A Modified HOSM Controller Applied to an ABS Laboratory Setup with Adaptive Parameter

The antilock braking system (ABS) is an electromechanical device whose controller is challenging to design because of its nonlinear dynamics and parameter uncertainties. In this paper, an adaptive controller is considered under the assumption that the friction coefficient is unknown. A modified high-order sliding-mode controller is designed to enhance the controller performance. The controller ensures tracking of the desired reference and identifies the unknown parameter, despite parametric variations acting on the real system. The stability proof is done using the Lyapunov approach. Some numerical and experimental tests evaluate the controller on a mechatronic system that represents a quarter-car model.

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Adaptive Fuzzy Sliding Mode Control for a Class of Nonlinear Dynamical Systems

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.71-78.4309 ◽

2011 ◽

Vol 71-78 ◽

pp. 4309-4312 ◽

Cited By ~ 4

Author(s):

Wen Da Zheng ◽

Gang Liu ◽

Jie Yang ◽

Hong Qing Hou ◽

Ming Hao Wang

Keyword(s):

Sliding Mode Control ◽

Nonlinear Dynamical Systems ◽

Sliding Mode ◽

Adaptive Controller ◽

Adaptive Sliding Mode Control ◽

Nonlinear Dynamical ◽

Mode Control ◽

Fuzzy Sliding Mode ◽

The Stability ◽

Theory Of Lyapunov Stability

This paper presents a FBFN-based (Fuzzy Basis Function Networks) adaptive sliding mode control algorithm for nonlinear dynamic systems. Firstly, we designed an perfect control law according to the nominal plant. However, there always exists discrepancy between nominal and actual mode, and the FBFN was applied to approximate the uncertainty. After that, the adaptive law was designed to update the parameters of FBFN to alleviate the approximating errors. Based on the theory of Lyapunov stability, the stability of the adaptive controller was given with a sufficient condition. Simulation example was also given to illustrate the effectiveness of the method.

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Neural Network-Based Adaptive Control of Robotic Manipulator: Application to a Three Links Cylindrical Robot

Iraqi Journal for Electrical And Electronic Engineering ◽

10.37917/ijeee.13.1.13 ◽

2017 ◽

Vol 13 (1) ◽

pp. 114-122

Author(s):

Abdul-Basset AL-Hussein

Keyword(s):

Neural Network ◽

Sliding Mode ◽

Robot Manipulator ◽

Tracking Error ◽

Adaptive Controller ◽

Robotic Manipulator ◽

Lyapunov Theorem ◽

External Disturbances ◽

The Stability ◽

Learning Law

A composite PD and sliding mode neural network (NN)-based adaptive controller, for robotic manipulator trajectory tracking, is presented in this paper. The designed neural networks are exploited to approximate the robotics dynamics nonlinearities, and compensate its effect and this will enhance the performance of the filtered error based PD and sliding mode controller. Lyapunov theorem has been used to prove the stability of the system and the tracking error boundedness. The augmented Lyapunov function is used to derive the NN weights learning law. To reduce the effect of breaching the NN learning law excitation condition due to external disturbances and measurement noise; a modified learning law is suggested based on e-modification algorithm. The controller effectiveness is demonstrated through computer simulation of cylindrical robot manipulator.

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Trajectory tracking of under-actuated quadcopter using Lyapunov-based optimum adaptive controller

Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering ◽

10.1177/09544100211010852 ◽

2021 ◽

pp. 095441002110108

Author(s):

M Navabi ◽

Ali Davoodi ◽

Hamidreza Mirzaei

Keyword(s):

Trajectory Tracking ◽

Sliding Mode ◽

Pso Algorithm ◽

Adaptive Controller ◽

Closed Loop System ◽

Parameter Uncertainties ◽

Trajectory Tracking Control ◽

Adaptive Sliding Mode Controller ◽

Backstepping Controller ◽

Flying Robot

In this article, optimum adaptive sliding mode controller (ASMC) optimized by particle swarm optimization (PSO) algorithm is designed to solve the trajectory tracking control problems of a quadcopter with model parameter uncertainties. Quadcopters have nonlinear, multi-input multi-output, coupled and under-actuated dynamics. For comparison with the designed controller, an adaptive integral backstepping controller approach is applied to compensate mass and inertia uncertainties of the flying robot. These methods guarantee stability of closed-loop system and force the states to track desired reference signals. The performance of both controllers is evaluated by numerical simulations. The obtained results demonstrate the better effectiveness of the designed PSO ASMC in stabilization of tracking particularly with parameter uncertainties.

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Adaptive Sliding Mode Control Vibrations of Structures

10.5772/intechopen.98193 ◽

2021 ◽

Author(s):

Fali Leyla ◽

Zizouni Khaled ◽

Saidi Abdelkrim ◽

Bousserhane Ismail Khalil ◽

Djermane Mohamed

Keyword(s):

Controller Design ◽

Sliding Mode ◽

Adaptive Controller ◽

Structural Vibration ◽

Adaptive Sliding Mode Control ◽

Sliding Mode Controller ◽

Active Devices ◽

The Stability ◽

Adaptation Law ◽

Low Sensitivity

The sliding mode controller is one of the interesting classical nonlinear controllers in structural vibration control. From its apparition, in the middle of the twentieth century, this controller was a subject of several studies and investigations. This controller was widely used in the control of various semi-active or active devices in the civil engineering area. Nevertheless, the sliding mode controller offered a low sensitivity to the uncertainties or the system condition variations despite the presence of the Chattering defect. However, the adaptation law is one of the frequently used solutions to overcome this phenomenon offering the possibility to adapt the controller parameters according to the system variations and keeping the stability of the whole system assured. The chapter provides a sliding mode controller design reinforced by an adaptive law to control the desired state of an excited system. The performance of the adaptive controller is proved by numerical simulation results of a three-story excited structure.

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Hardware in the Loop Simulation and Analysis of a Model of Fish Robotic System

ASME 2010 10th Biennial Conference on Engineering Systems Design and Analysis, Volume 5 ◽

10.1115/esda2010-24607 ◽

2010 ◽

Cited By ~ 1

Author(s):

S. Zeinoddini Meymand ◽

G. R. Vosoughi ◽

M. Farshchi ◽

A. Nemati

Keyword(s):

Control Method ◽

Sliding Mode ◽

Experimental Tests ◽

Adaptive Controller ◽

Robotic System ◽

Adaptive Sliding Mode Control ◽

Hardware In The Loop ◽

Fish Robot ◽

Adaptive Sliding Mode ◽

Adaptive Sliding Mode Controller

In the present study, an adaptive sliding mode control method was employed to control a fish robotic system using hardware in the loop methodology. Up to now, few researches have focused on autonomous control of fish robot in dynamic environments which may be the result of difficulties in modeling of hydrodynamic effects on fish robot. Therefore, following the introduction of the nonlinear model for the robot, elongated body theory, suggested by Lighthill, was used to analyze fish movements. Then, kinematics control to track desired trajectories was designed for under-actuated model of robot. Adaptive sliding mode controller, capable of adapting according to changes and uncertainties, was designed and implemented. Using a fabricated stand, experimental tests were performed using hardware in the loop simulation. Computer simulations accompanied by experimental results verify that the presented adaptive controller has two main advantages: first, they make a robot versatile and capable of moving in unknown environments because of system robustness under changes and uncertainties of parameters. Second, they leave out the need for expensive and time consuming experiments to recognize system model and reduce operations for final tuning of controller.

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Adaptive Super-Twisting Sliding Mode Control for Mobile Robots Based on High-Gain Observers

Journal of Control Science and Engineering ◽

10.1155/2020/4048507 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Haitao Liu ◽

Jianhao Nie ◽

Jian Sun ◽

Xuehong Tian

Keyword(s):

Control Strategy ◽

Sliding Mode ◽

Output Feedback Control ◽

Response Speed ◽

High Gain ◽

Parameter Uncertainties ◽

Trajectory Tracking Control ◽

Robust Adaptive ◽

The Stability ◽

Feedback Control Strategy

In this paper, a robust adaptive output feedback control strategy based on a sliding mode super-twisting algorithm is designed for the trajectory tracking control of a wheeled mobile robot. First, a robust adaptive law is designed to eliminate the influence of parameter uncertainties. Second, a double-power sliding mode surface is designed to improve the response speed of the robot system. Finally, a high-gain observer is employed to estimate the speed information. The stability of the closed-loop system is proved using the Lyapunov stability theorem. The effectiveness of the proposed control strategy is verified by simulation.

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Adaptive High Order Sliding Mode Controller Design for Hypersonic Vehicle with Flexible Body Dynamics

Mathematical Problems in Engineering ◽

10.1155/2013/357685 ◽

2013 ◽

Vol 2013 ◽

pp. 1-11 ◽

Cited By ~ 7

Author(s):

Bailing Tian ◽

Wenru Fan ◽

Qun Zong ◽

Jie Wang ◽

Fang Wang

Keyword(s):

Controller Design ◽

Sliding Mode ◽

Adaptive Controller ◽

Hypersonic Vehicle ◽

High Order ◽

Lyapunov Theory ◽

Sliding Mode Controller ◽

Flexible Body Dynamics ◽

Robust Adaptive ◽

The Stability

This paper describes the design of a nonlinear robust adaptive controller for a flexible hypersonic vehicle model which is nonlinear, multivariable, and unstable, and includes uncertain parameters. Firstly, a control-oriented model is derived for controller design. Then, the model analysis is conducted for this model via input-output (I/O) linearized technique. Secondly, the sliding mode manifold is designed based on the homogeneity theory. Then, the adaptive high order sliding mode controller is designed to achieve the tracking for hypersonic vehicle where the upper bounds of the uncertainties are not known in advance. Furthermore, the stability of the system is proved via the Lyapunov theory. Finally, the Monte-Carlo simulation results on the full-order nonlinear model with aerodynamic uncertainties are provided to demonstrate the effectiveness of the proposed control strategy.

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Adaptive and Robust Second Order Sliding Mode Controller Dedicated to Nonlinear Uncertain Pendulum Application System

European Journal of Engineering Research and Science ◽

10.24018/ejers.2019.4.12.1654 ◽

2019 ◽

Vol 4 (12) ◽

pp. 20-26

Author(s):

Hedi Dhouibi ◽

Jalel Ghabi ◽

Tarek Selmi

Keyword(s):

Nonlinear Systems ◽

Sliding Mode ◽

Research Work ◽

Adaptive Controller ◽

Second Order ◽

Closed Loop System ◽

Control Gain ◽

The Stability ◽

Bounded Uncertainties ◽

Second Order Sliding Mode

The research work presented within this paper deals with an innovative second-order sliding mode control (SOSMC) allocated to adaptive gain and associated with nonlinear systems subject to unknown but bounded uncertainties. The derived controller guarantees the control gain dynamical adaptation for the sake of counteracting the system’s uncertainties and to mitigate the chattering phenomenon. The Lyapunov method is also used to analyses the stability of any closed loop system (CLS) within a finite-time under bounded uncertainties assumptions. To assess how effective is the approach considered within this paper, the adaptive controller has been carefully studied on a benchmark of nonlinear systems on a damped overturned pendulum.

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An Enhanced Sliding Mode Speed Control for Induction Motor Drives

Actuators ◽

10.3390/act11010018 ◽

2022 ◽

Vol 11 (1) ◽

pp. 18

Author(s):

Fahimeh Shiravani ◽

Patxi Alkorta ◽

Jose Antonio Cortajarena ◽

Oscar Barambones

Keyword(s):

Induction Motor ◽

Control Method ◽

Sliding Mode ◽

Experimental Tests ◽

Lyapunov Stability Theory ◽

Sigmoid Function ◽

Parameter Uncertainties ◽

Integral Methods ◽

Synchronous Reference Frame ◽

Indirect Field Oriented Control

In this paper, an enhanced Integral Sliding Mode Control (ISMC) for mechanical speed of an Induction Motor (IM) is presented and experimentally validated. The design of the proposed controller has been done in the d-q synchronous reference frame and indirect Field Oriented Control (FOC). Global asymptotic speed tracking in the presence of model uncertainties and load torque variations has been guaranteed by using an enhanced ISMC surface. Moreover, this controller provides a faster speed convergence rate compared to the conventional ISMC and the Proportional Integral methods, and it eliminates the steady-state error. Furthermore, the chattering phenomenon is reduced by using a switching sigmoid function. The stability of the proposed controller under parameter uncertainties and load disturbances has been provided by using the Lyapunov stability theory. Finally, the performance of this control method is verified through numerical simulations and experimental tests, getting fast dynamics and good robustness for IM drives.

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Multiple Loop Fuzzy Neural Network Fractional Order Sliding Mode Control of Micro Gyroscope

Mathematics ◽

10.3390/math9172124 ◽

2021 ◽

Vol 9 (17) ◽

pp. 2124

Author(s):

Yunmei Fang ◽

Fang Chen ◽

Juntao Fei

Keyword(s):

Neural Network ◽

Fractional Order ◽

Fuzzy Neural Network ◽

Sliding Mode ◽

Parameter Uncertainties ◽

Control Approach ◽

Switching Law ◽

Lumped Parameter ◽

Fuzzy Neural ◽

The Stability

In this paper, an adaptive double feedback fuzzy neural fractional order sliding control approach is presented to solve the problem that lumped parameter uncertainties cannot be measured and the parameters are unknown in a micro gyroscope system. Firstly, a fractional order sliding surface is designed, and the fractional order terms can provide additional freedom and improve the control accuracy. Then, the upper bound of lumped nonlinearities is estimated online using a double feedback fuzzy neural network. Accordingly, the gain of switching law is replaced by the estimated value. Meanwhile, the parameters of the double feedback fuzzy network, including base widths, centers, output layer weights, inner gains, and outer gains, can be adjusted in real time in order to improve the stability and identification efficiency. Finally, the simulation results display the performance of the proposed approach in terms of convergence speed and track speed.

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