Network-based Adaptive Sliding Mode Control Research Articles

Neural Network-Based Adaptive Sliding Mode Control for T-S Fuzzy Fractional Order Systems

Neural Network-Based Adaptive Sliding Mode Control for T-S Fuzzy Fractional Order Systems

A composite neural network-based adaptive sliding mode control method for reluctance actuator maglev system

A composite neural network-based adaptive sliding mode control method for reluctance actuator maglev system

Adaptive neural network-based sliding mode tracking control for agricultural quadrotor with variable payload

In this paper, the path tracking controller for a class of agricultural quadrotor with variable payload under the influence of the model uncertainties and exogenous disturbances is proposed. Neural network-based adaptive control method is adopted to approximate the unknown and continuous dynamic of the quadrotor as well as to offset model uncertainties. Then a sliding mode control approach is employed to ensure that tracking errors are convergence. In order to compensate for payload changes, the total mass of the aircraft is estimated by using adaptive technique. Finally, a neural network-based adaptive sliding mode control algorithm is proposed to eliminate the effects of model uncertainties and exogenous disturbances so that path tracking of the quadrotor could be achieved. The asymptotic stability of the tracking system is confirmed by Lyapunov stability theory. The effectiveness of the proposed control strategy is verified by simulation results.

Neural network-based adaptive sliding mode control for underactuated dual overhead cranes suffering from matched and unmatched disturbances

To improve transportation capacity, dual overhead crane systems (DOCSs) are playing an increasingly important role in the transportation of large/heavy cargos and containers. Unfortunately, when trying to deal with the control problem, current methods fail to fully consider such factors as external disturbances, input dead zones, parameter uncertainties, and other unmodeled dynamics that DOCSs usually suffer from. As a result, dramatic degradation is caused in the control performance, which badly hinders the practical applications of DOCSs. Motivated by this fact, this paper designs a neural network-based adaptive sliding mode control (SMC) method for DOCS to solve the aforementioned issues, which achieves satisfactory control performance for both actuated and underactuated state variables, even in the presence of matched and mismatched disturbances. The asymptotic stability of the desired equilibrium point is proved with rigorous Lyapunov-based analysis. Finally, extensive hardware experimental results are collected to verify the efficiency and robustness of the proposed method.

Neural Network-Based Adaptive Sliding Mode Control for Gyroelastic Body

Gyroelastic body refers to a flexible structure with a cluster of angular momentum devices. The torques exerted by the angular momentum devices can be used for active vibration suppression. This paper addresses the vibration suppression of gyroelastic body in the presence of uncertainties and external disturbances. Considering that the modal variables of the gyroelastic body cannot be measured directly. Therefore, a neural network (NN) based adaptive modal observer is designed to estimate the modal information of the gyroelastic body. Based on the modal observer, a NN-based adaptive sliding mode output feedback controller (NNASMOFC) is designed. The stabilities of the NN-based modal observer and the NNASMOFC are proved using Lyapunov theory. The possible spillover problem is considered by optimal actuator placement. Simulation results demonstrate the effectiveness of the proposed controller and observer.

Neural network-based adaptive sliding mode control design for position and attitude control of a quadrotor UAV

In this paper, a novel method is suggested for the position and attitude tracking control of a quadrotor UAV in the presence of parametric uncertainties and external disturbance. The proposed method combines neural network adaptive scheme with sliding mode control, which preserves the advantages of the two methods. Firstly, dynamic model of quadrotor is divided into two fully actuated and under actuated subsystems. Secondly, sliding mode controllers are corresponding designed for each subsystem, and their coefficients in sliding manifolds are adaptively tuned by the neural network method. In each section, using Lyapunov theory, stability of closed loop system is proven.Finally, the method is examined for a square path tracking and a maximum overshoot of 7.5133% and a settling time 5.6648 s are obtained. By comparing the results obtained through different methods, it is concluded that the proposed controller provides the following main advantages: (1) good transient and steady state behaviors, (2) insensitivity to parameter variations, (3) disturbance rejection capability, and (4) remarkable stability and performance robustness. Hence, for operational purposes in which the fast and accurate response are of crucial importance, using the neural network-based adaptive sliding mode control approach is recommended.

Neural network-based sliding mode control for atmospheric-actuated spacecraft formation using switching strategy

This paper presents an adaptive neural networks-based control method for spacecraft formation with coupled translational and rotational dynamics using only aerodynamic forces. It is assumed that each spacecraft is equipped with several large flat plates. A coupled orbit-attitude dynamic model is considered based on the specific configuration of atmospheric-based actuators. For this model, a neural network-based adaptive sliding mode controller is implemented, accounting for system uncertainties and external perturbations. To avoid invalidation of the neural networks destroying stability of the system, a switching control strategy is proposed which combines an adaptive neural networks controller dominating in its active region and an adaptive sliding mode controller outside the neural active region. An optimal process is developed to determine the control commands for the plates system. The stability of the closed-loop system is proved by a Lyapunov-based method. Comparative results through numerical simulations illustrate the effectiveness of executing attitude control while maintaining the relative motion, and higher control accuracy can be achieved by using the proposed neural-based switching control scheme than using only adaptive sliding mode controller.

Neural Network-based Adaptive Sliding Mode Control Method for Tracking of a Nonholonomic Wheeled Mobile Robot with Unknown Wheel Slips, Model Uncertainties, and Unknown Bounded External Disturbances

Neural Network-based Adaptive Sliding Mode Control Method for Tracking of a Nonholonomic Wheeled Mobile Robot with Unknown Wheel Slips, Model Uncertainties, and Unknown Bounded External Disturbances

Neural network based adaptive sliding mode control of uncertain nonlinear systems

The purpose of this paper is the design of neural network-based adaptive sliding mode controller for uncertain unknown nonlinear systems. A special architecture adaptive neural network, with hyperbolic tangent activation functions, is used to emulate the equivalent and switching control terms of the classic sliding mode control (SMC). Lyapunov stability theory is used to guarantee a uniform ultimate boundedness property for the tracking error, as well as of all other signals in the closed loop. In addition to keeping the stability and robustness properties of the SMC, the neural network-based adaptive sliding mode controller exhibits perfect rejection of faults arising during the system operating. Simulation studies are used to illustrate and clarify the theoretical results.

Neural network-based adaptive sliding mode control for uncertain non-linear MIMO systems

The purpose of this paper is the design of neural network-based adaptive sliding mode controller (NASMC) for uncertain unknown MIMO non-linear systems. A special architecture adaptive neural network, with hyperbolic tangent activation functions, is used to emulate the equivalent and switching control terms of the classic sliding mode controller (SMC). The bounded motion of the system around the sliding surface and the stability of the global system, in the sense that all signals remain bounded, are guaranteed. Unlike other works, this is not a combination of neural networks and SMC approaches, but a new implementation of adaptive SMC using multiple neural networks approach, with special architecture. A two-link robot example and its simulation results are presented to illustrate the proposed approach.