Sliding Mode ◽  
Sliding Mode Controller ◽  
Control Performance ◽  
Reaching Law ◽  
Mode Control ◽  
Good Movement ◽  
Bioreactor System ◽  
Simulation Results

Bioprocesses have high nonlinearity and parameter uncertainty. In view of these specific natures of the bioreactor, system identification method was firstly used to linearize the nonlinear system and simplify the model of the biological reactor; then a new sliding mode controller with adaptive reaching law is designed for the reactor. The control method can not only analysis the sliding mode movement near or along the switching surface, but also design the dynamic process in trending segments of the system effectively, thus ensure good movement quality in the entire state space. Simulation results prove that the sliding mode control with adaptive reaching law can improve the control performance with negligible chattering and enhanced robustness.

scholarly journals Fault-Tolerant Aircraft Control Based on Self-Constructing Fuzzy Neural Network for Quadcopter

2021 ◽  
Vol 15 (1) ◽  
pp. 109-122
Author(s):  
Dejie Li ◽  
◽  
Pu Yang ◽  
Zhangxi Liu ◽  
Zixin Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Fault Tolerant ◽  
Sliding Mode ◽  
Failure Model ◽  
Aircraft Control ◽  
Actuator Failure ◽  
Fuzzy Neural ◽  
The Stability

This paper proposes a fault-tolerant aircraft control method based on a self-constructed fuzzy neural network for quadcopters with multiple actuator faults. We first introduce the actuator failure model and the model uncertainty. Subsequently, we establish a framework for a self-constructed fuzzy neural network observer with an adaptive rate to obtain the estimated value of the nonlinear term of the module uncertainty. We also design a multivariable sliding mode fault-tolerant controller to ensure the stability of the aircraft under this fault condition. Finally, we conduct experiments using the Pixhawk 4 flight controller installed on the QBall-X4 UAV experimental platform, such that the use of the flight controller’s fault coprocessor and redundant sensor design reduces the crash that occurs during the debugging of the control algorithm. Compared to the existing intelligent fault-tolerant control technology, our proposed method employs fewer fuzzy rules, and the number of these rules can be adaptively adjusted when the system model changes. In the experimental test, the aircraft was still able to fly stably under multi-actuator failure and interference conditions, thereby proving the stability of the proposed controller.

scholarly journals Stabilization of A Single Chamber Single Population Microbial Fuel Cell by Using of a Novel Nonlinear Adaptive Sliding Mode Control

2021 ◽  
Vol 24 (1) ◽  
pp. 14-20
Author(s):  
Xiuwei Fu ◽  
Li Fu ◽  
Hashem Imani Marrani
Keyword(s):  
Fuel Cell ◽  
Microbial Fuel Cell ◽  
Control Method ◽  
Sliding Mode ◽  
Adaptive Sliding Mode Control ◽  
Single Population ◽  
Simulation Results ◽  
The Stability ◽  
Classical Control ◽  
Combined Technique

The microbial fuel cell is one of the most important tools in the supply of renewable energy and its controller plays an important role in improving the performance and stability of its output. Using the advantages of adaptive and sliding mode methods, this paper presents a combined technique to ensure the stability and output voltage stabilization of the fuel cell in the presence of parametric uncertainties and nonlinear terms. The proposed control method is compared with classical control approaches and the simulation results confirm its efficiency.

Trajectory-Tracking-Based Adaptive Neural Network Sliding Mode Controller for Robot Manipulators

2020 ◽  
Vol 20 (3) ◽  
Author(s):  
Bin Ren ◽  
Yao Wang ◽  
Jiayu Chen
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Lyapunov Theory ◽  
Robot Dynamics ◽  
Sliding Mode Controller ◽  
Adaptive Neural Network ◽  
Proposed Model ◽  
The Impact

Abstract Unpredictable disturbances and chattering are the major challenges of the robot manipulator control. In recent years, trajectory-tracking-based controllers have been recognized by many researchers as the most promising method to understand robot dynamics with uncertainties and improve robot control. However, reliable trajectory-tracking-based controllers require high model precision and complexity. To develop an agile and straightforward method to mitigate the impact caused by uncertain disturbance and chattering, this study proposed an adaptive neural network sliding mode controller based on the super-twisting algorithm. The proposed model not only can minimize the tracking error but also improve the system robustness with a simpler structure. Moreover, the proposed controller has the following two distinctive features: (1) the weights of the radial basis function (RBF network) are designed to be adjusted in real-time and (2) the prior knowledge of the actual robot system is not required. The analytical model of the proposed controller was proved to be stable and ensured by the Lyapunov theory. To validate the proposed model, this study also conducted a comparative simulation on a two-link robot manipulator system with the conventional sliding mode controller and the model-based controller. The results suggest the proposed model improved the control accuracy and had fewer chattering.

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