Neural Sliding Mode Control Research Articles

Discrete-time sliding mode neuro-adaptive controller for SCARA robot arm

This work presents a discrete-time sliding mode neuro-adaptive control (DTSMNAC) method for robot manipulators. Due to the dynamics variations and uncertainties in the robot model, the trajectory tracking of robot manipulators has been one of the research areas for the last years. The proposed control structure is a practical design that combines a discrete-time neuro-adaptation technique with sliding mode control to compensate the dynamics variations in the robot. Using an online adaptation technique, a DTSMNAC controller is used to approximate the equivalent control in the neighborhood of the sliding surface. A sliding control is included to guarantee that the discrete-time neural sliding mode control can improve a stable closed-loop system for the trajectory tracking control of the robot with dynamics variations. The proposed technique simultaneously ensures the stability of the adaptation of the neural networks and can be obtained a suitable equivalent control when the parameters of the robot dynamics are unknown in advance. This neural adaptive system is applied to a SCARA robot manipulator and shows to be able to ensure that the output tracking error will converge to zero. Finally, experiments on a SCARA robot have been developed to show the performance of the proposed technique, including the comparison with a PID controller.

The neural sliding mode controller design of fan-plate system

This paper proposed a sliding mode angle control with neural network estimator design for a fan-plate system. The neural network estimator is based on radial basis function and it estimates the unknown lumped bounded uncertainty of parameter variations and external disturbances in real-time. The abilities of anti-disturbance and anti-chattering are better than conventional sliding mode controller and adaptive sliding mode controller. The Lyapunov stability theorem is employed to ensure the stability of the proposed controller. The convergence and signal tracking properties are better than the conventional sliding mode controller. Finally, we employed the experiment to validate the proposed method is feasible.

Dynamic Analysis and Control of a Permanent Magnet Synchronous Motor With External Perturbation

Chaotic motion and chaos control of a permanent magnet synchronous motor (PMSM) are studied in this paper. The dynamics of chaotic PMSM with load vibration perturbation is presented and its complex dynamic characteristics are analyzed by using bifurcation diagrams, Lyapunov exponents, and phase portraits. Furthermore, an adaptive neural sliding mode control is addressed to suppress chaos oscillations for the PMSM. The neural network approximation is applied in the controller to emulate of the load perturbation. Simulation results show that the proposed control scheme can eliminate the chaos and make the system achieve stable states even with the existence of unknown load vibration disturbance.

Neural Network-based Sliding Mode Control for Permanent Magnet Synchronous Motor

Neural Network-based Sliding Mode Control for Permanent Magnet Synchronous Motor

Neural Feedback Passivity of Unknown Nonlinear Systems via Sliding Mode Technique.

Passivity method is very effective to analyze large-scale nonlinear systems with strong nonlinearities. However, when most parts of the nonlinear system are unknown, the published neural passivity methods are not suitable for feedback stability. In this brief, we propose a novel sliding mode learning algorithm and sliding mode feedback passivity control. We prove that for a wide class of unknown nonlinear systems, this neural sliding mode control can passify and stabilize them. This passivity method is validated with a simulation and real experiment tests.

The Sliding Mode Variable Structure Control Method for Brushless DC Motors

The sliding mode variable structure control method for brushless DC motors with uncertain external disturbances and unknown loads is studied. A neural sliding mode control scheme is proposed for reducing chattering of sliding mode control. A global sliding mode manifold is designed in this approach, which guarantees that the system states can be on the sliding mode manifold at initial time and the system robustness is increased. A radial basis function neural network (RBFNN) is applied to learn the maximum of unknown loads and external disturbances. Based on the neural networks, the switching control parameters of sliding mode control can be adaptively adjusted with uncertain external disturbances and unknown loads. Therefore, the chattering of the sliding mode controller is reduced. Simulation results proved that this control scheme is valid.

Dynamic Neural Sliding Mode Control for Uncertain Nonlinear Systems

For uncertainty nonlinear system, a dynamic neural sliding mode (SM) controller is designed in this paper. A union upper bound of uncertainty is constructed. The union upper bound is from the uncertainty and disturbance of the system, and it is unknown. The designed dynamic neural SM controller can ensure the system asymptotic stability. Using Radial Basis Function Neural Networks (RBFNN) to learn adaptively the union upper boundary of the uncertainty and verifying its validity by theoretical analysis and simulation examples. The design scheme of the adaptive learning upper bound reduces the condition of theoretical analysis of SMC, effectively suppresses the chattering.

Adaptive neural sliding mode compensator for a class of nonlinear systems with unmodeled uncertainties

This paper addresses the problem of adaptive neural sliding mode control for a class of multi-input multi-output nonlinear system. The control strategy is an inverse nonlinear controller combined with an adaptive neural network with sliding mode control using an on-line learning algorithm. The adaptive neural network with sliding mode control acts as a compensator for a conventional inverse controller in order to improve the control performance when the system is affected by variations in its entire structure (kinematics and dynamics). The controllers are obtained by using Lyapunov's stability theory. Experimental results of a case study show that the proposed method is effective in controlling dynamic systems with unexpected large uncertainties.

Sliding Mode Neuro Adaptive Control in Trajectory Tracking for Mobile Robots

In this work a neural indirect sliding mode control method for mobile robots is proposed. Due to the nonholonomic property and restricted mobility, the trajectory tracking of this system has been one of the research topics for the last ten years. The proposed control structure combines a feedback linearization model, based on a kinematics nominal model, and a practical design that combines an indirect neural adaptation technique with sliding mode control to compensate the dynamics of the robot. Using an online adaptation scheme, a neural sliding mode controller is used to approximate the equivalent control in the neighbourhood of the sliding manifold. A sliding control is appended to ensure that the neural sliding mode control can achieve a stable closed-loop system for the trajectory-tracking control of a mobile robot with unknown nonlinear dynamics. The proposed design simultaneously guarantees the stability of the adaptation of the neural nets and obtains suitable equivalent control when the parameters of the robot model are unknown in advance. The robust adaptive scheme is applied to a mobile robot and shown to be able to guarantee that the output tracking error will converge to zero.

Design of adaptive fuzzy wavelet neural sliding mode controller for uncertain nonlinear systems

This paper proposes novel adaptive fuzzy wavelet neural sliding mode controller (AFWN-SMC) for a class of uncertain nonlinear systems. The main contribution of this paper is to design smooth sliding mode control (SMC) for a class of high-order nonlinear systems while the structure of the system is unknown and no prior knowledge about uncertainty is available. The proposed scheme composed of an Adaptive Fuzzy Wavelet Neural Controller (AFWNC) to construct equivalent control term and an Adaptive Proportional-Integral (A-PI) controller for implementing switching term to provide smooth control input. Asymptotical stability of the closed loop system is guaranteed, using the Lyapunov direct method. To show the efficiency of the proposed scheme, some numerical examples are provided. To validate the results obtained by proposed approach, some other methods are adopted from the literature and applied for comparison. Simulation results show superiority and capability of the proposed controller to improve the steady state performance and transient response specifications by using less numbers of fuzzy rules and on-line adaptive parameters in comparison to other methods. Furthermore, control effort has considerably decreased and chattering phenomenon has been completely removed.

Neural-fuzzy sliding mode control of non-linear systems

In this paper, neural-fuzzy sliding mode control is proposed for robust asymptotic stabilisation of a non-linear system. The sliding mode control has two terms as continuous mode and discontinuous mode are approximated using direct neural network control method based on neural sliding mode control and fuzzy sliding mode control method respectively. The combined control is termed as neural-fuzzy sliding mode control. The weights of the neural network are updated online to minimise the cost function. The cost function is the difference between reference value and plant output. The performance of the proposed controller is tested using an example of non-linear inverted pendulum and results are compared with conventional SMC and FSMC methods to demonstrate its superiority in terms of parameter variations and disturbance rejection.

Adaptive Neural Sliding Mode Control of Active Power Filter

A radial basis function (RBF) neural network adaptive sliding mode control system is developed for the current compensation control of three-phase active power filter (APF). The advantages of the adaptive control, neural network control, and sliding mode control are combined together to achieve the control task; that is, the harmonic current of nonlinear load can be eliminated and the quality of power system can be well improved. Sliding surface coordinate function and sliding mode controller are used as input and output of the RBF neural network, respectively. The neural network control parameters are online adjusted through gradient method and Lyapunov theory. Simulation results demonstrate that the adaptive RBF sliding mode control can compensate harmonic current effectively and has strong robustness to disturbance signals.

Robust adaptive neural sliding mode control of MEMS triaxial gyroscope with angular velocity estimation

In this paper, a robust adaptive sliding mode control strategy of micro electro-mechanical system (MEMS) triaxial gyroscope using radial basis function (RBF) neural network is presented for the system identification of MEMS gyroscope. A key property of this scheme is that the prior knowledge of the upper bound of the system uncertainties is not required. An adaptive RBF neural network controller is used to learn the unknown upper bound of model uncertainties and external disturbances. The adaptive RBF neural network is incorporated into the adaptive sliding mode control in the Lyapunov sense, and the stability of the proposed adaptive neural sliding mode control can be established. The dynamics and angular velocities of gyroscope can be identified in real time. Numerical simulations are investigated to verify the effectiveness of the proposed adaptive neural sliding mode control scheme, showing that the designed control system has better robust performance in its insensitivity to system nonlinearities; moreover, system parameters including angular velocity can be consistently estimated and tracking errors converge to zero asymptotically.

Adaptive vibration control of microelectromechanical systems triaxial gyroscope using radial basis function sliding mode technique

In this article, a novel radial basis function neural network adaptive sliding mode control is developed for the tracking control of microelectromechanical systems triaxial gyroscope. The advantages of the adaptive control, neural network control and sliding mode control are combined together to implement the control task. Switching function and sliding mode controller are utilized as the input and output of the radial basis function neural network, respectively. The proposed adaptive neural sliding mode control has on-line learning ability to deal with system nonlinearities by adjusting the control parameters updated from the Lyapunov analysis. Simulation studies are investigated to verify the effectiveness of the proposed adaptive neural sliding mode control scheme.

Nonlinear vibration semi-active control of automotive steering using magneto-rheological damper

Traffic accidents are often caused by vibration of automotive steering because the vibration can make a vehicle run like a snake. A novel semi-active vibration control strategy of automotive steering with magneto-rheological (MR) damper is proposed in this paper. An adaptive RBF neural sliding mode controller is designed for the vibration system. It is showed that an equivalent dynamic model for the vibration system is established by using Lagrange method, and then treats it as actual system partially. A feedback control law is designed to make this nominal model stable. Uncertain part of system and outside disturbance are estimated using RBF neural network, and their upper boundary is obtained automatically. By constructing reasonable switch function, state variables can arrive at origin asymptotically along the sliding mode. Strong robust character of control system is proved by stability analysis and a numerical simulation example is performed to support this control scheme.

Application of Adaptive RBF-SMC for Electro-Hydraulic Position Servo System

Electro-hydraulic servo system was hard to control with traditional control strategy and RBF-SMC (Radial Basis Function neural networks-Sliding Mode Control) controller was designed for this system. The mathematical model of the electro-hydraulic servo system was analyzed and the neural sliding mode controller was designed, the control law of sliding mode control was based on linearization feedback techniques and estimate parameters with RBF neural network. The simulation shows RBF neural networks can learning the uncertainties and disturbance, RBF-SMC has good control performance of reduces chattering and parameters estimation.

Decentralized RBFNN Type-2 Fuzzy Sliding Mode Controller for Robot Manipulator Driven by Artificial Muscles

In the few last years, investigations in neural networks, fuzzy systems and their combinations become attractive research areas for modeling and controlling of uncertain systems. In this paper, we propose a new robust controller based on the integration of a Radial Base Function Neural Network (RBFNN) and an Interval Type-2 Fuzzy Logic (IT2FLC) for robot manipulator actuated by pneumatic artificial muscles (PAM). The proposed approach was synthesized for each joint using Sliding Mode Control (SMC) and named Radial Base Function Neural Network Type-2 Fuzzy Sliding Mode Control (RBFT2FSMC). Several objectives can be accomplished using this control scheme such as: avoiding difficult modeling, attenuating the chattering effect of the SMC, reducing the rules number of the fuzzy control, guaranteeing the stability and the robustness of the system, and finally handling the uncertainties of the system. The proposed control approach is synthesized and the stability of the robot using this controller was analyzed using Lyapunov theory. In order to demonstrate the efficiency of the RBFT2FSMC compared to other control technique, simulations experiments were performed using linear model with parameters uncertainties obtained after identification stage. Results show the superiority of the proposed approach compared to RBFNN Type-1 Fuzzy SMC. Finally, an experimental study of the proposed approach was presented using 2-DOF robot.

Robust Adaptive Neural Sliding Mode Approach for Tracking Control of a MEMS Triaxial Gyroscope

In this paper, a neural network adaptive sliding mode control is proposed for an MEMS triaxial gyroscope with unknown system nonlinearities. An input-output linearization technique is incorporated into the neural adaptive tracking control to cancel the nonlinearities, and the neural network whose parameters are updated from the Lyapunov approach is used to perform the linearization control law. The sliding mode control is utilized to compensate the neural network's approximation errors. The stability of the closed-loop system can be guaranteed with the proposed adaptive neural sliding mode control. Numerical simulations are investigated to verify the effectiveness of the proposed adaptive neural sliding mode control scheme.

Model Reference Adaptive Neural Sliding Mode Control for Aero-Engine

The state parameter and work state of Aero-engine in the whole flight envelope are change greatly. So the controller should deal with parameter perturbation, disturbance and the change of external condition. In this paper the neural network sliding mode variable structure decoupling controller based on model reference adaptive for the aero-engine is designed. RBF neural network is used as the output of the sliding mode control, and sliding mode switching function is used as the neural network input. The weight of RBF network is adjusted adaptively by the error between reference model output and actual output. The results of simulation show that Model Reference Adaptive Neural Sliding Mode Controller (MRANBSMC) has a good control effect in the whole flight envelope. It eliminates the chattering phenomenon effectively and decouples well, which has a good robustness and control tracking performance in the disturbance and parameter perturbation.

Robust Adaptive Neural Sliding Mode Approach for Tracking Control of a MEMS Triaxial Gyroscope

In this paper, a neural network adaptive sliding mode control is proposed for an MEMS triaxial gyroscope with unknown system nonlinearities. An input-output linearization technique is incorporated into the neural adaptive tracking control to cancel the nonlinearities, and the neural network whose parameters are updated from the Lyapunov approach is used to perform the linearization control law. The sliding mode control is utilized to compensate the neural network's approximation errors. The stability of the closed-loop system can be guaranteed with the proposed adaptive neural sliding mode control. Numerical simulations are investigated to verify the effectiveness of the proposed adaptive neural sliding mode control scheme.